Sf Flex Estimator

Name: Sf Flex Estimator
Author: jaganpro

jaganpro/claude-code-sfskills

1 installs
423 repo stars
Updated April 27, 2026
jaganpro/claude-code-sfskills

Estimates Salesforce Flex Credit costs for Agentforce prompts and actions plus Data Cloud usage meters, producing public-price scenario projections and cost-optimization tips.

About

Produces public list-price Flex Credit estimates for Agentforce and Data Cloud workloads across low, medium, and high scenarios. A Salesforce architect uses it for budget sizing and cost tradeoff analysis before build or rollout.

Models prompt tiers, action types, and Data Cloud meters into scenarios
Planning and estimation only, not implementation

Sf Flex Estimator by the numbers

1 all-time installs (skills.sh)
Ranked #909 of 1,106 Finance & Trading skills by installs in the Skillselion catalog
Data as of Aug 2, 2026 (Skillselion catalog sync)

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Installs	1
repo stars	★ 423
Last updated	April 27, 2026
Repository	jaganpro/claude-code-sfskills ↗

What it does

Estimates Salesforce Flex Credit costs for Agentforce prompts and actions plus Data Cloud usage meters, producing public-price scenario projections and cost-optimization tips.

Files

SKILL.mdMarkdownGitHub ↗

sf-flex-estimator: Agentforce & Data Cloud Flex Credit Estimation

Use this skill when the user needs a public-price estimate for:

Agentforce prompt + action consumption
Data Cloud monthly usage meters
Flex Credit scenario planning
cost optimization recommendations before build or rollout

This skill is for planning and estimation, not implementation.

---

When This Skill Owns the Task

Use sf-flex-estimator when the user is asking questions like:

"What will this Agentforce agent cost per month?"
"Estimate Flex Credits for 5 prompts, 8 actions, and Data Cloud grounding"
"Compare low / medium / high usage scenarios"
"How much does Private Connect add?"
"What Flex Credit savings do we get if we reduce streaming or action count?"

Delegate elsewhere when the user is:

building Builder metadata, Prompt Builder templates, or action wiring → sf-ai-agentforce
authoring or fixing .agent files → sf-ai-agentscript
implementing Data Cloud connections, streams, DMOs, segments, or activations → sf-datacloud and the phase-specific sf-datacloud-* skills
creating test data or operational data imports → sf-data
deploying metadata or runtime assets → sf-deploy

---

Required Context to Gather First

Ask for or infer:

agent prompt count by tier: starter, basic, standard, advanced
action count by type: standard, custom, voice, sandbox
whether token overages are expected for prompts or actions
monthly Data Cloud meter volumes, if Data Cloud is in scope
whether Private Connect is required
whether the estimate should model a pilot, small production, enterprise, or multiple scenarios
whether the user wants public list-price guidance or is trying to reconcile contract-specific commercial numbers

If the user does not know exact monthly volumes, start with a baseline template and generate multiple scenarios.

---

Core Pricing Model

Agentforce

Agentforce billing is linear — no volume tiers.

Component	FC per invocation
Starter prompt	2
Basic prompt	2
Standard prompt	4
Advanced prompt	16
Standard / custom action	20
Voice action	30
Sandbox action	16

Data Cloud

Data Cloud uses monthly cumulative tiering.

Tier	Monthly FC range	Multiplier
Tier 1	0 - 300K	1.0x
Tier 2	300K - 1.5M	0.8x
Tier 3	1.5M - 12.5M	0.4x
Tier 4	12.5M+	0.2x

Other rules

Flex Credits are priced at $0.004 per FC in this skill.
Private Connect adds 20% of Data Cloud spend after tiering.
Agentforce and Data Cloud are estimated separately, then combined.
Estimates in this skill use publicly documented list pricing only.

For the full meter table and examples, read:

references/agentforce-pricing.md
references/data-cloud-pricing.md

---

Recommended Workflow

1. Baseline the structure

Model the agent and Data Cloud footprint first.

Useful starting templates:

assets/templates/basic-agent-template.json
assets/templates/hybrid-agent-template.json
assets/templates/data-cloud-template.json

2. Calculate the per-invocation cost

For Agentforce, estimate:

per-invocation FC = prompt FC + action FC + token overage FC

3. Calculate Data Cloud base FC

Map each monthly meter volume to the current public rate card, then apply cumulative tiering.

4. Generate scenarios

Use the standard scenario set unless the user provides a better one:

Low: 1K invocations / month
Medium: 10K / month
High: 100K / month
Enterprise: 500K / month

5. Validate assumptions and recommend optimizations

Check for:

too many prompts or actions
unnecessary streaming usage
likely token overages
missing Private Connect handling
unrealistic volume assumptions

---

Scripts and Templates

Calculator

assets/calculators/flex_calculator.py
assets/calculators/tier_multiplier.py

Validation helper

hooks/scripts/validate_estimate.py

This validator is a manual helper. It is intentionally not wired into the shared auto-validation dispatcher because generic .json or .md file patterns would create too much noise.

Example commands

# Per-invocation estimate for a template
python3 assets/calculators/flex_calculator.py \
  --mode structure \
  --agent-def assets/templates/basic-agent-template.json

# Scenario estimate for an Agentforce + Data Cloud design
python3 assets/calculators/flex_calculator.py \
  --mode scenarios \
  --agent-def assets/templates/hybrid-agent-template.json

# Tiering only
python3 assets/calculators/tier_multiplier.py \
  --base-fc 5000000 \
  --pretty

# Validate an estimate input document
python3 hooks/scripts/validate_estimate.py \
  --input assets/templates/hybrid-agent-template.json \
  --verbose

---

High-Signal Estimation Rules

Prefer standard prompts for most production reasoning workloads.
Use basic prompts only for simple routing/classification.
Action count often dominates cost faster than prompt count.
Data Cloud streaming is materially more expensive than prep/query/segment meters.
Tiering matters only for Data Cloud, not Agentforce.
Private Connect applies only to Data Cloud spend in this model.
If the user has contract-specific pricing, treat this skill as a public baseline and note that commercial terms may differ.

---

Output Format

When the estimate is complete, present: 1. workload summary 2. per-invocation Agentforce cost 3. monthly scenario table 4. Data Cloud tiering impact 5. top optimization recommendations 6. confidence / validation notes

Suggested shape:

Flex Credit estimate: <name>
Agentforce per invocation: <fc> FC ($<cost>)
Data Cloud monthly base: <fc> FC
Scenarios: <low / medium / high / enterprise>
Optimization priorities: <1-3 bullets>
Confidence: <high / medium / low>

---

Cross-Skill Integration

Need	Delegate to	Why
build the actual agent metadata	sf-ai-agentforce	implementation of Builder assets
build a deterministic `.agent` bundle	sf-ai-agentscript	authoring and validation of Agent Script
implement Data Cloud pipeline assets	sf-datacloud and `sf-datacloud-*`	live Data Cloud setup
package or deploy the solution	sf-deploy	deployment workflow
generate supporting test or sample data	sf-data	data preparation

A common chain is:

sf-ai-agentforce / sf-ai-agentscript / sf-datacloud-* → sf-flex-estimator → sf-deploy

---

Reference Map

Start here

README.md
references/calculation-methodology.md
references/common-use-cases.md
references/edge-cases.md

Pricing references

references/agentforce-pricing.md
references/data-cloud-pricing.md

Validation and scoring

references/scoring-rubric.md
hooks/scripts/validate_estimate.py

#!/usr/bin/env python3
"""Flex Credit calculator for Agentforce and Data Cloud."""

from __future__ import annotations

import argparse
import json
from dataclasses import asdict, dataclass, field
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

FC_COST = 0.004
PRIVATE_CONNECT_MULTIPLIER = 0.20

PROMPT_RATES: Dict[str, float] = {
    "starter": 2,
    "basic": 2,
    "standard": 4,
    "advanced": 16,
}

ACTION_RATES: Dict[str, float] = {
    "standard": 20,
    "custom": 20,
    "voice": 30,
    "sandbox": 16,
}

DC_RATES: Dict[str, float] = {
    "data_360_prep": 40,
    "data_360_unification": 75_000,
    "data_360_segmentation": 50,
    "data_360_activation": 60,
    "data_360_zero_copy_sharing": 60,
    "data_360_queries": 3,
    "data_360_unstructured_processing": 150,
    "data_360_intelligent_processing": 600,
    "data_360_streaming_pipeline": 3_500,
    "data_360_real_time_pipeline": 250_000,
    "data_360_code_extension": 40,
}

DC_ALIASES: Dict[str, str] = {
    "batch_internal": "data_360_prep",
    "batch_external": "data_360_prep",
    "prep": "data_360_prep",
    "profile_unification": "data_360_unification",
    "unification": "data_360_unification",
    "segmentation": "data_360_segmentation",
    "activation_batch": "data_360_activation",
    "activation_streaming": "data_360_activation",
    "queries": "data_360_queries",
    "unstructured": "data_360_unstructured_processing",
    "intelligent_processing": "data_360_intelligent_processing",
    "streaming": "data_360_streaming_pipeline",
    "real_time_pipeline": "data_360_real_time_pipeline",
    "zero_copy_sharing": "data_360_zero_copy_sharing",
    "code_extension": "data_360_code_extension",
}

TIER_THRESHOLDS: List[Tuple[float, float, float, str]] = [
    (0, 300_000, 1.0, "Tier 1"),
    (300_000, 1_500_000, 0.8, "Tier 2"),
    (1_500_000, 12_500_000, 0.4, "Tier 3"),
    (12_500_000, float("inf"), 0.2, "Tier 4"),
]


@dataclass
class AgentStructure:
    prompts: Dict[str, int] = field(default_factory=dict)
    actions: Dict[str, int] = field(default_factory=dict)
    token_overages: Dict[str, Any] = field(default_factory=lambda: {"prompts": 0, "actions": 0})

    def normalized(self) -> "AgentStructure":
        prompts = {k.lower(): int(v) for k, v in self.prompts.items() if int(v) != 0}
        actions = {k.lower(): int(v) for k, v in self.actions.items() if int(v) != 0}
        token_overages = self.token_overages or {"prompts": 0, "actions": 0}
        return AgentStructure(prompts=prompts, actions=actions, token_overages=token_overages)


@dataclass
class DataCloudOperations:
    operations: Dict[str, float] = field(default_factory=dict)
    private_connect: bool = False

    def normalized(self) -> "DataCloudOperations":
        normalized_ops: Dict[str, float] = {}
        for meter, volume in self.operations.items():
            canonical = normalize_meter_name(meter)
            normalized_ops[canonical] = normalized_ops.get(canonical, 0.0) + float(volume)
        return DataCloudOperations(operations=normalized_ops, private_connect=bool(self.private_connect))


@dataclass
class ScenarioVolumes:
    name: str
    agent_invocations: int
    dc_operations: Optional[Dict[str, float]] = None


@dataclass
class CostBreakdown:
    scenario_name: str
    agent_invocations: int
    af_prompt_fc: float
    af_action_fc: float
    af_token_overage_fc: float
    af_total_fc: float
    dc_base_fc: float
    dc_tiered_fc: float
    dc_discount_percent: float
    dc_private_connect_fc: float
    dc_total_fc: float
    total_monthly_fc: float
    total_monthly_cost: float
    total_annual_cost: float


def normalize_meter_name(meter: str) -> str:
    key = meter.strip().lower()
    return DC_ALIASES.get(key, key)


def empty_agent_structure() -> AgentStructure:
    return AgentStructure(
        prompts={"basic": 0, "standard": 0, "advanced": 0},
        actions={"standard": 0, "voice": 0, "sandbox": 0},
        token_overages={"prompts": 0, "actions": 0},
    )


def load_json(path: str | Path) -> Dict[str, Any]:
    with open(path, "r", encoding="utf-8") as handle:
        return json.load(handle)


def extract_agent_structure(data: Dict[str, Any]) -> AgentStructure:
    if not data:
        return empty_agent_structure()

    if "prompts" in data or "actions" in data:
        return AgentStructure(**data).normalized()

    nested = data.get("agent_structure")
    if isinstance(nested, dict):
        return AgentStructure(**nested).normalized()

    return empty_agent_structure()


def extract_dc_operations(data: Dict[str, Any]) -> Optional[DataCloudOperations]:
    if not data:
        return None

    if "operations" in data:
        return DataCloudOperations(**data).normalized()

    nested = data.get("data_cloud_operations")
    if isinstance(nested, dict):
        return DataCloudOperations(**nested).normalized()

    return None


def _weighted_extra_charge(base_breakdown: Dict[str, float], count: Any, total_components: int) -> float:
    if not count:
        return 0.0

    if isinstance(count, dict):
        raise TypeError("Dict-based overage handling is not supported by this helper")

    if total_components <= 0:
        return 0.0

    average_charge = base_breakdown["total_fc"] / total_components
    return float(count) * average_charge


def _explicit_prompt_overage_fc(prompt_overages: Dict[str, Any]) -> float:
    total = 0.0
    for tier, count in prompt_overages.items():
        total += PROMPT_RATES[tier.lower()] * float(count)
    return total


def _explicit_action_overage_fc(action_overages: Dict[str, Any]) -> float:
    total = 0.0
    for action_type, count in action_overages.items():
        total += ACTION_RATES[action_type.lower()] * float(count)
    return total


def calculate_per_invocation_cost(agent: AgentStructure) -> Tuple[float, Dict[str, float]]:
    agent = agent.normalized()

    prompt_fc = sum(PROMPT_RATES[tier] * count for tier, count in agent.prompts.items())
    action_fc = sum(ACTION_RATES[action_type] * count for action_type, count in agent.actions.items())

    prompt_count = sum(agent.prompts.values())
    action_count = sum(agent.actions.values())

    overage_fc = 0.0
    prompt_overages = (agent.token_overages or {}).get("prompts", 0)
    action_overages = (agent.token_overages or {}).get("actions", 0)

    if isinstance(prompt_overages, dict):
        overage_fc += _explicit_prompt_overage_fc(prompt_overages)
    else:
        overage_fc += _weighted_extra_charge(
            {"total_fc": prompt_fc}, prompt_overages, prompt_count
        )

    if isinstance(action_overages, dict):
        overage_fc += _explicit_action_overage_fc(action_overages)
    else:
        overage_fc += _weighted_extra_charge(
            {"total_fc": action_fc}, action_overages, action_count
        )

    total_fc = prompt_fc + action_fc + overage_fc

    breakdown = {
        "prompt_fc": round(prompt_fc, 2),
        "action_fc": round(action_fc, 2),
        "token_overage_fc": round(overage_fc, 2),
        "total_fc": round(total_fc, 2),
        "cost": round(total_fc * FC_COST, 4),
    }
    return round(total_fc, 2), breakdown


def calculate_dc_base_cost(dc_ops: DataCloudOperations) -> Tuple[float, Dict[str, Dict[str, float]]]:
    if not dc_ops:
        return 0.0, {}

    dc_ops = dc_ops.normalized()
    breakdown: Dict[str, Dict[str, float]] = {}
    total_fc = 0.0

    for meter, volume in dc_ops.operations.items():
        if meter not in DC_RATES:
            continue

        fc = float(volume) * DC_RATES[meter]
        breakdown[meter] = {
            "volume": round(float(volume), 4),
            "rate": DC_RATES[meter],
            "fc": round(fc, 2),
        }
        total_fc += fc

    return round(total_fc, 2), breakdown


def apply_tiered_multiplier(base_fc: float) -> Tuple[float, float, List[Dict[str, float]]]:
    if base_fc <= 0:
        return 0.0, 0.0, []

    remaining = float(base_fc)
    tiered_fc = 0.0
    tier_breakdown: List[Dict[str, float]] = []

    for start, end, multiplier, name in TIER_THRESHOLDS:
        if remaining <= 0:
            break

        tier_capacity = end - start
        fc_in_tier = min(remaining, tier_capacity)
        tier_value = fc_in_tier * multiplier
        tiered_fc += tier_value

        tier_breakdown.append(
            {
                "tier": name,
                "range_start": start,
                "range_end": end,
                "multiplier": multiplier,
                "base_fc": round(fc_in_tier, 2),
                "tiered_fc": round(tier_value, 2),
            }
        )
        remaining -= fc_in_tier

    discount_percent = ((base_fc - tiered_fc) / base_fc * 100) if base_fc > 0 else 0.0
    return round(tiered_fc, 2), round(discount_percent, 2), tier_breakdown


def calculate_scenario_cost(
    agent: AgentStructure,
    dc_ops: Optional[DataCloudOperations],
    scenario: ScenarioVolumes,
) -> CostBreakdown:
    per_invocation_fc, af_breakdown = calculate_per_invocation_cost(agent)
    af_total_fc = per_invocation_fc * scenario.agent_invocations

    dc_base_fc = 0.0
    dc_tiered_fc = 0.0
    dc_discount_percent = 0.0
    dc_private_connect_fc = 0.0

    if dc_ops and dc_ops.operations:
        effective_dc_ops = dc_ops
        if scenario.dc_operations is not None:
            effective_dc_ops = DataCloudOperations(
                operations=scenario.dc_operations,
                private_connect=dc_ops.private_connect,
            ).normalized()

        dc_base_fc, _ = calculate_dc_base_cost(effective_dc_ops)
        dc_tiered_fc, dc_discount_percent, _ = apply_tiered_multiplier(dc_base_fc)

        if effective_dc_ops.private_connect:
            dc_private_connect_fc = round(dc_tiered_fc * PRIVATE_CONNECT_MULTIPLIER, 2)

    dc_total_fc = round(dc_tiered_fc + dc_private_connect_fc, 2)
    total_monthly_fc = round(af_total_fc + dc_total_fc, 2)
    total_monthly_cost = round(total_monthly_fc * FC_COST, 2)
    total_annual_cost = round(total_monthly_cost * 12, 2)

    return CostBreakdown(
        scenario_name=scenario.name,
        agent_invocations=scenario.agent_invocations,
        af_prompt_fc=round(af_breakdown["prompt_fc"] * scenario.agent_invocations, 2),
        af_action_fc=round(af_breakdown["action_fc"] * scenario.agent_invocations, 2),
        af_token_overage_fc=round(af_breakdown["token_overage_fc"] * scenario.agent_invocations, 2),
        af_total_fc=round(af_total_fc, 2),
        dc_base_fc=round(dc_base_fc, 2),
        dc_tiered_fc=round(dc_tiered_fc, 2),
        dc_discount_percent=round(dc_discount_percent, 2),
        dc_private_connect_fc=round(dc_private_connect_fc, 2),
        dc_total_fc=round(dc_total_fc, 2),
        total_monthly_fc=round(total_monthly_fc, 2),
        total_monthly_cost=round(total_monthly_cost, 2),
        total_annual_cost=round(total_annual_cost, 2),
    )


def generate_standard_scenarios(
    agent: AgentStructure,
    dc_ops: Optional[DataCloudOperations] = None,
    scale_dc_volumes: bool = True,
) -> List[CostBreakdown]:
    scenarios = [
        ScenarioVolumes("Low (1K/month)", 1_000),
        ScenarioVolumes("Medium (10K/month)", 10_000),
        ScenarioVolumes("High (100K/month)", 100_000),
        ScenarioVolumes("Enterprise (500K/month)", 500_000),
    ]

    if dc_ops and dc_ops.operations and scale_dc_volumes:
        baseline_invocations = 1_000
        for scenario in scenarios:
            factor = scenario.agent_invocations / baseline_invocations
            scenario.dc_operations = {
                meter: round(volume * factor, 6)
                for meter, volume in dc_ops.normalized().operations.items()
            }

    return [calculate_scenario_cost(agent, dc_ops, scenario) for scenario in scenarios]


def _resolve_agent_and_dc(
    agent_path: Optional[str], dc_path: Optional[str]
) -> Tuple[AgentStructure, Optional[DataCloudOperations], Dict[str, Any], Dict[str, Any]]:
    agent_doc = load_json(agent_path) if agent_path else {}
    dc_doc = load_json(dc_path) if dc_path else agent_doc

    agent = extract_agent_structure(agent_doc)
    dc_ops = extract_dc_operations(dc_doc)
    if dc_ops is None:
        dc_ops = extract_dc_operations(agent_doc)

    return agent, dc_ops, agent_doc, dc_doc


def main() -> None:
    parser = argparse.ArgumentParser(description="Calculate Flex Credit estimates")
    parser.add_argument("--mode", required=True, choices=["structure", "scenarios", "tier-only"])
    parser.add_argument("--agent-def", help="Path to an agent or combined estimate JSON document")
    parser.add_argument("--dc-ops", help="Path to a Data Cloud or combined estimate JSON document")
    parser.add_argument("--base-fc", type=float, help="Base Data Cloud FC for tier-only mode")
    parser.add_argument("--output", help="Optional output JSON path")

    args = parser.parse_args()

    if args.mode == "tier-only":
        if args.base_fc is None:
            raise SystemExit("--base-fc is required for tier-only mode")
        tiered_fc, discount_percent, tier_breakdown = apply_tiered_multiplier(args.base_fc)
        result: Dict[str, Any] = {
            "base_fc": round(args.base_fc, 2),
            "tiered_fc": tiered_fc,
            "discount_percent": discount_percent,
            "tier_breakdown": tier_breakdown,
        }
    else:
        if not args.agent_def:
            raise SystemExit("--agent-def is required for structure and scenarios mode")

        agent, dc_ops, _, _ = _resolve_agent_and_dc(args.agent_def, args.dc_ops)

        if args.mode == "structure":
            per_invocation_fc, breakdown = calculate_per_invocation_cost(agent)
            result = {
                "per_invocation_fc": per_invocation_fc,
                "per_invocation_cost": round(per_invocation_fc * FC_COST, 4),
                "breakdown": breakdown,
            }
        else:
            scenarios = generate_standard_scenarios(agent, dc_ops)
            per_invocation_fc, breakdown = calculate_per_invocation_cost(agent)
            result = {
                "per_invocation_fc": per_invocation_fc,
                "per_invocation_cost": round(per_invocation_fc * FC_COST, 4),
                "breakdown": breakdown,
                "scenarios": [asdict(s) for s in scenarios],
            }

    if args.output:
        Path(args.output).write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
    else:
        print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()

#!/usr/bin/env python3
"""Standalone Data Cloud tier multiplier calculator."""

from __future__ import annotations

import argparse
import json
from pathlib import Path
from typing import Dict, List, Tuple

TIERS: List[Tuple[float, float, float, str]] = [
    (0, 300_000, 1.0, "Tier 1"),
    (300_000, 1_500_000, 0.8, "Tier 2"),
    (1_500_000, 12_500_000, 0.4, "Tier 3"),
    (12_500_000, float("inf"), 0.2, "Tier 4"),
]


def calculate_tiered_fc(base_fc: float, verbose: bool = False) -> Dict[str, object]:
    if base_fc <= 0:
        return {
            "base_fc": round(base_fc, 2),
            "tiered_fc": 0.0,
            "discount_percent": 0.0,
            "savings_fc": 0.0,
            "savings_dollars": 0.0,
            "tier_breakdown": [],
        }

    remaining = float(base_fc)
    tiered_fc = 0.0
    tier_breakdown = []

    for start, end, multiplier, label in TIERS:
        if remaining <= 0:
            break

        tier_capacity = end - start
        fc_in_tier = min(remaining, tier_capacity)
        fc_after_multiplier = fc_in_tier * multiplier
        savings = fc_in_tier - fc_after_multiplier
        tiered_fc += fc_after_multiplier

        if verbose or fc_in_tier > 0:
            discount_pct = round((1 - multiplier) * 100)
            tier_breakdown.append(
                {
                    "tier": label,
                    "range": f"{start:,.0f} - {end:,.0f} FC" if end != float('inf') else f"{start:,.0f}+ FC",
                    "multiplier": f"{multiplier:.1f}x",
                    "discount": f"{discount_pct}%",
                    "base_fc_in_tier": round(fc_in_tier, 2),
                    "tiered_fc_in_tier": round(fc_after_multiplier, 2),
                    "savings_in_tier": round(savings, 2),
                }
            )

        remaining -= fc_in_tier

    savings_fc = base_fc - tiered_fc
    discount_percent = (savings_fc / base_fc * 100) if base_fc > 0 else 0.0

    return {
        "base_fc": round(base_fc, 2),
        "tiered_fc": round(tiered_fc, 2),
        "discount_percent": round(discount_percent, 2),
        "savings_fc": round(savings_fc, 2),
        "savings_dollars": round(savings_fc * 0.004, 2),
        "tier_breakdown": tier_breakdown,
    }


def main() -> None:
    parser = argparse.ArgumentParser(description="Calculate cumulative Data Cloud tiering")
    parser.add_argument("--base-fc", type=float, required=True, help="Base monthly Data Cloud FC before tiering")
    parser.add_argument("--verbose", action="store_true", help="Include detailed tier breakdown")
    parser.add_argument("--pretty", action="store_true", help="Pretty-print results")
    parser.add_argument("--output", help="Optional JSON output path")
    args = parser.parse_args()

    result = calculate_tiered_fc(args.base_fc, verbose=args.verbose or args.pretty)

    if args.output:
        Path(args.output).write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")

    if args.pretty:
        print("\n" + "=" * 60)
        print("Data Cloud Tiered Multiplier Calculation")
        print("=" * 60)
        print(f"\nBase FC:          {result['base_fc']:>15,.0f} FC")
        print(f"Tiered FC:        {result['tiered_fc']:>15,.0f} FC")
        print(f"Savings:          {result['savings_fc']:>15,.0f} FC (${result['savings_dollars']:,.2f})")
        print(f"Discount:         {result['discount_percent']:>15.2f}%")

        if result["tier_breakdown"]:
            print("\nTier Breakdown:")
            print("-" * 60)
            for tier in result["tier_breakdown"]:
                if tier["base_fc_in_tier"] > 0:
                    print(f"\n{tier['tier']} ({tier['range']})")
                    print(f"  Multiplier:     {tier['multiplier']:>8} ({tier['discount']} discount)")
                    print(f"  Base FC:        {tier['base_fc_in_tier']:>15,.0f} FC")
                    print(f"  After discount: {tier['tiered_fc_in_tier']:>15,.0f} FC")
                    print(f"  Savings:        {tier['savings_in_tier']:>15,.0f} FC")
        print("\n" + "=" * 60 + "\n")
    elif not args.output:
        print(json.dumps(result, indent=2))


if __name__ == "__main__":
    main()

{
  "agent_structure": {
    "prompts": {
      "basic": 1,
      "standard": 2,
      "advanced": 0
    },
    "actions": {
      "standard": 3,
      "voice": 0,
      "sandbox": 0
    },
    "token_overages": {
      "prompts": 0,
      "actions": 0
    }
  },
  "assumptions": [
    "Agentforce only",
    "No Data Cloud usage",
    "No token overages",
    "Single-org rollout"
  ]
}

{
  "data_cloud_operations": {
    "operations": {
      "data_360_prep": 500.0,
      "data_360_streaming_pipeline": 50.0,
      "data_360_queries": 100.0,
      "data_360_segmentation": 50.0,
      "data_360_activation": 100.0
    },
    "private_connect": false
  },
  "assumptions": [
    "Data Cloud only",
    "Baseline meter volumes are scaled across standard scenarios",
    "No Private Connect"
  ]
}

{
  "agent_structure": {
    "prompts": {
      "basic": 1,
      "standard": 3,
      "advanced": 0
    },
    "actions": {
      "standard": 5,
      "voice": 0,
      "sandbox": 0
    },
    "token_overages": {
      "prompts": 0,
      "actions": 0
    }
  },
  "data_cloud_operations": {
    "operations": {
      "data_360_prep": 10.0,
      "data_360_streaming_pipeline": 2.0,
      "data_360_queries": 5.0,
      "data_360_segmentation": 1.0
    },
    "private_connect": false
  },
  "assumptions": [
    "Data Cloud volumes represent a 1K-invocation baseline for scenario scaling",
    "No token overages",
    "No Private Connect",
    "Single-org rollout"
  ]
}

#!/usr/bin/env python3
"""Manual validation helper for Flex Credit estimate inputs and outputs."""

from __future__ import annotations

import argparse
import json
import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

VALID_PROMPT_TIERS = {"starter", "basic", "standard", "advanced"}
VALID_ACTION_TYPES = {"standard", "custom", "voice", "sandbox"}

METER_ALIASES = {
    "batch_internal": "data_360_prep",
    "batch_external": "data_360_prep",
    "queries": "data_360_queries",
    "segmentation": "data_360_segmentation",
    "streaming": "data_360_streaming_pipeline",
    "activation_batch": "data_360_activation",
    "activation_streaming": "data_360_activation",
    "profile_unification": "data_360_unification",
    "unstructured": "data_360_unstructured_processing",
    "intelligent_processing": "data_360_intelligent_processing",
    "real_time_pipeline": "data_360_real_time_pipeline",
    "code_extension": "data_360_code_extension",
}

DC_REASONABLE_RANGES: Dict[str, Tuple[float, float]] = {
    "data_360_prep": (0.1, 50_000),
    "data_360_unification": (0.01, 1_000),
    "data_360_segmentation": (0.1, 10_000),
    "data_360_activation": (0.1, 10_000),
    "data_360_zero_copy_sharing": (0.1, 10_000),
    "data_360_queries": (0.001, 100_000),
    "data_360_unstructured_processing": (1, 100_000),
    "data_360_intelligent_processing": (1, 100_000),
    "data_360_streaming_pipeline": (0.01, 1_000),
    "data_360_real_time_pipeline": (0.001, 1_000),
    "data_360_code_extension": (0.1, 100_000),
}

REASONABLE_PROMPTS = (0, 20)
REASONABLE_ACTIONS = (0, 50)
REASONABLE_INVOCATIONS = (100, 10_000_000)
TYPICAL_PER_INVOCATION_RANGE = (4, 500)


@dataclass
class ValidationResult:
    passed: bool
    score: int
    max_score: int
    errors: List[str]
    warnings: List[str]
    recommendations: List[str]


def normalize_meter(meter: str) -> str:
    key = meter.strip().lower()
    return METER_ALIASES.get(key, key)


def extract_agent_structure(data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    if "agent_structure" in data and isinstance(data["agent_structure"], dict):
        return data["agent_structure"]
    if "prompts" in data or "actions" in data:
        return data
    return None


def extract_dc_operations(data: Dict[str, Any]) -> Optional[Dict[str, Any]]:
    if "data_cloud_operations" in data and isinstance(data["data_cloud_operations"], dict):
        return data["data_cloud_operations"]
    if "operations" in data:
        return data
    return None


def validate_agent_structure(agent_data: Optional[Dict[str, Any]]) -> Tuple[List[str], List[str]]:
    errors: List[str] = []
    warnings: List[str] = []

    if not agent_data:
        return errors, warnings

    prompts = agent_data.get("prompts", {})
    actions = agent_data.get("actions", {})

    total_prompts = sum(int(v) for v in prompts.values())
    total_actions = sum(int(v) for v in actions.values())

    if total_prompts < REASONABLE_PROMPTS[0]:
        warnings.append("Prompt count is below the normal range.")
    if total_prompts > REASONABLE_PROMPTS[1]:
        warnings.append(f"Many prompts configured ({total_prompts}). Consider consolidation.")

    if total_actions > REASONABLE_ACTIONS[1]:
        warnings.append(f"Many actions configured ({total_actions}). Consider consolidation or batching.")

    for tier in prompts:
        if tier.lower() not in VALID_PROMPT_TIERS:
            errors.append(f"Unknown prompt tier: {tier}")

    for action_type in actions:
        if action_type.lower() not in VALID_ACTION_TYPES:
            errors.append(f"Unknown action type: {action_type}")

    token_overages = agent_data.get("token_overages", {}) or {}
    prompt_overages = token_overages.get("prompts", 0)
    if isinstance(prompt_overages, (int, float)) and total_prompts > 0:
        overage_percent = float(prompt_overages) / total_prompts
        if overage_percent > 0.30:
            warnings.append(f"High prompt overage rate ({overage_percent:.1%}).")

    return errors, warnings


def validate_dc_operations(dc_data: Optional[Dict[str, Any]]) -> Tuple[List[str], List[str]]:
    errors: List[str] = []
    warnings: List[str] = []

    if not dc_data:
        return errors, warnings

    operations = dc_data.get("operations", {})
    if not operations:
        warnings.append("Data Cloud operations document is present but contains no operations.")
        return errors, warnings

    normalized_ops: Dict[str, float] = {}
    for meter, volume in operations.items():
        canonical = normalize_meter(meter)
        normalized_ops[canonical] = normalized_ops.get(canonical, 0.0) + float(volume)

    for meter, volume in normalized_ops.items():
        if meter not in DC_REASONABLE_RANGES:
            warnings.append(f"Unknown Data Cloud meter: {meter}")
            continue

        min_volume, max_volume = DC_REASONABLE_RANGES[meter]
        if volume < min_volume:
            warnings.append(f"{meter}: very low volume ({volume}).")
        elif volume > max_volume:
            warnings.append(f"{meter}: very high volume ({volume}). Please confirm.")

    if normalized_ops.get("data_360_streaming_pipeline", 0) > 0:
        warnings.append("Streaming usage detected. Confirm that real-time latency is actually required.")

    if normalized_ops.get("data_360_unification", 0) > 10:
        warnings.append("Unification volume is high. Recheck cadence and identity-resolution scope.")

    return errors, warnings


def validate_scenarios(scenarios: Optional[List[Dict[str, Any]]]) -> Tuple[List[str], List[str]]:
    errors: List[str] = []
    warnings: List[str] = []

    if not scenarios:
        warnings.append("No scenarios found in estimate.")
        return errors, warnings

    for scenario in scenarios:
        name = scenario.get("scenario_name", "Unknown")
        for required in ["agent_invocations", "total_monthly_fc", "total_monthly_cost"]:
            if required not in scenario:
                errors.append(f"{name}: scenario missing required field {required}")

        invocations = int(scenario.get("agent_invocations", 0) or 0)
        if invocations < REASONABLE_INVOCATIONS[0]:
            warnings.append(f"{name}: very low invocation count ({invocations}).")
        elif invocations > REASONABLE_INVOCATIONS[1]:
            warnings.append(f"{name}: very high invocation count ({invocations:,}).")

        af_total = float(scenario.get("af_total_fc", 0) or 0)
        if invocations > 0 and af_total > 0:
            per_invocation = af_total / invocations
            if per_invocation < TYPICAL_PER_INVOCATION_RANGE[0]:
                warnings.append(f"{name}: low per-invocation cost ({per_invocation:.1f} FC).")
            elif per_invocation > TYPICAL_PER_INVOCATION_RANGE[1]:
                warnings.append(f"{name}: high per-invocation cost ({per_invocation:.1f} FC).")

        dc_base = float(scenario.get("dc_base_fc", 0) or 0)
        dc_tiered = float(scenario.get("dc_tiered_fc", 0) or 0)
        if dc_base > 300_000 and dc_tiered >= dc_base:
            errors.append(f"{name}: Data Cloud tiering does not appear to have been applied.")

        pc = float(scenario.get("dc_private_connect_fc", 0) or 0)
        if pc > 0 and dc_tiered > 0:
            expected_pc = round(dc_tiered * 0.20, 2)
            if abs(pc - expected_pc) / expected_pc > 0.01:
                errors.append(f"{name}: Private Connect appears inconsistent with tiered Data Cloud FC.")

    return errors, warnings


def calculate_score(errors: List[str], warnings: List[str]) -> int:
    score = 130
    score -= len(errors) * 10
    score -= len(warnings) * 2
    return max(0, score)


def validate_estimate(data: Dict[str, Any]) -> ValidationResult:
    errors: List[str] = []
    warnings: List[str] = []
    recommendations: List[str] = []

    agent_data = extract_agent_structure(data)
    dc_data = extract_dc_operations(data)
    scenarios = data.get("scenarios")

    if not agent_data and not dc_data:
        errors.append("Estimate must contain agent_structure, data_cloud_operations, or both.")

    agent_errors, agent_warnings = validate_agent_structure(agent_data)
    dc_errors, dc_warnings = validate_dc_operations(dc_data)
    scenario_errors, scenario_warnings = validate_scenarios(scenarios)

    errors.extend(agent_errors + dc_errors + scenario_errors)
    warnings.extend(agent_warnings + dc_warnings + scenario_warnings)

    if dc_data and dc_data.get("private_connect"):
        recommendations.append("Double-check that Private Connect is truly required, since it adds 20% to tiered Data Cloud FC.")

    if dc_data and normalize_meter("streaming") in {normalize_meter(m) for m in dc_data.get("operations", {})}:
        recommendations.append("Compare a lower-streaming alternative if latency requirements allow it.")

    if agent_data:
        prompts = sum(int(v) for v in agent_data.get("prompts", {}).values())
        actions = sum(int(v) for v in agent_data.get("actions", {}).values())
        if actions > prompts and actions >= 5:
            recommendations.append("Action count may be the dominant cost driver. Look for consolidation opportunities.")

    score = calculate_score(errors, warnings)
    passed = len(errors) == 0 and score >= 78

    return ValidationResult(
        passed=passed,
        score=score,
        max_score=130,
        errors=errors,
        warnings=warnings,
        recommendations=recommendations,
    )


def main() -> None:
    parser = argparse.ArgumentParser(description="Validate a Flex Credit estimate JSON document")
    parser.add_argument("--input", required=True, help="Path to estimate JSON")
    parser.add_argument("--verbose", action="store_true", help="Pretty-print output")
    args = parser.parse_args()

    try:
        data = json.loads(Path(args.input).read_text(encoding="utf-8"))
    except Exception as exc:  # pragma: no cover - CLI path
        print(f"Error loading estimate: {exc}", file=sys.stderr)
        raise SystemExit(1)

    result = validate_estimate(data)

    if args.verbose:
        print("\n" + "=" * 60)
        print("Flex Credit Estimate Validation")
        print("=" * 60)
        print(f"\nStatus: {'✅ PASSED' if result.passed else '❌ FAILED'}")
        print(f"Score: {result.score}/{result.max_score} ({result.score / result.max_score * 100:.1f}%)")

        if result.errors:
            print(f"\n❌ Errors ({len(result.errors)}):")
            for error in result.errors:
                print(f"  - {error}")

        if result.warnings:
            print(f"\n⚠️  Warnings ({len(result.warnings)}):")
            for warning in result.warnings:
                print(f"  - {warning}")

        if result.recommendations:
            print("\n💡 Recommendations:")
            for recommendation in result.recommendations:
                print(f"  - {recommendation}")

        print("\n" + "=" * 60 + "\n")
    else:
        print(
            json.dumps(
                {
                    "passed": result.passed,
                    "score": result.score,
                    "max_score": result.max_score,
                    "errors": result.errors,
                    "warnings": result.warnings,
                    "recommendations": result.recommendations,
                },
                indent=2,
            )
        )

    raise SystemExit(0 if result.passed else 1)


if __name__ == "__main__":
    main()

MIT License

Copyright (c) 2024-2025 Jag Valaiyapathy

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

sf-flex-estimator

Flex Credit estimation for Agentforce and Data Cloud workloads in sf-skills.

This skill helps you estimate:

Agentforce per-invocation Flex Credits from prompt and action structure
Data Cloud monthly Flex Credits from usage meters
cumulative Data Cloud tier discounts
Private Connect uplift
low / medium / high / enterprise scenarios

What this skill is for

Use sf-flex-estimator when you need planning guidance before implementation:

pricing a new agent design
sizing a pilot vs production rollout
comparing architecture options
identifying the main cost drivers in a proposed design

What this skill is not for

It does not create Agentforce metadata or .agent files.
It does not provision or mutate Data Cloud assets.
It does not replace contract-specific pricing from Salesforce commercial teams.

Quick start

Basic agent estimate

python3 assets/calculators/flex_calculator.py \
  --mode structure \
  --agent-def assets/templates/basic-agent-template.json

Agentforce + Data Cloud scenario estimate

python3 assets/calculators/flex_calculator.py \
  --mode scenarios \
  --agent-def assets/templates/hybrid-agent-template.json

Data Cloud tiering only

python3 assets/calculators/tier_multiplier.py \
  --base-fc 5000000 \
  --pretty

Validate an estimate input

python3 hooks/scripts/validate_estimate.py \
  --input assets/templates/hybrid-agent-template.json \
  --verbose

Included templates

Template	Best for
`assets/templates/basic-agent-template.json`	Agentforce-only estimates
`assets/templates/hybrid-agent-template.json`	Agentforce + Data Cloud grounding
`assets/templates/data-cloud-template.json`	Data Cloud-only workloads

Included references

Path	Purpose
`SKILL.md`	main skill instructions and handoff rules
`references/agentforce-pricing.md`	Agentforce prompt/action pricing
`references/data-cloud-pricing.md`	Data Cloud meter pricing and tiering
`references/calculation-methodology.md`	formulas, assumptions, and scenario logic
`references/common-use-cases.md`	sample estimation patterns
`references/edge-cases.md`	token overages, Private Connect, multi-org notes
`references/scoring-rubric.md`	130-point validation rubric

Key pricing assumptions in this skill

Agentforce uses linear pricing in this model.
Data Cloud uses monthly cumulative tiering:
Tier 1: 0 - 300K at 1.0x
Tier 2: 300K - 1.5M at 0.8x
Tier 3: 1.5M - 12.5M at 0.4x
Tier 4: 12.5M+ at 0.2x
Flex Credits are modeled at $0.004 per FC.
Private Connect adds 20% of Data Cloud spend after tiering.
Estimates use public list-price guidance only.

Notes on validation

hooks/scripts/validate_estimate.py is a manual validation helper. It is not connected to the shared repo-wide auto-validation dispatcher because generic estimate files do not map cleanly to a single file extension or path convention.

Credit

Primary contributor: August Krys

Related repo:

not2technical/sf-skills

License

MIT — see LICENSE

Agentforce Flex Credit Pricing Reference

Overview

Agentforce pricing is based on per-invocation charges for prompts and actions. Unlike Data Cloud, Agentforce does not use tiered multipliers - pricing is linear.

Key Concepts:

Per-Invocation Billing: Each agent execution incurs prompt + action charges
Prompt Tiers: Four tiers based on model capability (2-16 FC)
Action Types: Three types with different rates (16-30 FC)
Token Overages: Additional charges when exceeding token limits
No Volume Discounts: Linear pricing regardless of volume

---

Prompt/Topic Pricing

Prompt Tier Table

Tier	FC per Call	Cost	Model Examples	Use Case
Starter (BYOLLM)	2 FC	$0.008	Bring Your Own LLM	Custom model integration
Basic	2 FC	$0.008	Gemini Flash, GPT-4o mini	Simple classification, routing
Standard	4 FC	$0.016	GPT-4o, Claude Sonnet 4	General-purpose agents
Advanced	16 FC	$0.064	Future deep research models	Complex reasoning, research

How to Choose a Tier

Use Basic (2 FC) when:

Simple intent classification
Yes/no questions
Basic routing logic
FAQ responses
Low-complexity reasoning

Use Standard (4 FC) when:

Multi-step reasoning required
Context-aware responses
Integration with multiple data sources
Moderate complexity workflows
Most production agents (80% of use cases)

Use Advanced (16 FC) when:

Deep research required
Complex multi-step planning
Advanced reasoning tasks
Specialized domain expertise
High-stakes decision making

Cost Comparison:

Scenario	Basic (2 FC)	Standard (4 FC)	Advanced (16 FC)
1,000 calls	$8	$16	$64
10,000 calls	$80	$160	$640
100,000 calls	$800	$1,600	$6,400

Recommendation: Start with Standard tier for production agents. Downgrade to Basic only for simple classification topics. Reserve Advanced for specialized deep-research topics.

---

Action Pricing

Action Type Table

Action Type	FC per Invocation	Cost	Use Case
Standard/Custom Action	20 FC	$0.08	API calls, data operations, integrations
Voice Action	30 FC	$0.12	Voice-enabled agent actions
Sandbox Action	16 FC	$0.064	Testing/development actions

Action Details

Standard/Custom Actions (20 FC):

Most common action type
Includes:
Apex action invocations
Flow invocations
External API calls (via Named Credentials)
Data operations (SOQL queries, DML)
Custom integrations
Used in 95% of production agents

Voice Actions (30 FC):

Voice-enabled action execution
50% premium over standard actions
Includes:
Speech-to-text processing
Voice-optimized responses
Telephony integrations
Use when: Voice channel required, accessibility needs

Sandbox Actions (16 FC):

Development/testing environment
20% discount vs standard actions
Use when: Testing new actions, staging environments
Not for production use

---

Token Overage Pricing

Token Limits

Prompt Token Limit: 2,000 tokens

Average prompt: 500-1,000 tokens
~1,500 words or ~10KB of text
Includes: System instructions + user input + context

Action Token Limit: 10,000 tokens

Average action: 2,000-5,000 tokens
~7,500 words or ~50KB of text
Includes: Action input + output + metadata

Overage Charges

When overage occurs:

Additional prompt charge: 1 × prompt tier rate
Additional action charge: 1 × action rate

Example: Standard Prompt Overage

Normal prompt (1,500 tokens): 4 FC
Oversized prompt (2,500 tokens): 4 FC + 4 FC = 8 FC
Cost: $0.016 → $0.032 (2x)

Example: Standard Action Overage

Normal action (5,000 tokens): 20 FC
Oversized action (12,000 tokens): 20 FC + 20 FC = 40 FC
Cost: $0.08 → $0.16 (2x)

Overage Frequency

Typical overage rates:

Prompts: <1% in well-designed agents
Actions: <1% in normal operations

High overage risk scenarios:

Large document grounding (>5 pages)
Complex multi-step reasoning
Extensive context injection
Large API response processing

How to estimate:

Simple agents: 0% overage
Moderate complexity: 5% prompt overage, 0% action overage
Complex agents: 20% prompt overage, 1% action overage

Avoiding Overages

Prompt optimization:

Keep system instructions concise (<500 tokens)
Limit context injection (<1,000 tokens)
Use retrieval for large documents (don't embed full text)
Paginate long responses

Action optimization:

Limit API response sizes
Use field filtering in SOQL (SELECT only needed fields)
Paginate large result sets
Compress action outputs

---

Per-Invocation Cost Calculation

Formula

per_invocation_fc = Σ(prompt_fc) + Σ(action_fc) + overage_fc
per_invocation_cost = per_invocation_fc × $0.004

Example 1: Simple Customer Service Agent

Structure:

3 prompts (Basic tier): Classification, Response Generation, Escalation Check
2 actions (Standard): Fetch Case Data, Update Case
No token overages

Calculation:

Prompts: 3 × 2 FC = 6 FC
Actions: 2 × 20 FC = 40 FC
Total: 46 FC per invocation = $0.184

Monthly costs:
- 1K invocations: 46K FC = $184
- 10K invocations: 460K FC = $1,840
- 100K invocations: 4.6M FC = $18,400

Example 2: Complex Sales Agent

Structure:

5 prompts (Standard tier): Intent, Lead Qualification, Recommendation, Objection Handling, Summary
10 actions (Standard): CRM lookups, opportunity updates, email sends
20% prompt overage (complex reasoning)

Calculation:

Prompts: 5 × 4 FC = 20 FC
Actions: 10 × 20 FC = 200 FC
Overages: 20% × 1 prompt × 4 FC = 0.8 FC (per invocation average)
Total: 220.8 FC per invocation ≈ 221 FC = $0.884

Monthly costs:
- 1K invocations: 221K FC = $884
- 10K invocations: 2.21M FC = $8,840
- 100K invocations: 22.1M FC = $88,400

Example 3: Voice-Enabled Agent

Structure:

4 prompts (Standard tier): Voice transcription processing, Intent, Response, Confirmation
6 actions (Voice): Phone system integration, CRM updates, scheduling
No overages

Calculation:

Prompts: 4 × 4 FC = 16 FC
Actions: 6 × 30 FC = 180 FC
Total: 196 FC per invocation = $0.784

Monthly costs:
- 1K invocations: 196K FC = $784
- 10K invocations: 1.96M FC = $7,840
- 100K invocations: 19.6M FC = $78,400

---

Optimization Strategies

1. Prompt Tier Optimization

Before: All Standard Prompts

Agent: 5 prompts (Standard)
Per-invocation: 5 × 4 FC = 20 FC
Cost per 100K: $8,000

After: Mixed Tier Strategy

Agent:
- 2 prompts (Basic): Classification, routing
- 3 prompts (Standard): Main reasoning
Per-invocation: (2 × 2) + (3 × 4) = 16 FC
Cost per 100K: $6,400
Savings: $1,600 (20%)

2. Action Consolidation

Before: Many Small Actions

Agent: 10 actions (each a separate API call)
Per-invocation: 10 × 20 FC = 200 FC
Cost per 100K: $80,000

After: Batch Operations

Agent: 4 actions (batch API calls, combined operations)
Per-invocation: 4 × 20 FC = 80 FC
Cost per 100K: $32,000
Savings: $48,000 (60%)

Consolidation techniques:

Batch API calls where possible
Combine related data fetches
Use composite APIs (e.g., Salesforce Composite API)
Cache frequently accessed data

3. Token Overage Prevention

Before: 20% Prompt Overages

Base: 5 × 4 FC = 20 FC
Overages: 20% × 5 × 4 FC = 4 FC
Total: 24 FC per invocation
Cost per 100K: $9,600

After: Optimized Context

Base: 5 × 4 FC = 20 FC
Overages: 0% (optimized)
Total: 20 FC per invocation
Cost per 100K: $8,000
Savings: $1,600 (16.7%)

4. Agent Structure Review

High-cost patterns to avoid:

❌ Using Advanced tier prompts unnecessarily
❌ Calling actions that return unused data
❌ Redundant prompts (duplicate reasoning steps)
❌ Overly complex system instructions
❌ Not batching related operations

Cost-efficient patterns:

✅ Use Basic tier for classification/routing
✅ Batch related API calls
✅ Keep system instructions concise
✅ Use retrieval instead of full-text embedding
✅ Cache frequently accessed data

---

Agent Structure Templates

Minimal Agent (Low Cost)

Prompts:
- 1 Basic: Intent classification (2 FC)
- 1 Standard: Response generation (4 FC)

Actions:
- 1 Standard: Data fetch (20 FC)

Total: 26 FC per invocation = $0.104

Use case: Simple FAQ bot, basic routing

Standard Agent (Balanced)

Prompts:
- 1 Basic: Classification (2 FC)
- 3 Standard: Reasoning, response, validation (12 FC)

Actions:
- 5 Standard: Data operations, integrations (100 FC)

Total: 114 FC per invocation = $0.456

Use case: Customer service, sales agent, most production use cases

Complex Agent (High Cost)

Prompts:
- 2 Basic: Classification, routing (4 FC)
- 5 Standard: Main reasoning (20 FC)
- 1 Advanced: Deep research (16 FC)

Actions:
- 10 Standard: Extensive integrations (200 FC)

Total: 240 FC per invocation = $0.96

Use case: Complex reasoning, multi-system integration, research agents

---

Real-World Cost Examples

Example 1: E-commerce Customer Service (10K invocations/month)

Agent Structure:

3 Standard prompts: Intent, Product Lookup, Response
4 Standard actions: Order lookup, inventory check, update CRM, send email

Costs:

Prompts: 3 × 4 = 12 FC
Actions: 4 × 20 = 80 FC
Total: 92 FC × 10,000 = 920K FC
Monthly: $3,680
Annual: $44,160

Example 2: Financial Services Advisor (100K invocations/month)

Agent Structure:

5 Standard prompts: Classification, risk assessment, recommendation, compliance check, summary
8 Standard actions: Account lookups, transaction queries, portfolio analysis, reporting
10% prompt overage (complex calculations)

Costs:

Prompts: 5 × 4 = 20 FC
Actions: 8 × 20 = 160 FC
Overages: 0.5 × 4 = 2 FC (average per invocation)
Total: 182 FC × 100,000 = 18.2M FC
Monthly: $72,800
Annual: $873,600

Example 3: Healthcare Appointment Scheduler (Voice, 5K invocations/month)

Agent Structure:

4 Standard prompts: Transcription processing, intent, scheduling, confirmation
6 Voice actions: Phone system, calendar lookups, booking, SMS notifications

Costs:

Prompts: 4 × 4 = 16 FC
Actions: 6 × 30 = 180 FC
Total: 196 FC × 5,000 = 980K FC
Monthly: $3,920
Annual: $47,040

---

Calculation Checklist

When estimating Agentforce costs:

1. ✅ Count prompts/topics by tier (Basic/Standard/Advanced) 2. ✅ Count actions by type (Standard/Voice/Sandbox) 3. ✅ Estimate token overages (0-20% of prompts, 0-1% of actions) 4. ✅ Calculate per-invocation FC (sum of all components) 5. ✅ Apply to usage scenarios (1K, 10K, 100K, 500K invocations) 6. ✅ Document agent structure (for future re-estimation) 7. ✅ Identify optimization opportunities (tier changes, action consolidation)

---

Common Mistakes

❌ Mistake 1: Applying Tiered Multipliers to Agentforce

# WRONG: Agentforce does not use tiered multipliers
af_fc = per_invocation_fc × invocations × 0.8  # No discount!

# CORRECT: Linear pricing
af_fc = per_invocation_fc × invocations

❌ Mistake 2: Not Counting All Prompts

# WRONG: Only counting "main" prompts
prompts = 2  # Missing classification, validation, etc.

# CORRECT: Count every prompt/topic in agent definition
prompts = 5  # All topics that execute per invocation

❌ Mistake 3: Overage on Total Instead of Per-Component

# WRONG: Applying overage to total
total_fc = prompt_fc + action_fc
overage_fc = total_fc × 0.20

# CORRECT: Overage on prompts only (usually)
prompt_overage_fc = prompt_fc × 0.20
action_overage_fc = action_fc × 0.01  # Rare

---

Python Calculation Tools

Using flex_calculator.py

# Calculate per-invocation cost
python3 flex_calculator.py --mode structure --agent-def agent.json

# Generate full scenarios
python3 flex_calculator.py --mode scenarios \
  --agent-def agent.json \
  --output scenarios.json

Agent Definition JSON Format

{
  "prompts": {
    "basic": 2,
    "standard": 3,
    "advanced": 0
  },
  "actions": {
    "standard": 5,
    "voice": 0,
    "sandbox": 0
  },
  "token_overages": {
    "prompts": 0,
    "actions": 0
  }
}

---

Version History

v1.0.0 (2026-03-10): Initial Agentforce pricing reference with per-invocation calculation

Calculation Methodology

This document defines how sf-flex-estimator turns an Agentforce + Data Cloud design into a public list-price Flex Credit estimate.

1. Scope the workload

Start by separating the estimate into two buckets:

1. Agentforce

prompt tiers
action types
token overages

2. Data Cloud

monthly usage meters
Private Connect requirement

These buckets are priced differently and should be modeled independently before combining the result.

---

2. Agentforce per-invocation formula

Agentforce is modeled per invocation.

per-invocation FC = prompt FC + action FC + token overage FC
per-invocation cost = per-invocation FC × 0.004

Prompt rates

Tier	FC
starter	2
basic	2
standard	4
advanced	16

Action rates

Type	FC
standard	20
custom	20
voice	30
sandbox	16

Example

Prompts:
- 1 basic = 2 FC
- 3 standard = 12 FC

Actions:
- 5 standard = 100 FC

Per invocation = 114 FC
Per invocation cost = 114 × 0.004 = $0.456

---

3. Token overage handling

This skill models token overages as additional prompt or action charges.

Preferred input

Use explicit counts when you know them:

{
  "token_overages": {
    "prompts": 1,
    "actions": 0
  }
}

That means one extra prompt-sized charge is added per invocation.

If exact distribution is unknown

The calculator uses a weighted average prompt or action rate based on the configured structure.

Example:

Prompts:
- 1 basic (2 FC)
- 3 standard (4 FC)
Average prompt rate = 14 / 4 = 3.5 FC

If prompt overages = 1:
extra prompt FC = 3.5

This keeps the estimate usable when the user only knows that overages occur, not which exact prompt/action causes them.

---

4. Data Cloud base FC formula

Data Cloud is modeled from monthly meter volumes.

base meter FC = monthly volume × public rate
base Data Cloud FC = sum(all meter FC)

Current public meter set used by this skill

Meter	FC per 1M units
data_360_prep	40
data_360_unification	75,000
data_360_segmentation	50
data_360_activation	60
data_360_zero_copy_sharing	60
data_360_queries	3
data_360_streaming_pipeline	3,500
data_360_real_time_pipeline	250,000
data_360_code_extension	40

Meter units that are not per 1M rows

Meter	Unit
data_360_unstructured_processing	MB
data_360_intelligent_processing	MB
data_360_code_extension	compute unit

The calculator accepts both current names and a limited set of legacy aliases, but the recommendation is to use the current data_360_* names in new estimate inputs.

---

5. Data Cloud tiering

After the base monthly Data Cloud FC is calculated, apply cumulative monthly tiering.

Tier	Range	Multiplier
1	0 - 300K	1.0x
2	300K - 1.5M	0.8x
3	1.5M - 12.5M	0.4x
4	12.5M+	0.2x

Cumulative example: 5M FC base

Tier 1:   300,000 × 1.0 =   300,000
Tier 2: 1,200,000 × 0.8 =   960,000
Tier 3: 3,500,000 × 0.4 = 1,400,000
Total tiered FC = 2,660,000

Effective discount = (5,000,000 - 2,660,000) / 5,000,000 = 46.8%

---

6. Private Connect

If Private Connect is enabled, apply it after Data Cloud tiering.

private connect FC = tiered Data Cloud FC × 0.20

Example:

tiered Data Cloud FC = 2,660,000
Private Connect FC = 532,000
Data Cloud total = 3,192,000

---

7. Combined monthly total

total monthly FC = Agentforce FC + tiered Data Cloud FC + Private Connect FC
total monthly cost = total monthly FC × 0.004
total annual cost = total monthly cost × 12

---

8. Standard scenario generation

The default scenario set in this skill is:

Scenario	Invocations / month
Low	1,000
Medium	10,000
High	100,000
Enterprise	500,000

Default scaling behavior

If Data Cloud operations are present, the calculator scales the monthly meter volumes in proportion to the scenario multiplier using 1K invocations as the baseline.

Example baseline:

{
  "data_cloud_operations": {
    "operations": {
      "data_360_prep": 10.0,
      "data_360_streaming_pipeline": 2.0,
      "data_360_queries": 5.0,
      "data_360_segmentation": 1.0
    }
  }
}

Then the generated scenarios use:

Scenario	Scale factor
Low	1x
Medium	10x
High	100x
Enterprise	500x

This is useful when the user only has a baseline design and wants a rough scenario ladder.

---

9. Validation philosophy

The 130-point rubric is intended to check:

completeness of the structure model
correctness of the pricing model used
realism of usage assumptions
clarity of scenario output
handling of edge cases like overages and Private Connect

Use the manual validator when you want a fast sanity check:

python3 hooks/scripts/validate_estimate.py --input assets/templates/hybrid-agent-template.json --verbose

---

10. What this skill does not model

This skill intentionally does not model:

custom contract discounts
promotional pricing
regional commercial exceptions
non-public enterprise agreement terms
operational costs outside Flex Credits (implementation effort, support, monitoring, data quality work)

When the user needs commercial certainty, treat this skill as a public baseline estimate, not a substitute for actual pricing approval.

Common Use Cases

This file shows a few representative estimation patterns supported by sf-flex-estimator.

1. Basic Agentforce-only estimate

Template:

assets/templates/basic-agent-template.json

Structure:

1 basic prompt
2 standard prompts
3 standard actions
no Data Cloud

Result

Per invocation: 70 FC ($0.28)

Typical scenarios

Scenario	Monthly FC	Monthly cost	Annual cost
Low (1K)	70,000	$280.00	$3,360.00
Medium (10K)	700,000	$2,800.00	$33,600.00
High (100K)	7,000,000	$28,000.00	$336,000.00

Best fit:

FAQ agents
lightweight service triage
pilot implementations without Data Cloud grounding

---

2. Hybrid Agentforce + Data Cloud estimate

Template:

assets/templates/hybrid-agent-template.json

Structure:

1 basic prompt
3 standard prompts
5 standard actions
Data Cloud baseline:
data_360_prep: 10.0
data_360_streaming_pipeline: 2.0
data_360_queries: 5.0
data_360_segmentation: 1.0

Per invocation

114 FC ($0.456)

Standard generated scenarios

Scenario	Total monthly FC	Monthly cost	Annual cost	Data Cloud discount
Low (1K)	121,465	$485.86	$5,830.32	0.00%
Medium (10K)	1,214,650	$4,858.60	$58,303.20	0.00%
High (100K)	12,057,200	$48,228.80	$578,745.60	11.96%
Enterprise (500K)	59,153,000	$236,612.00	$2,839,344.00	42.32%

Best fit:

service agents grounded with unified customer context
order history or profile lookup use cases
architecture tradeoff conversations where the user wants to compare action count versus Data Cloud scaling

---

3. Data Cloud-only estimate

Template:

assets/templates/data-cloud-template.json

Baseline meters:

data_360_prep: 500.0
data_360_streaming_pipeline: 50.0
data_360_queries: 100.0
data_360_segmentation: 50.0
data_360_activation: 100.0

Standard generated scenarios

Scenario	Base Data Cloud FC	Tiered FC	Monthly cost	Annual cost
Low (1K)	203,800	203,800	$815.20	$9,782.40
Medium (10K)	2,038,000	1,475,200	$5,900.80	$70,809.60
High (100K)	20,380,000	7,236,000	$28,944.00	$347,328.00
Enterprise (500K)	101,900,000	23,540,000	$94,160.00	$1,129,920.00

Best fit:

Data Cloud planning before an agent exists
ingestion/segment/activation scenario modeling
conversations focused on meter mix and tier savings

---

Command patterns

# Agentforce only
python3 assets/calculators/flex_calculator.py --mode structure --agent-def assets/templates/basic-agent-template.json

# Hybrid scenario ladder
python3 assets/calculators/flex_calculator.py --mode scenarios --agent-def assets/templates/hybrid-agent-template.json

# Data Cloud tiering sanity check
python3 assets/calculators/tier_multiplier.py --base-fc 5000000 --pretty

Data Cloud Flex Credit Pricing Reference

This reference captures the public Data Cloud pricing model used by sf-flex-estimator.

Core concepts

Data Cloud is modeled from monthly usage meters.
The base monthly FC total is then reduced using cumulative monthly tiering.
Private Connect is modeled as 20% of tiered Data Cloud FC.
This document reflects the public list-price assumptions used by the skill, not contract-specific pricing.

---

Current meter table used by the skill

Meter	FC per 1M units	Typical use
`data_360_prep`	40	preparation and transformation
`data_360_unification`	75,000	identity resolution / unification
`data_360_segmentation`	50	segment creation and refresh
`data_360_activation`	60	outbound activation/export
`data_360_zero_copy_sharing`	60	sharing without copying
`data_360_queries`	3	query execution
`data_360_streaming_pipeline`	3,500	streaming/event ingestion
`data_360_real_time_pipeline`	250,000	high-cost real-time pipeline workloads
`data_360_code_extension`	40	code extension compute

Meter units that are not per 1M rows

Meter	Unit
`data_360_unstructured_processing`	MB
`data_360_intelligent_processing`	MB
`data_360_code_extension`	compute unit

---

Legacy alias handling

The calculator accepts a compatibility layer for common legacy names, including:

batch_internal → data_360_prep
queries → data_360_queries
segmentation → data_360_segmentation
streaming → data_360_streaming_pipeline
profile_unification → data_360_unification
activation_batch / activation_streaming → data_360_activation

For new estimate inputs, prefer the current data_360_* meter names.

---

Tier structure

Data Cloud tiering is applied monthly and cumulatively.

Tier	Monthly FC range	Multiplier	Discount
Tier 1	`0 - 300,000`	`1.0x`	0%
Tier 2	`300,000 - 1,500,000`	`0.8x`	20%
Tier 3	`1,500,000 - 12,500,000`	`0.4x`	60%
Tier 4	`12,500,000+`	`0.2x`	80%

Cumulative example: 5M base FC

Tier 1:   300,000 × 1.0 =   300,000
Tier 2: 1,200,000 × 0.8 =   960,000
Tier 3: 3,500,000 × 0.4 = 1,400,000
Total tiered FC = 2,660,000

Savings = 5,000,000 - 2,660,000 = 2,340,000 FC
Effective discount = 46.8%

---

Private Connect

Private Connect is modeled as:

Private Connect FC = tiered Data Cloud FC × 0.20

Example

Tiered Data Cloud FC: 2,660,000
Private Connect FC:     532,000
Data Cloud total FC:  3,192,000

Private Connect applies only to the Data Cloud portion of the estimate in this skill.

---

Practical interpretation of the meters

`data_360_prep`

Use for transformation-heavy preparation workloads and baseline ingestion prep.

`data_360_streaming_pipeline`

Use for true streaming/event-style ingestion. This is often one of the largest cost drivers in mixed workloads.

`data_360_queries`

Usually a relatively small meter unless the workload is heavily query-driven.

`data_360_unification`

This is a high-cost meter and deserves explicit discussion whenever identity resolution volume is large.

`data_360_activation`

Model outbound audience or data activation here.

---

Optimization heuristics

1. Challenge streaming assumptions

Streaming can dominate monthly FC faster than prep/query/segment meters. If hourly or scheduled latency is acceptable, estimate a batch-oriented alternative and compare.

2. Separate high-cost meters from background meters

Call out data_360_unification, data_360_streaming_pipeline, and data_360_real_time_pipeline explicitly so the user sees what is really driving cost.

3. Show the impact of tiering at scale

The effective discount can change materially as Data Cloud FC crosses 300K, 1.5M, and 12.5M monthly thresholds.

4. Price Private Connect separately

Keep Private Connect visible as its own line item. It should not disappear into the base meter total.

---

Example base calculation

{
  "data_cloud_operations": {
    "operations": {
      "data_360_prep": 10.0,
      "data_360_streaming_pipeline": 2.0,
      "data_360_queries": 5.0,
      "data_360_segmentation": 1.0
    },
    "private_connect": false
  }
}

data_360_prep:               10 ×    40 =     400 FC
data_360_streaming_pipeline:  2 × 3,500 =   7,000 FC
data_360_queries:            5 ×     3 =      15 FC
data_360_segmentation:       1 ×    50 =      50 FC
Base Data Cloud FC = 7,465 FC

Since 7,465 FC is below Tier 2, tiering does not change the result.

---

Command reference

python3 assets/calculators/tier_multiplier.py --base-fc 5000000 --pretty
python3 assets/calculators/flex_calculator.py --mode scenarios --agent-def assets/templates/hybrid-agent-template.json

---

Pricing caution

This skill intentionally stays within public list-price guidance. If a user needs exact commercial numbers for a live deal, call out that contract-specific terms may differ from the estimate produced here.

Edge Cases

This document highlights the most common scenarios where a fast estimate can become misleading if assumptions are not called out explicitly.

1. Token overages

If prompts or actions exceed their normal token thresholds, add extra prompt/action charges per invocation.

Guidance

use explicit overage counts when known
if the user only knows that overages happen, document that the estimate is using a weighted average overage charge
high-context grounding, large payload actions, and document-heavy prompts deserve extra scrutiny

---

2. Private Connect

Private Connect is a separate uplift on top of tiered Data Cloud FC.

Private Connect FC = tiered Data Cloud FC × 0.20

Guidance

show it as its own line item
do not bury it inside the base Data Cloud number
confirm whether it is actually required for the target deployment

---

3. Unification-heavy workloads

data_360_unification is one of the highest-cost meters in the public model.

Guidance

challenge the proposed cadence
check whether the volume is full-refresh or incremental
show unification separately from lower-cost prep/query/segment meters

---

4. Streaming overuse

data_360_streaming_pipeline can dominate monthly cost faster than prep/query/segment meters.

Guidance

estimate an alternative with lower streaming volume whenever the business process tolerates latency
present both the real-time and batch-oriented option if the user is undecided

---

5. Multi-org architectures

This skill can still guide multi-org planning, but the estimate should be broken into buckets instead of pretending everything is one homogeneous workload.

Recommended split:

home org Agentforce spend
home org Data Cloud spend
companion org Data Cloud spend
Private Connect by org, if required

Guidance

keep each org or region as its own line item
do not assume the same Data Cloud volumes across every org unless the user confirms that assumption
note that this skill is still a public-price planning model, not a substitute for commercial review

---

6. Contract-specific pricing

This skill intentionally uses public list-price assumptions.

Guidance

If the user asks for exact commercial numbers:

provide the public baseline estimate
explicitly say commercial terms may differ
avoid claiming contract accuracy that the public model cannot guarantee

---

7. Unknown or legacy meter names

Older notes may use legacy names such as batch_internal, streaming, queries, or profile_unification.

Guidance

normalize them to current data_360_* meters when possible
call out that the estimate is using the current public meter model
avoid mixing current and legacy labels in the final report

Scoring Rubric

Overview

SF-Flex-Estimator uses a 130-point scoring system across 7 categories to evaluate estimate quality. This document provides detailed scoring criteria for each category.

---

Scoring Categories

1. Use Case Analysis (25 points)

Purpose: Measure completeness of workload decomposition and component identification.

Score	Criteria
23-25 (Excellent)	• All agent components identified (prompts, actions, DC ops)<br>• Prompt tiers correctly determined<br>• Action types properly categorized<br>• Data Cloud operations fully mapped<br>• Requirements gathering complete with no gaps
18-22 (Good)	• Most components identified<br>• Minor gaps in prompt tier determination<br>• Action types mostly correct<br>• DC operations partially mapped<br>• Requirements mostly complete
13-17 (Fair)	• Basic components identified<br>• Some uncertainty in prompt tiers<br>• Action types partially correct<br>• DC operations incomplete<br>• Requirements have significant gaps
<13 (Poor)	• Incomplete component identification<br>• Prompt tiers unknown or incorrect<br>• Action types not categorized<br>• DC operations missing or wrong<br>• Requirements gathering inadequate

Common Deductions:

Missing prompt count: -5 points
Unknown prompt tiers: -3 points per tier
Actions not categorized: -4 points
DC operations not mapped: -5 points
No edge case consideration: -3 points

---

2. Estimation Accuracy (25 points)

Purpose: Measure correctness of Flex Credit calculations.

Score	Criteria
23-25 (Excellent)	• Per-invocation cost calculated correctly<br>• All rates applied accurately<br>• Token overages handled properly<br>• DC rates correct for all meters<br>• Math verified and correct
18-22 (Good)	• Per-invocation cost mostly correct<br>• Minor rate application errors<br>• Token overages estimated reasonably<br>• DC rates mostly correct<br>• Math has minor errors (<5%)
13-17 (Fair)	• Per-invocation cost has errors<br>• Some rates applied incorrectly<br>• Token overages not estimated<br>• DC rates partially incorrect<br>• Math errors (5-15%)
<13 (Poor)	• Per-invocation cost wrong<br>• Rates applied incorrectly<br>• Token overages ignored<br>• DC rates wrong<br>• Significant math errors (>15%)

Common Deductions:

Wrong prompt rate: -3 points per prompt
Wrong action rate: -4 points per action
Token overage not calculated: -3 points
DC rate incorrect: -3 points per meter
Calculation error >10%: -5 points

---

3. Data Cloud Tiering (20 points)

Purpose: Measure correctness of tiered multiplier application.

Score	Criteria
18-20 (Excellent)	• Correct 4-tier cumulative calculation<br>• All tier boundaries identified<br>• Volume-based discounting accurate<br>• Private Connect calculated correctly<br>• Tier savings documented
14-17 (Good)	• Tiered multiplier applied (minor errors)<br>• Most tier boundaries correct<br>• Volume discounting mostly accurate<br>• Private Connect handled<br>• Tier savings estimated
10-13 (Fair)	• Tiered multiplier attempted but errors<br>• Some tier boundaries wrong<br>• Volume discounting partially correct<br>• Private Connect may be wrong<br>• Tier savings missing
<10 (Poor)	• No tiered multiplier applied<br>• Flat discount used instead<br>• Tier boundaries ignored<br>• Private Connect calculation wrong<br>• Tier savings not calculated

Common Deductions:

Flat discount instead of cumulative: -8 points
Wrong tier boundary: -3 points each
Private Connect on wrong base (AF+DC instead of DC only): -5 points
Private Connect not applied when required: -4 points
Tier savings not documented: -2 points

Critical Errors (immediate score <10):

Using flat multiplier across all volume (e.g., base_fc × 0.6 for entire amount)
Applying Private Connect to Agentforce FC
Not applying any tiering when DC FC > 300K

---

4. Cost Breakdown (20 points)

Purpose: Measure clarity and completeness of cost presentation.

Score	Criteria
18-20 (Excellent)	• Clear categorization (AF, DC, PC)<br>• All scenarios generated (Low/Medium/High/Enterprise)<br>• Monthly and annual projections<br>• Per-component visibility<br>• Formatted for readability
14-17 (Good)	• Good categorization<br>• Most scenarios generated<br>• Monthly projections present<br>• Component breakdown mostly clear<br>• Reasonable formatting
10-13 (Fair)	• Basic categorization<br>• Limited scenarios<br>• Only monthly OR annual<br>• Component breakdown incomplete<br>• Poor formatting
<10 (Poor)	• No clear categorization<br>• Single scenario only<br>• Missing projections<br>• No component breakdown<br>• Unreadable format

Common Deductions:

Missing scenario: -3 points each (max 4 scenarios)
No annual projection: -2 points
No component breakdown: -4 points
Poor formatting: -2 points
Missing cost summary: -3 points

---

5. Edge Case Handling (15 points)

Purpose: Measure handling of special scenarios and complexity.

Score	Criteria
14-15 (Excellent)	• Token overages estimated and applied<br>• Private Connect handled correctly<br>• Multi-org scenarios addressed<br>• Sandbox vs production considered<br>• All edge cases documented
11-13 (Good)	• Token overages considered<br>• Private Connect mentioned<br>• Multi-org partially addressed<br>• Most edge cases handled
8-10 (Fair)	• Token overages mentioned<br>• Private Connect may be missed<br>• Multi-org not considered<br>• Some edge cases handled
<8 (Poor)	• Token overages ignored<br>• Private Connect not handled<br>• Multi-org not addressed<br>• Edge cases missing

Common Deductions:

Token overages not estimated: -3 points
Private Connect not considered when >1M DC FC: -4 points
Multi-org not addressed when applicable: -3 points
Sandbox costs not differentiated: -2 points
Voice actions not priced correctly: -3 points

Edge Cases Checklist:

✅ Token overages (prompts >2K, actions >10K)
✅ Private Connect (20% of DC, not AF)
✅ Multi-org deployments (Home + Companion)
✅ Sandbox vs production action rates
✅ Voice action premium (30 FC vs 20 FC)
✅ Mixed workloads (AF + DC)

---

6. Documentation Quality (15 points)

Purpose: Measure clarity of assumptions, methodology, and recommendations.

Score	Criteria
14-15 (Excellent)	• Clear assumptions documented<br>• Methodology explained<br>• Actionable recommendations (3+)<br>• Optimization opportunities identified<br>• Well-structured and readable
11-13 (Good)	• Assumptions documented<br>• Methodology described<br>• Some recommendations (1-2)<br>• Optimization mentioned<br>• Readable structure
8-10 (Fair)	• Basic assumptions<br>• Methodology unclear<br>• Limited recommendations<br>• No optimization guidance<br>• Poor structure
<8 (Poor)	• No assumptions documented<br>• Methodology missing<br>• No recommendations<br>• No optimization guidance<br>• Disorganized

Common Deductions:

Assumptions not documented: -4 points
Methodology not explained: -3 points
No optimization recommendations: -4 points
Poor structure/readability: -2 points
Missing next steps: -2 points

Documentation Checklist:

✅ Assumptions clearly stated
✅ Calculation methodology explained
✅ At least 3 optimization recommendations
✅ Savings projections for recommendations
✅ Next steps provided
✅ Confidence level stated (High/Medium/Low)

---

7. Validation & Sanity Checks (10 points)

Purpose: Measure reasonableness and validation of estimates.

Score	Criteria
9-10 (Excellent)	• All validation checks pass<br>• Realistic volume estimates<br>• Sanity checks documented<br>• Warnings flagged appropriately<br>• Confidence level assessed
7-8 (Good)	• Most validation passes<br>• Volumes reasonable<br>• Basic sanity checks<br>• Major warnings noted<br>• Confidence mentioned
5-6 (Fair)	• Some validation issues<br>• Volumes questionable<br>• Limited sanity checks<br>• Warnings may be missed<br>• Confidence unclear
<5 (Poor)	• Validation fails<br>• Unrealistic volumes<br>• No sanity checks<br>• Warnings ignored<br>• No confidence assessment

Common Deductions:

Unrealistic agent invocations (e.g., 50M/month for pilot): -2 points
Unrealistic DC volumes (e.g., 1B rows streaming): -3 points
Per-invocation cost out of range (4-500 FC): -2 points
No validation performed: -5 points
Confidence level not stated: -1 point

Validation Checks:

✅ Agent invocations: 100 - 10M/month
✅ Prompt count: 1-20 per agent
✅ Action count: 0-50 per agent
✅ Per-invocation: 4-500 FC
✅ DC volumes: Within reasonable ranges per meter
✅ Token overage: 0-30% of prompts
✅ Cost scaling: Linear for AF, tiered for DC

---

Overall Scoring

Score Calculation

Total Score = Use Case (25) + Accuracy (25) + DC Tiering (20) +
              Cost Breakdown (20) + Edge Cases (15) +
              Documentation (15) + Validation (10)
Max Score = 130 points

Quality Thresholds

Score Range	Grade	Description	Action
117-130 (90%+)	✅ Excellent	High confidence, production ready	Proceed with confidence
104-116 (80-89%)	⭐ Very Good	Production ready with minor review	Minor adjustments recommended
91-103 (70-79%)	⚠️ Good	Review assumptions before proceeding	Review and validate
78-90 (60-69%)	⚠️ Needs Work	Address gaps in analysis	Significant revision needed
<78 (<60%)	❌ BLOCKED	Critical issues must be resolved	Must re-estimate

Common Score Ranges

117-130 (Excellent):

All components identified and mapped correctly
Tiered multipliers applied accurately
All scenarios generated with proper breakdowns
Edge cases handled comprehensively
Clear documentation with actionable recommendations
All validation checks pass

104-116 (Very Good):

Most components correct, minor gaps
Tiered multipliers mostly correct
All scenarios present, minor formatting issues
Most edge cases handled
Good documentation, some recommendations
Validation mostly passes

91-103 (Good):

Core components identified, some uncertainties
Tiered multipliers applied with some errors
Limited scenarios or missing projections
Some edge cases missed
Basic documentation, limited recommendations
Some validation warnings

78-90 (Needs Work):

Missing components or incorrect mapping
Tiered multipliers wrong or not applied
Incomplete scenarios
Edge cases largely ignored
Poor documentation
Multiple validation failures

<78 (Blocked):

Critical calculation errors
Flat discount instead of tiered
No scenarios or breakdowns
Edge cases completely missed
No documentation
Unrealistic estimates

---

Scoring Examples

Example 1: Excellent Score (125/130)

Breakdown:

Use Case Analysis: 25/25 - All components identified, prompt tiers correct
Estimation Accuracy: 24/25 - Per-invocation correct, minor rounding
Data Cloud Tiering: 20/20 - Cumulative tiers applied perfectly
Cost Breakdown: 19/20 - All scenarios, minor formatting improvement
Edge Case Handling: 15/15 - Token overages, Private Connect, multi-org
Documentation: 14/15 - Clear assumptions, 3 recommendations
Validation: 8/10 - All checks pass, one minor warning

Grade: ✅ Excellent - Production ready

Example 2: Good Score (95/130)

Breakdown:

Use Case Analysis: 20/25 - Some prompt tier uncertainty
Estimation Accuracy: 22/25 - Minor rate application errors
Data Cloud Tiering: 14/20 - Tiered multiplier applied, boundary issues
Cost Breakdown: 16/20 - Missing one scenario
Edge Case Handling: 9/15 - Token overages missed, Private Connect handled
Documentation: 10/15 - Basic assumptions, limited recommendations
Validation: 4/10 - Some unrealistic volumes

Grade: ⚠️ Good - Review assumptions before proceeding

Example 3: Blocked Score (65/130)

Breakdown:

Use Case Analysis: 15/25 - Missing action types
Estimation Accuracy: 10/25 - Wrong rates applied
Data Cloud Tiering: 5/20 - Flat discount used instead of cumulative
Cost Breakdown: 12/20 - Only one scenario
Edge Case Handling: 6/15 - Most edge cases ignored
Documentation: 7/15 - No assumptions, no recommendations
Validation: 10/10 - Unrealistic volumes, no validation

Grade: ❌ BLOCKED - Must re-estimate

---

Self-Assessment Checklist

Before finalizing an estimate, verify:

Use Case Analysis (25 points)

[ ] All prompts counted and tiers assigned
[ ] All actions counted and types categorized
[ ] Token overages estimated
[ ] Data Cloud operations identified and mapped
[ ] Special requirements noted (Private Connect, multi-org)

Estimation Accuracy (25 points)

[ ] Per-invocation cost calculated
[ ] Correct rates applied to all components
[ ] Token overages factored in
[ ] DC rates correct for all meters
[ ] Math verified

Data Cloud Tiering (20 points)

[ ] Cumulative 4-tier logic applied (not flat discount)
[ ] Tier boundaries correct (300K, 1.5M, 12.5M)
[ ] Private Connect calculated correctly (20% of DC tiered FC)
[ ] Tier savings documented

Cost Breakdown (20 points)

[ ] 4 scenarios generated (Low/Medium/High/Enterprise)
[ ] Monthly and annual projections
[ ] AF, DC, PC broken out separately
[ ] Clear formatting

Edge Case Handling (15 points)

[ ] Token overages estimated
[ ] Private Connect considered if DC >1M FC
[ ] Multi-org addressed if applicable
[ ] Voice/sandbox rates correct

Documentation (15 points)

[ ] Assumptions documented
[ ] Methodology explained
[ ] 3+ optimization recommendations with savings
[ ] Next steps provided

Validation (10 points)

[ ] Volumes realistic
[ ] Per-invocation in range (4-500 FC)
[ ] Sanity checks performed
[ ] Confidence level stated

---

Version History

v1.0.0 (2026-03-10): Initial scoring rubric with 130-point system

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About

Sf Flex Estimator by the numbers

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What it does

Files

sf-flex-estimator: Agentforce & Data Cloud Flex Credit Estimation

When This Skill Owns the Task

Required Context to Gather First

Core Pricing Model

Agentforce

Data Cloud

Other rules

Recommended Workflow

1. Baseline the structure

2. Calculate the per-invocation cost

3. Calculate Data Cloud base FC

4. Generate scenarios

5. Validate assumptions and recommend optimizations

Scripts and Templates

Calculator

Validation helper

Example commands

High-Signal Estimation Rules

Output Format

Cross-Skill Integration

Reference Map

Start here

Pricing references

Validation and scoring

Credits and Sources

Primary contributor

August Krys

Public sources used for this skill

Repository adaptation

Scope of this skill

sf-flex-estimator

What this skill is for

What this skill is not for

Quick start

Basic agent estimate

Agentforce + Data Cloud scenario estimate

Data Cloud tiering only

Validate an estimate input

Included templates

Included references

Key pricing assumptions in this skill

Notes on validation

Credit

License

Agentforce Flex Credit Pricing Reference

Overview

Prompt/Topic Pricing

Prompt Tier Table

How to Choose a Tier

Action Pricing

Action Type Table

Action Details

Token Overage Pricing

Token Limits

Overage Charges

Overage Frequency

Avoiding Overages

Per-Invocation Cost Calculation

Formula

Example 1: Simple Customer Service Agent

Example 2: Complex Sales Agent

Example 3: Voice-Enabled Agent

Optimization Strategies

1. Prompt Tier Optimization

2. Action Consolidation

3. Token Overage Prevention

4. Agent Structure Review

Agent Structure Templates

Minimal Agent (Low Cost)

Standard Agent (Balanced)

Complex Agent (High Cost)

Real-World Cost Examples

Example 1: E-commerce Customer Service (10K invocations/month)

Example 2: Financial Services Advisor (100K invocations/month)

Example 3: Healthcare Appointment Scheduler (Voice, 5K invocations/month)

`data_360_prep`

`data_360_streaming_pipeline`

`data_360_queries`

`data_360_unification`

`data_360_activation`