Revenue Operations
Analyze sales pipeline coverage, stage conversion, velocity, and deal aging so solo founders can see quota risk and funnel leaks.
Overview
Revenue Operations is an agent skill most often used in Grow (also Validate pricing) that analyzes pipeline coverage, conversions, velocity, and deal aging for quota and funnel health.
Install
npx skills add https://github.com/alirezarezvani/claude-skills --skill revenue-operationsWhat is this skill?
- Computes pipeline coverage vs quota with explicit target bands (e.g. 3.0x–4.0x)
- Stage-to-stage conversion rates across Discovery through Closed Won
- Sales velocity model: deal size, win rate, cycle days, daily and monthly velocity
- Deal aging against per-stage thresholds with at-risk and over-threshold flags
- Structured JSON suitable for dashboards or agent-written exec summaries
- Example coverage target band 3.0x–4.0x with 2.21x sample ratio
- Sample funnel tracks 4 stage transitions through Closed Won
- Per-stage aging thresholds (e.g. Negotiation 56 days, Discovery 90 days)
Adoption & trust: 649 installs on skills.sh; 17.5k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You have open deals and a quota but no clear read on coverage, conversion leaks, or which opportunities are rotting in stage.
Who is it for?
Indie SaaS founders or small teams exporting pipeline JSON or CRM summaries who need RevOps-style diagnostics without hiring ops staff.
Skip if: Pre-revenue builders with no pipeline data, or deep CRM admin/setup where the bottleneck is tooling configuration not analysis.
When should I use this skill?
Reviewing CRM or JSON pipeline exports for coverage, conversion, velocity, or aging before forecasts or sales standups.
What do I get? / Deliverables
You receive structured RevOps metrics—coverage rating, stage conversions, velocity, and aging lists—you can act on in forecasting and weekly reviews.
- Coverage and quota attainment assessment
- Stage conversion and velocity summary
- Aging and at-risk deal list with day-over-threshold detail
Recommended Skills
Journey fit
Spans multiple journey phases - primary shelf plus alternate fits below.
Grow → analytics is the canonical shelf because outputs are pipeline KPIs, conversion rates, and revenue velocity—not product code. Analytics subphase matches funnel math, coverage ratios, and operational metrics founders track while scaling revenue.
Where it fits
Rate pipeline At Risk when coverage is 2.21x vs a 3–4x target before board or investor update.
Sanity-check average deal size and win rate assumptions used in revenue projections.
Flag Proposal-stage deals 28+ days over threshold and prioritize follow-up or disqualification.
How it compares
Analytics-oriented skill over funnel JSON, not a payment integration or email sequencer.
Common Questions / FAQ
Who is revenue-operations for?
Solo builders and tiny sales teams selling B2B offers who want agent help interpreting pipeline health and quota coverage.
When should I use revenue-operations?
In Grow analytics for weekly pipeline reviews, Validate pricing when sizing deals against quota, and Operate iterate when tuning sales process after a soft quarter.
Is revenue-operations safe to install?
Treat pipeline JSON as confidential; review the Security Audits panel on this Prism page and avoid pasting customer PII into prompts you do not control.
SKILL.md
READMESKILL.md - Revenue Operations
{ "coverage": { "total_pipeline_value": 1105000, "quota": 500000, "coverage_ratio": 2.21, "rating": "At Risk", "target": "3.0x - 4.0x" }, "stage_conversions": [ { "from_stage": "Discovery", "to_stage": "Qualification", "from_count": 17, "to_count": 12, "conversion_rate_pct": 70.6 }, { "from_stage": "Qualification", "to_stage": "Proposal", "from_count": 12, "to_count": 9, "conversion_rate_pct": 75.0 }, { "from_stage": "Proposal", "to_stage": "Negotiation", "from_count": 9, "to_count": 5, "conversion_rate_pct": 55.6 }, { "from_stage": "Negotiation", "to_stage": "Closed Won", "from_count": 5, "to_count": 2, "conversion_rate_pct": 40.0 } ], "velocity": { "num_opportunities": 17, "avg_deal_size": 74588.24, "win_rate_pct": 11.8, "avg_cycle_days": 32.5, "velocity_per_day": 4594.2, "velocity_per_month": 137826.09 }, "aging": { "global_aging_threshold_days": 90, "stage_thresholds": { "Discovery": 90, "Qualification": 78, "Proposal": 67, "Negotiation": 56 }, "total_open_deals": 15, "healthy_deals": 13, "at_risk_deals": 2, "aging_deals": [ { "id": "D011", "name": "Vertex Solutions", "stage": "Proposal", "age_days": 95, "threshold_days": 67, "days_over": 28, "value": 110000 }, { "id": "D014", "name": "Horizon Telecom", "stage": "Negotiation", "age_days": 60, "threshold_days": 56, "days_over": 4, "value": 250000 } ] }, "risk": { "overall_risk": "MEDIUM", "risk_factors_count": 3, "concentration_risks": [], "has_concentration_risk": false, "stage_distribution": { "Discovery": { "count": 5, "value": 194000, "pct_of_pipeline": 17.6 }, "Qualification": { "count": 3, "value": 150000, "pct_of_pipeline": 13.6 }, "Proposal": { "count": 4, "value": 333000, "pct_of_pipeline": 30.1 }, "Negotiation": { "count": 3, "value": 428000, "pct_of_pipeline": 38.7 } }, "empty_stages": [], "coverage_gaps": [ { "quarter": "2025-Q2", "pipeline_value": 344000, "quarterly_target": 125000.0, "coverage_ratio": 2.75, "gap": "Below 3x target" } ] } } # Forecast Accuracy Report - [Period] ## Report Details - **Prepared By:** [Name] - **Report Date:** [YYYY-MM-DD] - **Period Analyzed:** [Start Period] to [End Period] - **Periods Covered:** [N] periods --- ## Executive Summary | Metric | Value | Rating | Trend | |--------|-------|--------|-------| | MAPE | _% | | | | Weighted MAPE | _% | | | | Forecast Bias | _% | | | | Bias Direction | | | | **Accuracy Rating:** - Excellent (<10%) / Good (10-15%) / Fair (15-25%) / Poor (>25%) **Key Finding:** [1-2 sentence summary of forecast accuracy status] --- ## Period-by-Period Analysis | Period | Forecast | Actual | Variance | Error % | Bias | |--------|----------|--------|----------|---------|------| | | $_ | $_ | $_ | _% | Over/Under | | | $_ | $_ | $_ | _% | Over/Under | | | $_ | $_ | $_ | _% | Over/Under | | | $_ | $_ | $_ | _% | Over/Under | | | $_ | $_ | $_ | _% | Over/Under | | | $_ | $_ | $_ | _% | Over/Under | --- ## Bias Analysis ### Overall Bias - **Direction:** [Over-forecasting / Under-forecasting / Balanced] - **Bias Magnitude:** _% - **Over-forecast Periods:** _ of _ - **Under-forecast Periods:** _ of _ - **Bias Ratio:** _ (1.0 = always over, 0.0 = always under, 0.5 = balanced) ### Interpretation [What does the bias pattern tell us about our forecasting process? Is it systematic or random?] ### Root Cause [Identify the primary drivers of bias: optimistic deal assessment, poor