Startup Competitors

Startup Competitors

Deep competitive intelligence that goes beyond surface-level profiles. Produces actionable battle cards, pricing landscape analysis, and strategic vulnerability mapping using real web data.

How It Works

INTAKE → RESEARCH (3 sequential waves) → SYNTHESIS → BATTLE CARDS

The process is focused: understand the product, research competitors deeply across 3 dimensions, synthesize findings, and produce actionable output. Typical runtime: 15-25 minutes in Claude Code (parallel agents), 30-45 minutes in Claude.ai (sequential).

Language

Default output language is English. If the user writes in another language or explicitly requests one, use that language for all outputs instead.

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Phase 0: Resume Check

Before anything else, check if a PROGRESS.md created by this skill exists in the working directory or a project subdirectory (the skill name field says startup-competitors). If it does, read it and resume from the last incomplete phase. Tell the user: "I found progress from a previous session. You completed [phases]. Picking up from [next phase]."

If no progress file exists — or the one found belongs to a different skill — start from Phase 1.

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Phase 1: Intake

Short and focused — 1-2 rounds of questions, not an extended interview. The goal is just enough context to run targeted research.

Check for Prior startup-design Work

Before asking questions, check if a startup-design session has already been completed for this project. Look for these files in the working directory or subdirectories:

• 01-discovery/competitor-landscape.md — competitor profiles and analysis
• 01-discovery/market-analysis.md — market size, trends, regulatory
• 01-discovery/target-audience.md — customer personas, pain points
• 00-intake/brief.md — product description and context

If these files exist, read them and use the data as a head start:

• Extract the product description, target market, and known competitors from the brief
• Use the competitor list from competitor-landscape.md as the starting point for deeper analysis (startup-design profiles 5-8 competitors at surface level — this skill goes much deeper on each)
• Pull market size and trends from market-analysis.md to contextualize the competitive landscape
• Use customer pain points from target-audience.md to focus the sentiment mining on what matters most

Tell the user: "I found data from a previous startup-design session. I'll use it as a starting point and go deeper on the competitive analysis."

Skip the intake interview entirely if the startup-design files provide enough context. Go straight to research.

What to Ask (if no prior data exists)

Round 1 — The basics:

• What's your product/idea? (one sentence is fine)
• What problem does it solve and for whom?
• What market/category are you in?
• Do you know any competitors already? (names, URLs)

Round 2 — Sharpening (only if needed):

• What geography/market are you targeting?
• What's your pricing model or range?
• What do you consider your key differentiator?

Don't over-interview. If the user gives a clear description upfront, skip straight to research. The competitive analysis itself will surface what matters.

Output

Save to {project-name}/intake.md — a brief summary of the product, market, and known competitors. If built on startup-design data, note the source files used. The project name should be derived from the product/market (kebab-case, e.g., ai-email-assistant).

Create {project-name}/PROGRESS.md with: project name, skill name (startup-competitors), start date, language, research mode (Live / Knowledge-Based), and a phase checklist. Update it after each phase completes. If PROGRESS.md already exists from a previous session, resume from the last incomplete phase.

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Phase 1.5: Research Depth Assessment

After intake, assess market complexity and present the Research Depth recommendation to the user.

Reference: Read references/research-scaling.md for the complexity scoring matrix, tier definitions, wave configurations, and the user communication template.

Process

1. Score three factors from the intake: market breadth (1-3), known competitors (1-3), geographic scope (1-3) 2. Sum the scores (range 3-9) and map to a tier: Light (3-4), Standard (5-7), Deep (8-9) 3. Present the Research Depth table to the user (see research-scaling.md for the exact template) 4. Wait for user response: light, deep, or ok to accept the recommendation 5. Record the selected tier in PROGRESS.md

The selected tier determines the number of agents per wave and search rounds per agent in Phase 2. See research-scaling.md for exact wave configurations per tier.

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Phase 2: Research

Three sequential research waves, each attacking the competitive landscape from a different angle — agents within a wave run in parallel. Together they produce a 360-degree view.

Environment Detection

Check if the Agent tool is available:

• Agent tool available (Claude Code): Spawn all agents within each wave in parallel. This is faster.
• Agent tool NOT available (Claude.ai, web): Execute research sequentially, following the same templates. Same depth, just slower.

Web Search

This skill requires WebSearch for real data. If WebSearch is unavailable or denied, fall back to Knowledge-Based Mode: use training data, mark all findings with [Knowledge-Based — verify independently], and reduce confidence ratings by one level.

Reference: Read references/research-principles.md before starting any wave. It defines source quality tiers, cross-referencing rules, and how to handle data gaps.

Wave 1: Competitor Profiles + Pricing Intelligence

Reference: Read references/research-wave-1-profiles-pricing.md for agent templates.

Two agents (or two sequential blocks):

A1: Competitor Deep-Dives — Identify and profile 5-8 direct competitors plus 2-3 adjacent solutions (broader platforms, manual alternatives, tools from neighboring categories that compete for the same budget). For each: product, features, team size, funding, traction signals, strengths, weaknesses. Go beyond their marketing page — check reviews, job postings, and funding data.

A2: Pricing Intelligence — For each competitor: reverse-engineer the pricing model. Not just "it costs $49/mo" but: what's the value metric (per seat? per usage? flat?), how do tiers differentiate, what pricing psychology do they use (anchoring, decoy, charm pricing), what's the switching cost (technical, contractual, emotional). Build a tier-by-tier comparison.

Wave 2: Customer Sentiment Mining

Reference: Read references/research-wave-2-sentiment-mining.md for agent templates.

Two agents (or two sequential blocks):

B1: Review Mining — Mine G2, Capterra, TrustRadius, Product Hunt, and App Store reviews for each competitor. Extract patterns: what do people praise? What do they complain about? What features do they request? Organize by competitor and by pain theme. Include verbatim quotes.

B2: Forum & Community Mining — Mine Reddit, Indie Hackers, Hacker News, Quora, and niche communities. Find: complaints about existing tools, "what do you use for X?" threads, migration stories, workaround discussions. Build a language map — the exact words customers use to describe their problems and desires. Identify churn signals — why people leave each competitor.

Wave 3: GTM & Strategic Signals

Reference: Read references/research-wave-3-gtm-signals.md for agent templates.

Two agents (or two sequential blocks):

C1: Go-to-Market Analysis — For each competitor: primary acquisition channel, sales motion (self-serve vs. sales-led), content strategy (blog frequency, topics, quality), social presence, paid advertising signals, partnership plays. Build a channel opportunity map showing competitor saturation vs. opportunity per channel.

C2: Strategic & Growth Signals — Funding trajectory (rounds, investors, timing), hiring patterns (engineering-heavy = building, sales-heavy = scaling, support-heavy = struggling), content/SEO footprint (what keywords they rank for, where the gaps are), product roadmap signals from changelogs and public statements. Identify content pillars each competitor owns and which topics nobody covers well.

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Post-Research Checkpoint

After all three waves complete, before synthesis, briefly present what the research found to the user: how many competitors were profiled, the top customer pain themes, the most notable strategic signals (funding, hiring, GTM patterns). Ask: "Does this align with your expectations? Any competitors to add or remove before I synthesize?"

Keep it to one message — this is a quick alignment check, not a full report.

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Phase 3: Synthesis

Reference: Read references/research-synthesis.md for synthesis protocol and battle card template.

After the checkpoint, synthesize raw findings into strategic deliverables. This step creates the real value — it's not reporting, it's pattern-matching across data sources.

How to Synthesize

Synthesis is where raw competitor data becomes strategy — it's reasoning, not formatting. Before writing, think hard about how the findings interlock: a pricing gap means little until you connect it to a recurring customer complaint and a hiring signal. This is the highest-leverage thinking in the analysis, so if the model supports extended thinking, spend it here. Then work through these steps deliberately:

1. Read all raw files before writing anything 2. Connect findings across waves: pricing gaps + customer complaints + hiring signals = strategic opportunities 3. Identify contradictions between sources and explain which to trust 4. Rate confidence for each major claim (High / Medium / Low) 5. Surface strategic implications — not just facts, but what they mean 6. Aggregate all data gaps from raw files into a dedicated "Data Gaps & Research Limitations" section in the competitors-report — every analysis has blind spots, and being explicit about them prevents false confidence 7. Include adjacent solutions (broader platforms, manual alternatives, tools from neighboring categories) — customers don't just choose between direct competitors, they choose between "good enough" options from adjacent spaces

Output Files

Every deliverable file must start with a standardized header: # {Title}: {product} followed by *Skill: startup-competitors | Generated: {date}*. Every deliverable must end with Red Flags, Yellow Flags, and Sources sections.

`{project-name}/competitors-report.md` — The main deliverable:

• Executive summary (5-sentence competitive landscape overview)
• Market concentration assessment (fragmented / consolidating / dominated)
• Key findings per research dimension
• Strategic opportunities (where to compete)
• Strategic risks (where to avoid)
• Competitive moat assessment (network effects, switching costs, data moat, brand, scale)
• Data gaps & research limitations (mandatory — aggregate from all raw files)
• Red flags and yellow flags

`{project-name}/competitive-matrix.md` — Feature comparison table:

• Features as rows, competitors as columns
• Rating: strong / adequate / weak / missing
• Highlight gaps where no competitor serves well
• Your product included (or placeholder if pre-launch)

`{project-name}/pricing-landscape.md` — Dedicated pricing analysis:

• Tier-by-tier comparison across all competitors
• Value metric analysis (what each charges for and why)
• Pricing psychology breakdown (anchoring, decoy, freemium strategies)
• Price positioning map (axes: price vs. feature depth)
• Pricing whitespace — where there's room to position
• Switching cost matrix (per competitor: technical, contractual, emotional)

`{project-name}/battle-cards/{competitor-name}.md` — One per competitor:

• One-page format: who they are, their strengths, their weaknesses
• How to win against them (specific talking points)
• When they win over you (be honest)
• Customer objections and responses
• Key vulnerability to exploit
• Churn signals (why their customers leave)

Raw Data

Keep raw research files in {project-name}/raw/ for reference:

• competitor-profiles.md
• pricing-intelligence.md
• review-mining.md
• forum-mining.md
• gtm-analysis.md
• strategic-signals.md

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Phase 3.5: Research Verification

After synthesis completes and all deliverable files are written, run a verification pass.

Reference: Read references/verification-agent.md for the full verification protocol, universal checks, and skill-specific checks.

Process

1. Spawn agent V1: Verification — it reads all deliverable files and checks for: unlabeled claims, internal contradictions, confidence rating consistency, missing data gaps, missing flags, stale data, and duplicate-source false corroboration 2. V1 also runs startup-competitors-specific checks: battle card vs. report consistency, matrix vs. profiles alignment, pricing landscape vs. profiles consistency, cross-deliverable coherence 3. V1 produces {project-name}/verification-report.md 4. If Critical issues found: Pause and present issues to the user. Ask: fix first, or proceed as-is? 5. If only Warnings/Info: Show one-line summary

In Claude.ai or when Agent tool is unavailable, run the verification checks yourself in the main conversation following the same protocol.

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Honesty Protocol

Reference: Read references/honesty-protocol.md for full protocol and anti-pattern details.

Competitive intelligence is only useful if it's honest. Core rules apply (label claims, quantify, declare gaps), plus competitive-intelligence-specific additions:

1. No cheerleading. If a competitor is objectively better at something, say so. Battle cards that ignore competitor strengths are useless in real sales conversations. 2. Label claims. Use [Data], [Estimate], [Assumption], [Opinion] tags. Never present guesses as facts. 3. Quantify. "$12M ARR growing 40% YoY" not "they're growing fast." 4. Date everything. Flag data older than 12 months. 5. Declare gaps. "DATA GAP: Could not find reliable data on [X]" is always better than fabrication. 6. Surface red flags. If the competitive landscape looks brutal, say so directly. 7. Challenge confirmation bias. When research confirms what the founder already believes, probe deeper. Look for disconfirming evidence.

See references/honesty-protocol.md for the full anti-pattern table (6 entries) and detailed protocol.

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Reference Files

Read only what you need for the current phase.

Radical Honesty Protocol

This skill exists to help founders make good decisions — not to feel good. An AI that cheerleads every idea is actively harmful: it wastes the founder's time, money, and emotional energy. These principles are non-negotiable and apply to every phase.

Tell the truth, even when it's uncomfortable

• If the market is too small, say so directly. Don't soften "$12M and shrinking" into "room for a focused player."
• If the idea has a fatal flaw, name it up front. Don't bury it in a list of minor risks.
• If the founder's assumptions contradict research, flag it explicitly: "You assumed X, but the data shows Y."
• Challenge "everyone needs this" (who specifically?), "there's no competition" (there's always competition, even if it's doing nothing), and unsupported market claims.
• Never use vague positive language to avoid delivering bad news. Replace "interesting opportunity" with the specific finding.

Separate facts from opinions

• Label every major claim with its basis:
• [Data] — sourced finding with citation
• [Estimate] — calculated projection with stated assumptions
• [Assumption] — unverified belief that needs testing
• [Opinion] — your analytical judgment
• When data is missing or weak, say so.
• Never present estimates as facts.
• A confident-sounding fabrication is worse than an honest "I don't know."

Surface flags proactively

In every output file, include a Flags section at the end:

• Red Flags — Issues that could undermine the competitive analysis or the business.
• Yellow Flags — Concerns that need investigation or monitoring.

If there are no flags, write "No flags identified" — don't skip the section.

Challenge the founder's assumptions

Don't just accept what the user says at face value:

• Ask "What evidence do you have for that?" when the founder claims competitive advantages
• Push back on "we have no competitors" — there's always competition, even doing nothing
• Question "we're better at everything" — no product wins on every dimension
• When the founder dismisses a competitor, test that dismissal against data

Competitive Intelligence-Specific Rules

1. Acknowledge competitor strengths. Battle cards that ignore competitor strengths are useless in real sales conversations. If a competitor is objectively better at something, say so directly. The goal is to help the founder win deals, not feel good about their product.

2. Challenge confirmation bias. When research confirms what the founder already believes about a competitor, probe deeper. Confirmation bias is the #1 enemy of competitive intelligence. Look for disconfirming evidence.

3. Don't cherry-pick reviews. When mining customer sentiment, present the full picture — positive AND negative. Three angry Reddit posts don't mean a product is failing if it has 4.5 stars on G2 with 500 reviews. Represent sentiment proportionally.

4. Flag intelligence gaps honestly. If you couldn't find pricing, revenue, or traction data for a competitor, say so. A blank cell is better than a guess. Founders make resource allocation decisions based on competitive intelligence — fabricated data leads to bad strategy.

5. Rate competitor threat honestly. The final report must include an honest threat assessment per competitor:

• High threat — Strong product, growing fast, well-funded, overlapping target
• Medium threat — Competitive on some dimensions, gaps on others
• Low threat — Weak product, stagnant, or targeting different segment

Don't inflate threats to create urgency or deflate them to comfort the founder.

Competitive Intelligence Anti-Patterns

Ground rules

• Ground in evidence. Every competitive claim should trace to research findings.
• Make it actionable. Intelligence that can't inform strategy is worthless.
• No fabrication. If data not found, say so.

Research Principles

These principles apply to ALL research agents across all waves. Every agent must follow them.

Iterative Deep Research

Each agent performs 5-8 web searches minimum, organized in sequential rounds that drill deeper:

• Round 1: Broad overview queries
• Round 2: Drill-down into specific findings from Round 1
• Round 3: Cross-reference and validate
• Round 4: Reality check and edge cases

Do NOT stop after a single query. The first search gives you the surface — the follow-ups give you the insight.

Source Quality Tiers

Rank every finding by source reliability:

For competitive intelligence, Tier 3 sources are more valuable than usual — individual reviews and forum posts reveal the real customer experience that polished marketing hides. But don't use them for hard numbers.

Cross-Referencing

Never trust a single source for important claims. For every key finding:

• Look for 2-3 independent sources
• If sources agree: note convergence and cite all
• If sources disagree: note both figures, explain the discrepancy, and state which you trust more and why

Quantification

Vague claims are worthless. Always push for numbers:

• Bad: "They're a big player"
• Good: "$12M ARR, 2,500+ customers, Series B ($28M from Accel)"
• Bad: "Their pricing is high"
• Good: "$49/seat/mo for Pro, $99/seat/mo for Enterprise, vs. market median of $35/seat/mo"

Dating

Always note when data was published. Flag anything older than 12 months as potentially outdated. Competitor landscapes shift fast — a funding round, pivot, or acquisition can change everything in weeks.

Handling Research Failures

Sometimes WebSearch won't find what you need:

1. Try alternative queries. Rephrase, use synonyms, try different angles. At least 3 variations before declaring a gap. 2. Use proxy data. If you can't find a competitor's revenue, estimate from team size, funding, pricing × estimated customers. Show your math. 3. Declare the gap explicitly. Write: "DATA GAP: Could not find reliable data on [X]. Closest proxy: [Y]. Confidence: Low." 4. Never fabricate. An honest "unknown" is infinitely more valuable than a made-up number. 5. Suggest how to fill the gap. Point to specific reports or recommend reaching out to the competitor's customers directly.

Research Scaling Protocol

Dynamic scaling adjusts research depth based on market complexity and user preference. Evaluated after intake, before research begins.

Complexity Score

Assess three factors from the intake data:

Complexity score = sum of the three factors (range: 3-9)

Research Depth Tiers

Manual override always wins. If the user requests "light" on a score-9 market, use Light. If they request "deep" on a score-3 market, use Deep.

User Communication

After calculating the score, show this to the user:

## Research Depth

Based on your intake, I've assessed the research complexity:

| Factor           | Assessment          | Score |
|------------------|---------------------|-------|
| Market breadth   | {description}       | {1-3} |
| Known competitors| {N} identified      | {1-3} |
| Geographic scope | {description}       | {1-3} |

**Complexity score: {X}/9 — recommended depth: {Light/Standard/Deep}**

You can override this. Here's what each depth means:

| Depth        | Agents | Searches per agent | Best for                                      |
|--------------|--------|--------------------|-----------------------------------------------|
| **Light**    | {N}    | 2-3 rounds         | Quick scan, niche markets, time-sensitive decisions |
| **Standard** | {N}    | 3-4 rounds         | Most cases, balanced depth vs. speed           |
| **Deep**     | {N}    | 5-6 rounds         | Crowded markets, high-stakes decisions, thorough due diligence |

→ Type **light**, **deep**, or **ok** to accept the recommendation.

The agent counts shown should reflect the actual numbers for this skill (see Wave Configuration below).

Wave Configuration: startup-competitors

Light (3-4 score or user override)

Wave 1: Competitor Profiles + Pricing (1 agent)

• A1: Competitor Profiles & Pricing (merge A1+A2 into one agent, cover profiles and pricing together)

Wave 2: Customer Sentiment (1 agent)

• B1: Review & Community Mining (merge B1+B2 into one agent, cover reviews and forums together)

Wave 3: GTM & Strategic Signals (1 agent)

• C1: GTM & Growth Signals (merge C1+C2 into one agent, cover GTM and strategic signals together)

Total: 3 agents (vs. 6 Standard), 2-3 search rounds per agent

Standard (5-7 score, default)

No changes to current wave structure:

• Wave 1: 2 agents (A1, A2)
• Wave 2: 2 agents (B1, B2)
• Wave 3: 2 agents (C1, C2)

Total: 6 agents, 3-4 search rounds per agent

Deep (8-9 score or user override)

Wave 1: Competitor Profiles + Pricing (3 agents)

• A1: Competitor Deep-Dives (unchanged)
• A2: Pricing Intelligence (unchanged)
• A3: Adjacent Competitor Profiles (NEW: profile 3-5 adjacent/emerging competitors not in the direct set, including recent launches and stealth startups)

Wave 2: Customer Sentiment (3 agents)

• B1: Review Mining (unchanged)
• B2: Forum & Community Mining (unchanged)
• B3: Social Media Sentiment (NEW: mine Twitter/X, LinkedIn, and YouTube for competitor mentions, sentiment patterns, and influencer opinions)

Wave 3: GTM & Strategic Signals (3 agents)

• C1: Go-to-Market Analysis (unchanged)
• C2: Strategic & Growth Signals (unchanged)
• C3: Tech Stack & Product Analysis (NEW: analyze competitors' technology choices, API ecosystems, integration depth, and technical moats)

Total: 9 agents, 5-6 search rounds per agent

PROGRESS.md

Record the selected tier in PROGRESS.md:

- **Research Depth:** {Light/Standard/Deep} (score: {X}/9, {override: user request / auto})

Synthesis & Battle Cards

After ALL waves complete (6 agents), synthesize the raw findings into polished deliverables. This step creates the real analytical value — it connects dots across data sources to surface opportunities that individual research pieces can't reveal on their own.

Synthesis Protocol

Before Writing

1. Read ALL raw files in {project-name}/raw/ before writing anything 2. Look for patterns across sources — what themes repeat? 3. Identify contradictions between sources and explain which you trust more 4. Connect the dots: pricing gaps + customer complaints + hiring signals = opportunities

Cross-Wave Connections to Look For

These are the high-value insights that come from combining data:

• Complaint + Pricing = Opportunity: Customers complain about a feature AND the competitor charges a premium for it → undercut with better value
• Hiring + Product Direction = Threat: Competitor hiring AI engineers + recent AI mentions in changelog → they're coming for that space
• Churn Signal + Switching Cost = Wedge: People want to leave but data portability is hard → build easy migration as a differentiator
• Content Gap + Search Volume = Quick Win: Nobody ranks for a high-volume term → own it early
• Review Pattern + Missing Feature = MVP Feature: Multiple competitors lack something customers want → build it first
• Funding + Team Size = Reality Check: Well-funded competitor with 100+ engineers → don't compete on features, compete on focus

Confidence Rating

Rate every major claim:

• High: Multiple Tier 1/2 sources agree, recent data
• Medium: Some evidence but gaps, or sources partially disagree
• Low: Limited data, mostly inferred, or data older than 12 months

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Output File: competitors-report.md

Structure:

# Competitive Intelligence Report: {market/product}
*Skill: startup-competitors | Generated: {date}*

## Executive Summary
{5 sentences max: market concentration, key finding, biggest opportunity, biggest risk, overall assessment}

## Market Concentration
- **Structure:** fragmented / consolidating / dominated
- **Number of active players:** {count}
- **Funding concentration:** {is money flowing in or drying up?}
- **Entry barriers:** low / medium / high — {why}

## Key Players at a Glance
| Competitor | Stage | Funding | Strength | Weakness | Threat |
|-----------|-------|---------|----------|----------|--------|
| ... | ... | ... | ... | ... | H/M/L |

## Adjacent Solutions & Substitutes
{Broader platforms, manual alternatives, and tools from neighboring categories that compete for the same budget or job. Include: what job they solve, why buyers consider them, and how they compare to direct competitors.}

## Strategic Opportunities
For each opportunity:
### Opportunity: {name}
- **What:** {description}
- **Evidence:** {data points from research}
- **Confidence:** High / Medium / Low
- **How to exploit:** {specific recommendation}

## Strategic Risks
For each risk:
### Risk: {name}
- **What:** {description}
- **Evidence:** {data points}
- **Severity:** High / Medium / Low
- **Mitigation:** {how to protect against it}

## Competitive Moat Assessment
Evaluate the market on 5 moat dimensions:
| Moat Type | Present in Market? | Who Has It | Strength |
|----------|-------------------|-----------|----------|
| Network effects | yes / no | {who} | strong / weak |
| Switching costs | yes / no | {who} | strong / weak |
| Data moat | yes / no | {who} | strong / weak |
| Brand/trust | yes / no | {who} | strong / weak |
| Economies of scale | yes / no | {who} | strong / weak |

{Paragraph explaining what this means for a new entrant}

## Data Gaps & Research Limitations
Aggregate all data gaps from raw research files into a single section. For each gap:
- What data is missing
- Why it matters for decision-making
- How to fill it (specific actions the founder can take)

This section is mandatory — every competitive analysis has blind spots. Being explicit about them builds trust and prevents false confidence.

## Red Flags
- {flag 1 — things that should worry the founder}
- {flag 2}

## Yellow Flags
- {flag 1 — things to watch}
- {flag 2}

---

Output File: competitive-matrix.md

# Competitive Feature Matrix: {market}

## Feature Comparison
| Feature | {Your Product} | {Comp 1} | {Comp 2} | {Comp 3} | {Comp 4} |
|---------|---------------|----------|----------|----------|----------|
| {feature 1} | {rating} | {rating} | ... | ... | ... |
| {feature 2} | ... | ... | ... | ... | ... |

Rating scale: Strong / Adequate / Weak / Missing / Unknown

## Gap Analysis
Features where no competitor excels (all Weak or Missing):
- {gap 1} — {opportunity implication}
- {gap 2} — {opportunity implication}

## Differentiation Opportunities
Based on the matrix, the clearest paths to differentiation:
1. {opportunity — what to build and why}
2. {opportunity}
3. {opportunity}

---

Output File: pricing-landscape.md

# Pricing Landscape: {market}

## Market Pricing Overview
- **Dominant value metric:** {what most charge for}
- **Price range:** {lowest — highest for comparable tiers}
- **Median price point:** {for standard tier}
- **Free tier prevalence:** {X of Y competitors offer free}

## Tier-by-Tier Comparison
| | {Comp 1} | {Comp 2} | {Comp 3} | {Comp 4} |
|---|----------|----------|----------|----------|
| Free tier | {what's included} | ... | ... | ... |
| Entry tier | ${price} — {limits} | ... | ... | ... |
| Mid tier | ${price} — {limits} | ... | ... | ... |
| Top tier | ${price} — {limits} | ... | ... | ... |
| Enterprise | {custom?} | ... | ... | ... |

## Value Metric Analysis
| Competitor | Value Metric | Why It Works/Doesn't | Impact on Scaling |
|-----------|-------------|---------------------|-------------------|
| ... | per seat | {analysis} | {how costs grow} |

## Pricing Psychology in Use
| Tactic | Used By | How |
|--------|---------|-----|
| Anchoring | {who} | {details} |
| Decoy tier | {who} | {details} |
| Charm pricing | {who} | {details} |
| Annual lock-in | {who} | {discount %} |

## Switching Cost Matrix
| Competitor | Technical Cost | Contractual Cost | Emotional Cost | Overall |
|-----------|---------------|-----------------|----------------|---------|
| ... | H/M/L | H/M/L | H/M/L | H/M/L |

## Pricing Whitespace
{Where there's room to position on price — underserved segments, untried models, price points nobody occupies}

## Recommendations
- **If competing on price:** {strategy}
- **If competing on value:** {strategy}
- **If competing on model:** {alternative pricing approach that no competitor uses}

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Post-Synthesis Verification

After writing all deliverables and battle cards, run the Verification Agent protocol. See references/verification-agent.md for the full process. The verification step checks all deliverables for unlabeled claims, internal contradictions, confidence rating consistency, and startup-competitors-specific coherence (battle card vs. report consistency, matrix vs. profiles alignment, pricing landscape vs. profiles consistency, cross-deliverable opportunity/risk traceability).

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Output File: battle-cards/{competitor-name}.md

One battle card per competitor. Keep each to ONE page — these are reference tools for quick use, not deep research docs.

# Battle Card: {Competitor Name}
*Last updated: {date}*

## At a Glance
- **What they do:** {one sentence}
- **Founded:** {year} | **Funding:** {total} | **Team:** ~{size}
- **Price:** {range} | **Model:** {value metric}
- **Best for:** {their ideal customer}

## Their Strengths (be honest)
- {strength 1 — with evidence}
- {strength 2}
- {strength 3}

## Their Weaknesses (your openings)
- {weakness 1 — with evidence from reviews/forums}
- {weakness 2}
- {weakness 3}

## How to Win Against Them
Specific talking points when a prospect is evaluating both:
- **When they say "{objection},"** respond: {counter with evidence}
- **When they say "{objection},"** respond: {counter}
- **Lead with:** {your strongest differentiator vs. this specific competitor}

## When They Win Over You
Be honest about when the competitor is the better choice:
- {scenario 1 — e.g., "Enterprise teams needing SSO and audit logs"}
- {scenario 2}

## Their Customers' Top Complaint
"{verbatim quote from review}" — {source}
This matters because: {strategic implication}

## Key Vulnerability
{The single biggest weakness you can exploit — with evidence}

## Churn Signals
Why their customers leave:
- {reason 1} — frequency: common / occasional
- {reason 2}

## Watch For
{What this competitor is likely to do next based on strategic signals}

Wave 1: Competitor Profiles + Pricing Intelligence

Read research-principles.md first.

---

Agent A1: Competitor Deep-Dives

Research task: Deep analysis of direct competitors for {product description}
Context: {product summary from intake}
Known competitors: {list from intake, if any}

RESEARCH PROTOCOL — identify and profile 5-8 direct competitors:

ROUND 1 — Identify competitors (4-5 searches):
- "{problem} software/app/tool {current year}"
- "best {product category} tools {current year}"
- "{known competitor 1} vs alternatives"
- "G2 {product category} grid"
- "{product category} Product Hunt"
- "top {product category} startups"
- "{problem} solutions" OR "how do {customer type} currently handle {problem}"
  (this catches adjacent solutions — inventory tools, platforms with overlapping features, manual/offline alternatives that compete for the same budget)

ROUND 2 — Deep-dive each competitor (2-3 searches per competitor):
- Visit their website: capture positioning, features, messaging, social proof
- "{competitor name} review G2 Capterra"
- "{competitor name} crunchbase funding"
- "{competitor name} linkedin employees" (team size signals)
- "{competitor name} changelog" or "{competitor name} updates {current year}"

ROUND 3 — Competitive dynamics (2-3 searches):
- "{product category} market share"
- "{competitor 1} vs {competitor 2}" comparison articles
- "{product category} landscape {current year}"

For EACH competitor, build a complete profile:

## {Competitor Name}
- **Website:** {url}
- **Founded:** {year}
- **Headquarters:** {location}
- **Team size:** {estimate from LinkedIn/Crunchbase}
- **Funding:** {total raised, last round, lead investors}
- **Stage:** bootstrapped / seed / Series A / Series B+ / public
- **Estimated revenue:** {if available, or proxy estimate}

### Product
- **Tagline:** {their actual tagline/positioning statement}
- **Core offering:** {what they sell in one sentence}
- **Key features:** {top 5-8 features}
- **Tech stack signals:** {any public info}
- **Integrations:** {key integrations}
- **Platform:** {web / mobile / desktop / API}

### Market Position
- **Target customer:** {who they serve — be specific}
- **Positioning:** {how they describe themselves}
- **Key differentiator:** {what they claim makes them unique}
- **Social proof:** {notable customers, case studies, logos}

### Traction Signals
- **G2/Capterra:** {review count and average rating}
- **Product Hunt:** {launch date, upvotes}
- **Social media:** {follower counts, engagement level}
- **Job postings:** {number and type}
- **Web traffic signals:** {if available from Similarweb/press mentions}
- **Notable customers:** {logos or case studies}

### Strengths
- {strength 1 — based on evidence, not speculation}
- {strength 2}
- {strength 3}

### Weaknesses
- {weakness 1 — based on reviews, gaps, complaints}
- {weakness 2}
- {weakness 3}

### Threat Level: Low / Medium / High
- {why — with evidence}

---

After all profiles:

## Landscape Summary
- **Total competitors identified:** {number profiled + number found but not profiled}
- **Market concentration:** fragmented / consolidating / dominated by 1-2 players
- **Average funding level:** {across profiled competitors}
- **Common positioning themes:** {what most competitors emphasize}
- **Gaps in the market:** {what no competitor does well}

## Adjacent Solutions
Products that aren't direct competitors but compete for the same budget or solve an overlapping problem. These matter because customers often choose "good enough" adjacent tools over a dedicated solution.
- {adjacent solution 1} — what it does, how it overlaps, why someone might pick it instead
- {adjacent solution 2}
Include: broader platforms with partial feature overlap, manual/offline alternatives, tools from adjacent categories that could expand into this space.

## Data Gaps
- [What you couldn't find and why it matters]

Save to: {project-name}/raw/competitor-profiles.md

---

Agent A2: Pricing Intelligence

Research task: Pricing reverse-engineering for competitors in {product category}
Context: {product summary from intake}
Competitors to analyze: {list from A1 if available, otherwise discover during research}

RESEARCH PROTOCOL:

ROUND 1 — Capture pricing pages (1 search per competitor):
- Visit each competitor's pricing page directly
- Screenshot or capture: tiers, prices, feature lists, CTAs
- Note: annual vs monthly pricing, currency, any free tier

ROUND 2 — Deep pricing analysis (2-3 searches):
- "{competitor name} pricing" (for third-party breakdowns)
- "{competitor name} pricing changes" (for pricing history)
- "{product category} pricing comparison {current year}"
- "{competitor name} enterprise pricing" (often hidden)

ROUND 3 — Value metric analysis (1-2 searches):
- "{product category} pricing model" (per-seat vs usage vs flat)
- "how much does {competitor name} cost" (real user discussions)

For EACH competitor, analyze:

## {Competitor Name} — Pricing Breakdown

### Pricing Model
- **Value metric:** {what they charge for — per seat / per usage / flat / hybrid}
- **Why this metric:** {how it aligns with value delivered}
- **How it scales:** {does price grow linearly with usage? Are there volume discounts?}

### Tier Structure
| | {Tier 1} | {Tier 2} | {Tier 3} | {Enterprise} |
|---|----------|----------|----------|-------------|
| Price (monthly) | | | | |
| Price (annual) | | | | |
| Annual discount | | | | |
| {Key feature 1} | | | | |
| {Key feature 2} | | | | |
| {Key feature 3} | | | | |
| {Key limit 1} | | | | |
| Target persona | | | | |

### Pricing Psychology
- **Anchoring:** {do they use a high-price tier to make mid-tier attractive?}
- **Decoy effect:** {is there a tier designed to push people to a specific plan?}
- **Charm pricing:** {$49 vs $50? $99 vs $100?}
- **Social proof on pricing:** {which tier is "most popular"?}
- **Free tier strategy:** {what's free and what's gated?}
- **Annual lock-in:** {discount size, refund policy}

### Switching Costs
- **Technical:** {data export? API migration? Integration rewiring?}
- **Contractual:** {annual contracts? Cancellation penalties?}
- **Emotional:** {brand loyalty? Learning curve for alternatives?}
- **Data portability:** {can you export your data easily?}

---

After all competitors:

## Pricing Landscape Summary
- **Dominant value metric:** {what most charge for}
- **Price range:** {lowest to highest for comparable tiers}
- **Median price point:** {for the most common tier}
- **Free tier prevalence:** {how many offer free plans}
- **Annual discount range:** {typical discounts}
- **Pricing whitespace:** {where there's room to position — underserved price points or models}
- **Switching cost patterns:** {are switching costs high or low in this market?}

## Data Gaps
- [Competitors with hidden/custom pricing]
- [Enterprise pricing that couldn't be found]

Save to: {project-name}/raw/pricing-intelligence.md

Wave 2: Customer Sentiment Mining

Read research-principles.md first.

---

Agent B1: Review Mining

Research task: Mine customer reviews for competitors in {product category}
Context: {product summary from intake}
Competitors to analyze: {list from Wave A}

Review mining reveals what customers actually experience — not what competitor marketing promises. The gap between promise and reality is where opportunities live.

HANDLING SCARCE REVIEWS:
Some markets (especially B2B, niche, or emerging categories) have very few formal reviews on G2/Capterra. When this happens:
1. Expand to additional platforms: App Store, Play Store, Trustpilot, Google Maps reviews (for physical products/services), industry-specific review sites
2. Search for case studies and testimonials on competitor websites — extract the language used
3. Search for "{competitor name} experience" or "{competitor name} honest review" on blogs and YouTube
4. Lean more heavily on forum mining (Wave B2) for unfiltered opinions
5. Declare the scarcity explicitly in Data Gaps — "only X reviews found across platforms" is valuable information itself (it signals market immaturity or low switching)
Always include at least 2-3 verbatim quotes per competitor, even if they come from blog posts, tweets, or forum comments rather than formal review platforms.

RESEARCH PROTOCOL:

ROUND 1 — Aggregate review platforms (2 searches per competitor):
- "{competitor name} reviews G2"
- "{competitor name} reviews Capterra" OR "Trustradius"
- "{competitor name} reviews Product Hunt"
- "{competitor name} app store reviews" (if mobile product)

ROUND 2 — Negative review deep-dive (1-2 searches per competitor):
- "{competitor name} complaints"
- "{competitor name} problems reddit"
- "{competitor name} worst things"

ROUND 3 — Feature request patterns (1-2 searches):
- "{competitor name} feature request"
- "{competitor name} missing features"
- "{product category} wishlist"

For EACH competitor, extract:

## {Competitor Name} — Review Analysis

### Review Volume & Ratings
- **G2:** {count} reviews, {avg} stars
- **Capterra:** {count} reviews, {avg} stars
- **TrustRadius:** {count} reviews, {avg} stars
- **Product Hunt:** {upvotes}, {comment sentiment}
- **App Store / Play Store:** {if applicable}

### What People Love (top 3-5 themes)
For each theme:
- **Theme:** {e.g., "Easy onboarding"}
- **Frequency:** mentioned in ~{X}% of positive reviews
- **Verbatim quotes:**
  - "{exact quote}" — {source, date}
  - "{exact quote}" — {source, date}

### What People Hate (top 3-5 themes)
For each theme:
- **Theme:** {e.g., "Pricing feels unfair at scale"}
- **Frequency:** mentioned in ~{X}% of negative reviews
- **Severity:** annoyance / blocker / deal-breaker
- **Verbatim quotes:**
  - "{exact quote}" — {source, date}
  - "{exact quote}" — {source, date}

### Most Requested Features
- {feature 1} — mentioned {X} times
- {feature 2} — mentioned {X} times
- {feature 3} — mentioned {X} times

### Churn Signals
Reasons people leave this competitor:
- {reason 1 — with evidence}
- {reason 2 — with evidence}
- {reason 3 — with evidence}

---

After all competitors:

## Cross-Competitor Pain Patterns
| Pain Theme | {Comp 1} | {Comp 2} | {Comp 3} | {Comp 4} | Opportunity |
|-----------|----------|----------|----------|----------|-------------|
| {pain 1} | severity | severity | severity | severity | {implication} |
| {pain 2} | ... | ... | ... | ... | ... |

Pains shared across multiple competitors = structural market problems = biggest opportunities.

## Data Gaps
- [Competitors with few reviews]
- [Platforms not checked]

Save to: {project-name}/raw/review-mining.md

---

Agent B2: Forum & Community Mining

Research task: Mine forums and communities for customer voice about {product category}
Context: {product summary from intake}
Competitors: {list from Wave A}

Forum mining captures unfiltered opinions that people won't write in formal reviews. It also reveals the exact language customers use — gold for positioning and copywriting.

RESEARCH PROTOCOL:

ROUND 1 — Reddit (3-4 searches):
- "site:reddit.com {product category} recommendations"
- "site:reddit.com {competitor name} alternative"
- "site:reddit.com {problem statement} tool"
- "site:reddit.com switching from {competitor name}"

ROUND 2 — Indie communities (2-3 searches):
- "site:indiehackers.com {product category}"
- "site:news.ycombinator.com {product category}"
- "{product category} forum discussion"

ROUND 3 — Q&A and niche (2 searches):
- "site:quora.com best {product category}"
- "{product category} community Slack Discord"

ROUND 4 — Migration stories (1-2 searches):
- "switched from {competitor name} to"
- "migrating from {competitor name}"
- "why I left {competitor name}"

OUTPUT FORMAT:

## Forum & Community Findings

### Discussion Themes
For each major theme found:
- **Theme:** {what people are discussing}
- **Volume:** {approximate number of threads/comments}
- **Sentiment:** positive / negative / mixed
- **Key threads:**
  - [{thread title}]({url}) — {key takeaway}

### Language Map
The exact words customers use — organized for reuse in positioning and copy:

**To describe the problem:**
- "{exact phrase}" — used in {X} threads
- "{exact phrase}" — used in {X} threads

**To describe desired solution:**
- "{exact phrase}"
- "{exact phrase}"

**To describe frustrations with competitors:**
- "{exact phrase}" — about {competitor}
- "{exact phrase}" — about {competitor}

**To describe switching triggers:**
- "{exact phrase}"
- "{exact phrase}"

### "What do you use for X?" Threads
| Thread | Top Recommended | Runner Up | Common Criteria |
|--------|----------------|-----------|-----------------|
| {title} | {tool} ({why}) | {tool} | {what people care about} |

### Migration Stories
For each migration story found:
- **From:** {competitor} → **To:** {competitor}
- **Why they switched:** {reason}
- **What they gained:** {benefit}
- **What they lost:** {trade-off}
- **Would they switch again?** {yes/no and why}

### Churn Signal Summary
Aggregated reasons people leave competitors:
| Reason | Competitors Affected | Frequency | Severity |
|--------|---------------------|-----------|----------|
| {reason} | {which ones} | common / occasional | high / medium |

## Data Gaps
- [Communities not found or not active for this category]
- [Competitors with no forum presence]

Save to: {project-name}/raw/forum-mining.md

Wave 3: GTM & Strategic Signals

Read research-principles.md first.

---

Agent C1: Go-to-Market Analysis

Research task: How competitors in {product category} acquire and retain customers
Context: {product summary from intake}
Competitors: {list from Wave A}

Understanding how competitors grow reveals where the market attention flows — and where there are underexploited channels.

RESEARCH PROTOCOL:

ROUND 1 — Marketing presence (2-3 searches per top competitor):
- Check competitor websites: blog, resources, webinars, case studies
- "{competitor name} marketing strategy"
- "{competitor name} blog" (assess frequency, topics, quality)

ROUND 2 — Advertising signals (2-3 searches):
- "{competitor name} ads" OR check Google Ads transparency center
- "{competitor name} Facebook Ad Library"
- "{product category} sponsored content"

ROUND 3 — Sales motion (2 searches):
- "{competitor name} sales team linkedin"
- Analyze each competitor's CTA: self-serve signup vs demo vs contact sales

ROUND 4 — Partnerships and community (1-2 searches):
- "{competitor name} integrations"
- "{competitor name} partner program"
- "{competitor name} community" OR "{competitor name} forum"

For EACH competitor:

## {Competitor Name} — GTM Breakdown

### Acquisition Model
- **Primary channel:** {SEO / paid ads / outbound sales / partnerships / community / PLG}
- **Secondary channel:** {if identifiable}
- **Sales motion:** {self-serve / sales-assisted / enterprise sales}
- **Signup friction:** {free trial / freemium / demo required / contact sales}

### Content & SEO
- **Blog frequency:** {posts per month, estimated}
- **Content quality:** {surface-level / solid / exceptional}
- **Content topics:** {what they write about}
- **SEO strength:** {do they rank for category terms?}
- **Resource library:** {ebooks, webinars, templates — what kind?}

### Social & Community
- **LinkedIn:** {followers, posting frequency, engagement}
- **Twitter/X:** {followers, posting frequency, engagement}
- **Other platforms:** {YouTube, TikTok, Instagram — if relevant}
- **Community:** {own community? Active? Size?}

### Paid Advertising
- **Google Ads:** {visible? what keywords?}
- **Social ads:** {Facebook, LinkedIn, Twitter — visible?}
- **Messaging in ads:** {what do they emphasize?}

### Partnerships
- **Integration partners:** {key integrations}
- **Channel partners:** {resellers, agencies}
- **Co-marketing:** {joint content, events}

---

After all competitors:

## Channel Opportunity Map
| Channel | Competitor Saturation | Opportunity Level | Est. Effort | Notes |
|---------|----------------------|-------------------|-------------|-------|
| SEO/Content | High / Medium / Low | High / Medium / Low | {S/M/L} | {why} |
| Paid Search | ... | ... | ... | ... |
| Social/Community | ... | ... | ... | ... |
| Outbound Sales | ... | ... | ... | ... |
| Partnerships | ... | ... | ... | ... |
| Product-Led Growth | ... | ... | ... | ... |

## Key Takeaways
- What's working for competitors (proven channels)
- What channels are underexploited (opportunity)
- Where to win with limited budget (quick wins)

## Data Gaps
- [Competitors with no visible marketing]
- [Channels you couldn't assess]

Save to: {project-name}/raw/gtm-analysis.md

---

Agent C2: Strategic & Growth Signals

Research task: Strategic signals and growth trajectory for competitors in {product category}
Context: {product summary from intake}
Competitors: {list from Wave A}

Hiring patterns, funding trajectory, and content investments reveal where competitors are heading — not just where they are today.

RESEARCH PROTOCOL:

ROUND 1 — Funding trajectory (1-2 searches per competitor):
- "{competitor name} crunchbase"
- "{competitor name} funding {current year}"
- "{competitor name} acquisition" OR "{competitor name} acquired"

ROUND 2 — Hiring signals (1-2 searches per competitor):
- "{competitor name} careers" OR "{competitor name} jobs"
- "{competitor name} hiring linkedin"
- Note: engineering-heavy = building new features; sales-heavy = scaling; support-heavy = struggling with growth

ROUND 3 — Content & SEO footprint (2-3 searches):
- "site:{competitor domain} blog" (content volume)
- "{product category} {key topic}" (who ranks for important terms?)
- "{product category} comparison" OR "{product category} vs" (who owns comparison content?)

ROUND 4 — Product direction signals (1-2 searches):
- "{competitor name} changelog"
- "{competitor name} roadmap"
- "{competitor name} new features {current year}"
- "{competitor name} AI" (if relevant to the category)

For EACH competitor:

## {Competitor Name} — Strategic Signals

### Funding Trajectory
- **Total raised:** {amount}
- **Last round:** {type, amount, date, lead investor}
- **Funding velocity:** {time between rounds}
- **Signal:** {growing fast / steady / slowing / bootstrapped by choice}
- **Potential exit signals:** {acquisition rumors, IPO filings}

### Hiring Patterns
- **Total job openings:** {count}
- **Engineering roles:** {count} — signal: {building new product / scaling infra / maintaining}
- **Sales/Marketing roles:** {count} — signal: {scaling acquisition / entering new market}
- **Support/CS roles:** {count} — signal: {growing customer base / high churn needing management}
- **Key hires:** {notable recent senior hires and what they signal}
- **Location signals:** {hiring in new markets?}

### SEO & Content Footprint
- **Keywords they rank for:** {top category terms}
- **Content pillars:** {what topics they own}
- **Content gaps:** {category topics they don't cover}
- **Blog authority:** {estimated domain authority / content maturity}

### Product Direction
- **Recent major features:** {what they shipped in last 6 months}
- **Roadmap signals:** {where they're heading based on public info}
- **Tech bets:** {AI, mobile, API, integrations — what are they investing in?}
- **Platform plays:** {trying to become a platform? Building ecosystem?}

---

After all competitors:

## Strategic Landscape Summary

### Where the Market Is Heading
- {trend 1 based on collective competitor signals}
- {trend 2}
- {trend 3}

### Competitor Trajectory Map
| Competitor | Stage | Trajectory | Investing In | Biggest Risk |
|-----------|-------|-----------|-------------|-------------|
| {name} | {stage} | growing / stable / declining | {area} | {risk} |

### Content/SEO Gap Analysis
| Topic/Keyword | {Comp 1} | {Comp 2} | {Comp 3} | Opportunity |
|--------------|----------|----------|----------|-------------|
| {keyword} | ranks / absent | ... | ... | {who could win this} |

### Underexploited Content Pillars
Topics that matter to the market but no competitor covers well:
- {topic 1} — why it matters, why it's uncovered
- {topic 2}

## Data Gaps
- [Competitors with no public hiring data]
- [Private companies with no funding info]

Save to: {project-name}/raw/strategic-signals.md

Verification Agent Protocol

After synthesis completes, spawn a Verification Agent (V1) that audits all deliverables for consistency, accuracy, and completeness. This step catches issues that individual agents and synthesis can miss.

When to Run

• After synthesis is complete and all deliverable files are written
• Before presenting the final output to the user
• Uses one agent: V1: Verification

Agent Task

The V1 agent reads ALL deliverable files (not raw files) and checks them against the rules below. It produces a verification-report.md in the project directory.

Universal Checks

These apply to every skill in the startup plugin:

1. Claims Without Source

Every quantitative claim must have a data label: [Data], [Estimate], [Assumption], or [Opinion]. Flag any number, percentage, or factual assertion without a label.

2. Internal Contradictions

Cross-check numbers and statements across deliverable files. Flag when:

• The same metric appears with different values in two files
• A claim in one file contradicts a claim in another
• Confidence ratings disagree (e.g., "High confidence" in one file, different data in another suggests Medium)

3. Confidence Rating Consistency

Verify that confidence ratings match the evidence:

• A claim with only one Tier 3 source cannot be rated High
• A claim with multiple Tier 1 sources should not be rated Low
• Every major section must have a confidence rating

4. Data Gaps Declared

Every deliverable must have a Data Gaps section. Flag:

• Files missing the Data Gaps section entirely
• Sections where data is clearly thin but no gap is declared
• Gaps mentioned in raw files that didn't make it into the synthesized deliverables

5. Flags Present

Every deliverable must end with Red Flags and Yellow Flags sections. Flag:

• Files missing these sections
• Files with "No flags identified" where the content clearly contains risks

6. Stale Data

Flag any data point older than 18 months that isn't marked as potentially outdated.

7. Duplicate Sources

Flag when the same source is used as "independent corroboration" in multiple places. Two claims both citing the same blog post don't have independent verification.

Skill-Specific Checks: startup-competitors

In addition to the universal checks above, verify:

Battle Card vs. Report Consistency

• Every competitor in the battle cards must appear in the competitors-report.md Key Players table
• Strengths/weaknesses in battle cards must not contradict the competitor profiles in the report
• Threat levels must be consistent between battle cards and the report

Matrix vs. Profiles Alignment

• Every competitor in competitive-matrix.md must have a profile in competitors-report.md
• Feature ratings (Strong/Adequate/Weak/Missing) in the matrix must be supported by evidence in the report or battle cards
• Gap analysis items must connect to actual findings, not assumptions

Pricing Landscape vs. Profiles Consistency

• Pricing data in pricing-landscape.md must match pricing mentioned in battle cards
• Value metrics must be consistent across the pricing landscape and competitor profiles
• Switching cost assessments must align between pricing landscape and battle cards

Cross-Deliverable Coherence

• Strategic opportunities in the report must be supported by evidence from at least two deliverables (e.g., pricing gap + customer complaint)
• Strategic risks must be traceable to specific competitor data
• Moat assessment must reference specific competitors and evidence

Output: verification-report.md

# Verification Report: {project-name}
*Generated: {date}*

## Summary
- **Critical issues:** {count}
- **Warnings:** {count}
- **Info:** {count}

## Critical Issues
Issues that could mislead decision-making. The process pauses here for user review.

### {Issue title}
- **File(s):** {affected files}
- **Section:** {section name}
- **Problem:** {description}
- **Suggested fix:** {how to resolve}

## Warnings
Issues that reduce quality but don't block decisions.

### {Issue title}
- **File(s):** {affected files}
- **Problem:** {description}
- **Suggested fix:** {how to resolve}

## Info
Minor improvements and observations.

- {observation}
- {observation}

## Verification Checklist
- [ ] All quantitative claims labeled
- [ ] No internal contradictions found
- [ ] Confidence ratings consistent with evidence
- [ ] Data gaps declared in all deliverables
- [ ] Red/Yellow flags present in all deliverables
- [ ] No stale data unmarked
- [ ] No duplicate-source false corroboration
- [ ] Battle cards consistent with report (skill-specific)
- [ ] Matrix aligned with profiles (skill-specific)
- [ ] Pricing landscape consistent with profiles (skill-specific)
- [ ] Opportunities backed by multi-source evidence (skill-specific)

Flow Control

• If Critical issues > 0: Pause. Show the user: "Verification found {N} critical issues that could affect decision-making." List them. Ask: "Should I fix these before continuing, or proceed as-is?"
• If only Warnings/Info: Show a one-line summary: "Verification complete: {N} warnings, {N} info items. See verification-report.md for details." Continue.