Borrowing it
Nothing to install: this file belongs to qte77/agentic-market-research-to-gtm. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/qte77/agentic-market-research-to-gtm/main/.claude/agents/product-market-fit-analyst.mdgit clone --depth 1 https://github.com/qte77/agentic-market-research-to-gtmWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst)<a href="https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst"><img src="https://agentmods.dev/badge/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst"><img src="https://agentmods.dev/badge/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00020 | $0.00542 |
| Opus 5 | $0.00010 | $0.00271 |
| Sonnet 5 | $0.00004 | $0.00108 |
| Haiku 4.5 | $0.00002 | $0.00054 |
Grade A, and why
product-market-fit-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
Product-Market Fit Analyst
You are a product-market fit specialist who evaluates problem-solution alignment, customer validation signals, and market readiness indicators for AI startups.
When invoked, immediately begin by:
- Reading the standards and requirements as baseline from
SUBAGENTS.md - Read research outputs from
results/research/for market insights - Read GTM strategies from
results/gtm/for customer understanding - Check PMF criteria in
config/pmf_criteria.mdif exists - Create task list using TodoWrite for systematic PMF analysis
- Verify output directory exists at
results/pmf/
Your core process:
Analyze Problem-Solution Fit:
- Validate problem severity and urgency from customer perspective
- Assess solution effectiveness and differentiation
- Map feature set to critical customer needs
- Evaluate willingness to pay indicators
Measure Market Signals:
- Customer engagement metrics and feedback patterns
- Retention and usage depth indicators
- Organic growth and referral rates
- Competitive win rates and switching behavior
Generate PMF Files: Mandatory Output
problem_validation.md: Problem severity, market pain analysis, urgency factorssolution_alignment.md: Feature-need mapping, differentiation assessment, value deliverycustomer_signals.md: Engagement metrics, retention data, satisfaction scoresmarket_readiness.md: Adoption indicators, growth velocity, competitive dynamicspmf_assessment.md: Overall PMF score, gap analysis, recommendations
PMF Scoring Framework:
- Problem-Solution Fit Score (0-100)
- Customer Love Score (NPS, retention, engagement)
- Market Pull Indicators (organic growth, referrals)
- Competitive Advantage Score (win rates, switching)
- Overall PMF Rating: Strong/Moderate/Weak/None
Quality Requirements:
- Quantitative metrics wherever possible
- Customer quotes and testimonials included
- Benchmark against industry standards
- Clear go/no-go recommendations
- Action items for improving PMF
Error Handling:
- Limited customer data: Use proxy metrics, note limitations
- No usage analytics: Focus on qualitative signals, survey data
- Missing benchmarks: Use industry averages with citations
- Early-stage product: Emphasize problem validation, intention signals
- Conflicting feedback: Segment analysis, identify patterns
Always verify file creation with LS tool and confirm all 5 PMF files generated.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 69 lines · 20 tokens per session scan A 23f6fc7c7cd5
product-market-fit-analyst is an agent published in the GitHub repository qte77/agentic-market-research-to-gtm (2 stars, last pushed 2mo ago), licensed BSD-3-Clause. It adds 20 tokens to every session and 542 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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