What is product-market-fit-analyst?

A product-market-fit review guide for checking whether an AI startup solves an important customer problem and is ready for its market.

Use it to assess problem severity, solution fit, customer engagement, retention, referrals, competitive wins, switching behavior, and willingness to pay, then produce written analysis files.

It helps organize customer research, market evidence, and sales information into a clearer judgment about whether the product meets a real need.

How qte77/agentic-market-research-to-gtm configures Claude Code

Reuse

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.

Copy the file

curl -O https://raw.githubusercontent.com/qte77/agentic-market-research-to-gtm/main/.claude/agents/product-market-fit-analyst.md

Clone the repo

git clone --depth 1 https://github.com/qte77/agentic-market-research-to-gtm

Made for: Claude Code.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst/github.svg)](https://agentmods.dev/agents/qte77/agentic-market-research-to-gtm/product-market-fit-analyst)

Your own site

<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.

Your own site · 80×15

<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>

Per session 20 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 542 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

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 5.5	$0.00008	$0.00217
Sonnet 5.5	$0.00004	$0.00108
Haiku 4.5	$0.00002	$0.00054

Measured 2mo ago against content hash 23f6fc7c7cd5, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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.

.claude/agents/product-market-fit-analyst.md · 69 lines

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.md if 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 factors
solution_alignment.md: Feature-need mapping, differentiation assessment, value delivery
customer_signals.md: Engagement metrics, retention data, satisfaction scores
market_readiness.md: Adoption indicators, growth velocity, competitive dynamics
pmf_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.

Changes

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.

2mo ago First seen · 69 lines · 20 tokens per session scan A 23f6fc7c7cd5

Subscribe to this mod's changes

product-market-fit-analyst is an agent published in the GitHub repository qte77/agentic-market-research-to-gtm (2 stars, last pushed 4d ago), licensed BSD-3-Clause. It adds 20 tokens to every session and 542 once invoked, about $0.0001 per session on Opus 5.5. 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.

Marketing and SEO Planning agentic-workflow ai-agents gtm market-research product-validation

product-market-fit-analyst next to 6 agents from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
product-market-fit-analystqte77/agentic-market-research-to-gtm	Agent	2	4d ago	BSD-3-Clause	A	20	Claude Code
alchemistdrobins25/craft	Agent	73	2d ago	MIT	A	355	Claude Code
verifierdrobins25/craft	Agent	73	2d ago	MIT	A	178	Claude Code
config-safety-revieweralirezarezvani/claude-code-tresor	Agent	782	4mo ago	MIT	A	37	Claude Code
web3-gasalvin-reyes/better-agentic-ide	Agent	45	2d ago	MIT	A	43	Claude Code
architecture-strategisttdwhere123/do-it	Agent	28	2d ago	MIT	A	34	Claude Code, Cursor
fact-checkersynaptiai/synapti-marketplace	Agent	7	yesterday	Apache-2.0	A	39	Claude Code

agentic-market-research-to-gtm: Agent for Claude Code