Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add shawnpang/startup-founder-skills --skill review-mininggit clone --depth 1 https://github.com/shawnpang/startup-founder-skillsWrote 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/skills/shawnpang/startup-founder-skills/review-mining)<a href="https://agentmods.dev/skills/shawnpang/startup-founder-skills/review-mining"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/review-mining/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/skills/shawnpang/startup-founder-skills/review-mining"><img src="https://agentmods.dev/badge/skills/shawnpang/startup-founder-skills/review-mining.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 | $0.00064 | $0.01372 |
| Opus 5 | $0.00032 | $0.00686 |
| Sonnet 5 | $0.00013 | $0.00274 |
| Haiku 4.5 | $0.00006 | $0.00137 |
Grade A, and why
review-mining 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- review-mining — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Mining
When to Use
- Founder wants to understand real user pain points for a market or competitor product
- Founder wants voice-of-customer language to use in copy, emails, or pitch decks
- Founder wants to validate a product idea by finding recurring complaints
- Founder wants to identify gaps competitors aren't solving
- Founder wants to build a feature comparison based on what users actually care about
Context Required
- Competitor names or product category to research
- Review platforms to mine (Trustpilot, G2, Capterra, Product Hunt, App Store, Play Store, Reddit)
- What the founder is trying to learn (pain points, switching triggers, feature gaps, use cases)
- The founder's own product positioning (to identify opportunities)
Workflow
- Define research scope — identify 3-5 competitors or products to analyze and which platforms have the most relevant reviews for the category (B2B → G2/Capterra, B2C → Trustpilot/App Store, developer tools → Reddit/HN).
- Collect reviews — gather 1-3 star reviews (pain points) and 4-5 star reviews (what users love and would miss). Focus on reviews from the last 12 months for relevance. Aim for 50-100 reviews per competitor.
- Extract pain point themes — categorize complaints into recurring themes. For each theme, capture:
- The pain point in the user's own words (verbatim quotes)
- Frequency (how many reviews mention it)
- Severity (annoyance vs. deal-breaker vs. switching trigger)
- Which competitor(s) it applies to
- Extract switching triggers — find reviews where users explicitly say why they left or are considering leaving. These are gold for positioning and outreach.
- Extract "jobs to be done" — from positive reviews, identify what users are actually hiring the product to do (often different from what the product markets itself as).
- Map to opportunities — cross-reference pain points against your product's capabilities. Identify where you solve problems competitors don't.
- Generate artifacts — produce the pain point report, voice-of-customer swipe file, and positioning recommendations.
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 · 117 lines · 64 tokens per session scan A 960f30980285
review-mining is a skill published in the GitHub repository shawnpang/startup-founder-skills (321 stars, last pushed 5mo ago), licensed MIT. It adds 64 tokens to every session and 1,372 once invoked, about $0.0003 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 2026-08-30.
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