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Agent skill

Review Mining

by shawnpang in shawnpang/startup-founder-skills

When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores.

MITAuto-check passedSales & Support

Install Review Mining

skills CLI
$ npx skills add shawnpang/startup-founder-skills --skill review-mining -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install shawnpang/startup-founder-skills review-mining --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/shawnpang/startup-founder-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/review-mining .claude/skills/review-mining && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
review-mining
GitHub stars
343
Token cost
~1.5k tokens
SKILL.md length
610 words
Files
1
Skills in repo
50
Repo updated
First seen
Licence
MIT

At a glance

When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores.

  • Works in 7 steps: Define research scope — identify 3-5… → Collect reviews — gather 1-3 star… → Extract pain point themes — categorize… → …
  • Wants to research customer pain points
  • SKILL.md covers When to Use, Context Required, Workflow and Output Format, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Review Mining is an agent skill from shawnpang/startup-founder-skills. When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores. Also use when the user mentions "what are users saying", "competitor reviews", "pain points", or "voice of customer research".

Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Sales & Support, covering Customer feedback analysis, App store release and Market research. The repository describes itself as: AI agent skills for tech startup founders — fundraising, sales, product, recruiting, engineering, legal, ops, and growth. Works with Claude Code, Cursor, Codex, and any Agent… The licence is MIT.

When your agent uses it

  • Wants to research customer pain points
  • Sentiment using review platforms like Trustpilot
  • The user mentions what are users saying
  • Competitor reviews

Example prompts

  • “what are users saying”
  • “competitor reviews”
  • “pain points”
  • “/review-mining”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Define research scope — identify 3-5 competitors or products to analyze and which platforms have the most relevant reviews for the…
  2. 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…
  3. Extract pain point themes — categorize complaints into recurring themes. For each theme, capture
  4. Extract switching triggers — find reviews where users explicitly say why they left or are considering leaving. These are gold for…
  5. Extract "jobs to be done" — from positive reviews, identify what users are actually hiring the product to do (often different from what…
  6. Map to opportunities — cross-reference pain points against your product's capabilities. Identify where you solve problems competitors don't.
  7. Generate artifacts — produce the pain point report, voice-of-customer swipe file, and positioning recommendations.

What it can do on your machine

Read from SKILL.md and the folder at commit 4ad31b4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Review Mining loads about 1.5k tokens when it runs. Until then it costs about 72 tokens; SKILL.md has 610 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~72
When it runs · the whole SKILL.md, loaded when a task matches
~1.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from shawnpang/startup-founder-skills at commit 4ad31b4, republished under its MIT licence (© shawnpang). 610 words, ~1,531 tokens.

Download SKILL.mdSave it as .claude/skills/review-mining/SKILL.md (or your agent's skills folder).
name
review-mining
description
When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores. Also use when the user mentions "what are users saying", "competitor reviews", "pain points", or "voice of customer research".
related
competitive-analysis, user-research-synthesis, feedback-synthesis, cold-outreach
reads
startup-context

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

  1. 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).
  2. 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.
  3. 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
  4. Extract switching triggers — find reviews where users explicitly say why they left or are considering leaving. These are gold for positioning and outreach.
  5. 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).
  6. Map to opportunities — cross-reference pain points against your product's capabilities. Identify where you solve problems competitors don't.
  7. Generate artifacts — produce the pain point report, voice-of-customer swipe file, and positioning recommendations.

Output Format

markdown
## Review Mining Report: [Category/Competitors]

### Research Scope
- Competitors analyzed: [list]
- Platforms: [list]
- Reviews analyzed: [count]
- Date range: [range]

### Top Pain Points (ranked by frequency x severity)

#### 1. [Pain Point Theme] — mentioned in [X]% of negative reviews
- **Severity:** [Annoyance / Frustration / Deal-breaker / Switching trigger]
- **Competitors affected:** [list]
- **User quotes:**
  - "[verbatim quote]" — [platform], [star rating]
  - "[verbatim quote]" — [platform], [star rating]
- **Your opportunity:** [how your product addresses or could address this]

#### 2. [Pain Point Theme] ...

### Switching Triggers
| Trigger | Frequency | From → To | Quote |
|---------|-----------|-----------|-------|
| ... | ... | ... | ... |

### Voice of Customer Swipe File
**Words users use for the problem:** [list of exact phrases]
**Words users use for the desired outcome:** [list of exact phrases]
**Emotional language:** [frustration words, relief words]

### Positioning Opportunities
- [Opportunity 1]: [what you can claim based on competitor weakness]
- [Opportunity 2]: [underserved use case you can own]
Show full SKILL.md (306 more words)Show less

Frameworks & Best Practices

Where to mine by product type:

Product TypeBest Sources
B2B SaaSG2, Capterra, TrustRadius
B2C / ConsumerTrustpilot, App Store, Play Store
Developer ToolsReddit, Hacker News, GitHub Issues
E-commerce / DTCTrustpilot, Amazon reviews
AnyTwitter/X complaints, Reddit threads

Review analysis principles:

  • 1-2 star reviews reveal deal-breakers and switching triggers
  • 3 star reviews reveal "good enough but frustrated" — the most persuadable users
  • 4-5 star reviews reveal what users truly value (defend these in your product)
  • Recent reviews (last 6-12 months) matter more than old ones
  • Verified purchase/user reviews carry more weight

Verbatim language is the output. The exact words users use to describe their pain are more valuable than your summary. These become headlines, email subject lines, ad copy, and landing page copy.

Common mistakes:

  • Only reading negative reviews (you miss what users actually value)
  • Summarizing instead of quoting (you lose the authentic language)
  • Treating all complaints equally (frequency x severity matters)
  • Ignoring the context of who's reviewing (enterprise vs SMB, power user vs casual)
  • Mining once and never returning (do this quarterly)
  • competitive-analysis — for broader competitor research beyond reviews
  • user-research-synthesis — for synthesizing your own customer interviews
  • feedback-synthesis — for analyzing feedback from your own users
  • cold-outreach — use voice-of-customer language in prospecting emails

Examples

Prompt: "I'm building a project management tool. What are the biggest pain points people have with Asana and Monday.com?"

Good output includes: Mining Trustpilot, G2, and Capterra for Asana and Monday.com, extracting the top 5-7 pain points with verbatim quotes, identifying switching triggers, and mapping them to positioning opportunities.

Prompt: "We're a Trustpilot alternative. Help me understand what businesses hate about Trustpilot."

Good output includes: Mining Trustpilot's own reviews (meta!), G2, and Reddit for complaints about Trustpilot, extracting themes like review gating, pricing, fake review handling, and producing a voice-of-customer swipe file the founder can use in outreach.

© shawnpang, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/review-mining of shawnpang/startup-founder-skills.

Open the folder on GitHubat commit 4ad31b4

Compare with similar skills

Review Mining next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Review Mining compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Review Mining this skillshawnpang/startup-founder-skills343—~1.5kAutomated safety check: PassMIT
Replica EntrepreneurJakeschincariol/replica-skill1.4k—~1.2kAutomated safety check: PassMIT
Review Managementappeeky/aso-skills2.2k—~1.5kAutomated safety check: PassMIT
Memstack Product Feedback Analyzercwinvestments/memstack423—~2.7kAutomated safety check: PassProprietary
Voice Of Customer Synthesizergooseworks-ai/goose-skills1.2k1 repos~2.4kAutomated safety check: PassMIT
Product Agentgustavscirulis/snapgrid1161 repos~2.6kAutomated safety check: PassCustom licence

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Categories

Questions about Review Mining

What does Review Mining do?

When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores. Review Mining is an agent skill from shawnpang/startup-founder-skills. When the user wants to research customer pain points, complaints, or sentiment using review platforms like Trustpilot, G2, Capterra, or app stores.

When should I use Review Mining?

Review Mining fits situations like: wants to research customer pain points; sentiment using review platforms like Trustpilot; the user mentions what are users saying; competitor reviews.

How do I install Review Mining in Claude Code?

Run `npx skills add shawnpang/startup-founder-skills --skill review-mining -a claude-code`. Or copy the skill folder (skills/review-mining in shawnpang/startup-founder-skills) into .claude/skills/review-mining in your project. Claude Code loads it when a task matches its description.

How do I install Review Mining in Codex?

Run `npx skills add shawnpang/startup-founder-skills --skill review-mining -a codex`. Or copy the skill folder (skills/review-mining in shawnpang/startup-founder-skills) into .agents/skills/review-mining in your project. Codex loads it when a task matches its description.

Can I use Review Mining in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add shawnpang/startup-founder-skills --skill review-mining -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/review-mining, .gemini/skills/review-mining, .github/skills/review-mining and .opencode/skills/review-mining in your project.

What does Review Mining need to run?

SKILL.md names no scripts, command-line tools or credentials: Review Mining is instructions for the agent only.

Does Review Mining access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Review Mining safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Review Mining use?

Review Mining is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Review Mining use?

About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Review Mining?

Skills that share tags, products or a category with Review Mining: Replica Entrepreneur (Jakeschincariol/replica-skill, 1.4k stars), Review Management (appeeky/aso-skills, 2.2k stars), Memstack Product Feedback Analyzer (cwinvestments/memstack, 423 stars) and Voice Of Customer Synthesizer (gooseworks-ai/goose-skills, 1.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Review Mining?

shawnpang (a GitHub user) maintains it in shawnpang/startup-founder-skills, which has 343 GitHub stars. The repository holds 50 skills in this directory. The repository was last updated on March 16, 2026.

Source: shawnpang/startup-founder-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.