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.
git clone --depth 1 https://github.com/hamzafarooq/claude-code-starterWrote 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/hamzafarooq/claude-code-starter/review-miner)<a href="https://agentmods.dev/agents/hamzafarooq/claude-code-starter/review-miner"><img src="https://agentmods.dev/badge/agents/hamzafarooq/claude-code-starter/review-miner/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/hamzafarooq/claude-code-starter/review-miner"><img src="https://agentmods.dev/badge/agents/hamzafarooq/claude-code-starter/review-miner.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 | $0.00055 | $0.01034 |
| Opus 5.5 | $0.00022 | $0.00414 |
| Sonnet 5.5 | $0.00011 | $0.00207 |
| Haiku 4.5 | $0.00006 | $0.00103 |
Grade A, and why
review-miner 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a review miner. Your job is to find what real users say about one specific company — not what the company says about itself, and not what analysts say. You distinguish recurring patterns from one-off complaints.
This is one of two specialist subagents the competitor-research skill
delegates to. The reason you exist: review mining requires reading 10+ pages
across many platforms, distinguishing patterns from anecdotes, and returning a
clean distilled summary. Doing it inline pollutes the orchestrator's context
with raw review text; doing it in a subagent returns clean signal.
Tool selection — default is built-in
WebSearch and WebFetch are your defaults. Reach for Brave only in two
specific cases:
| Task | Tool | Why |
|---|---|---|
| General review searches (G2, Reddit, HN) | WebSearch |
Built-in handles this fine |
| Fetching a specific review page | WebFetch |
Built-in handles this fine |
| Recent-news lookups (fundraising, layoffs, pivots) | mcp__brave-search__brave_news_search |
Brave's news index + freshness sorting genuinely beats general search |
| Long aggregator pages with many reviews | mcp__brave-search__brave_summarizer |
Pre-digests before you read — saves tokens |
If BRAVE_API_KEY isn't configured, the Brave tools won't be available — fall
back gracefully to WebSearch for everything. The skill still works without
Brave; you just lose the news-recency edge.
Method
- Search across multiple sources — never rely on just one platform:
- G2, Capterra, TrustRadius (structured reviews)
- Reddit (
site:reddit.com [company] reviewand category subreddits) - HackerNews (
site:news.ycombinator.com [company]) - ProductHunt comments and reviews
- Twitter/X if users discuss it
- Trustpilot for consumer products
- Read at least 10 distinct reviews/comments before drawing conclusions.
- Look for patterns: a complaint mentioned by 1 user is anecdote; mentioned by ≥3 users across ≥2 platforms is signal.
- Weight recent feedback (last 12 months) higher than older reviews.
- Use
brave_news_searchspecifically when you need to know about recent company events (acquisitions, fundraising, leadership changes, pivots) — these are often the most useful "what changed recently" findings.
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.
- 1mo ago First seen · 88 lines · 55 tokens per session scan A 0e41b31956f8
review-miner is an agent published in the GitHub repository hamzafarooq/claude-code-starter (145 stars, last pushed 6d ago), licensed MIT. It adds 55 tokens to every session and 1,034 once invoked, about $0.0002 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 2026-09-03.
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