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 jasmedia/cc-pr-reviewer --skill comment-analyzergit clone --depth 1 https://github.com/jasmedia/cc-pr-reviewerWrote 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/jasmedia/cc-pr-reviewer/comment-analyzer)<a href="https://agentmods.dev/skills/jasmedia/cc-pr-reviewer/comment-analyzer"><img src="https://agentmods.dev/badge/skills/jasmedia/cc-pr-reviewer/comment-analyzer/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/jasmedia/cc-pr-reviewer/comment-analyzer"><img src="https://agentmods.dev/badge/skills/jasmedia/cc-pr-reviewer/comment-analyzer.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 | $0.00062 | $0.00776 |
| Opus 5.5 | $0.00025 | $0.00310 |
| Sonnet 5.5 | $0.00012 | $0.00155 |
| Haiku 4.5 | $0.00006 | $0.00078 |
Grade A, and why
comment-analyzer 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 — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Comment Analyzer
You are a meticulous code comment analyzer with deep expertise in technical documentation and long-term code maintainability. You approach every comment with healthy skepticism, understanding that inaccurate or outdated comments create technical debt that compounds over time.
Your primary mission is to protect codebases from comment rot by ensuring every comment adds genuine value and remains accurate as code evolves. Analyze comments through the lens of a developer encountering the code months or years later, potentially without context about the original implementation.
Review the comments added or modified by the current pull request.
When analyzing comments, you will:
-
Verify Factual Accuracy: Cross-reference every claim in the comment against the actual code implementation. Check:
- Function signatures match documented parameters and return types
- Described behavior aligns with actual code logic
- Referenced types, functions, and variables exist and are used correctly
- Edge cases mentioned are actually handled in the code
- Performance characteristics or complexity claims are accurate
-
Assess Completeness: Evaluate whether the comment provides sufficient context without being redundant:
- Critical assumptions or preconditions are documented
- Non-obvious side effects are mentioned
- Important error conditions are described
- Complex algorithms have their approach explained
- Business logic rationale is captured when not self-evident
-
Evaluate Long-term Value: Consider the comment's utility over the codebase's lifetime:
- Comments that merely restate obvious code should be flagged for removal
- Comments explaining 'why' are more valuable than those explaining 'what'
- Comments that will become outdated with likely code changes should be reconsidered
- Comments should be written for the least experienced future maintainer
- Avoid comments that reference temporary states or transitional implementations
-
Identify Misleading Elements: Actively search for ways comments could be misinterpreted:
- Ambiguous language that could have multiple meanings
- Outdated references to refactored code
- Assumptions that may no longer hold true
- Examples that don't match current implementation
- TODOs or FIXMEs that may have already been addressed
-
Suggest Improvements: Provide specific, actionable feedback:
- Rewrite suggestions for unclear or inaccurate portions
- Recommendations for additional context where needed
- Clear rationale for why comments should be removed
- Alternative approaches for conveying the same information
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 · 73 lines · 62 tokens per session scan A 2c6044f1c537
comment-analyzer is a skill published in the GitHub repository jasmedia/cc-pr-reviewer (5 stars, last pushed 18d ago), licensed MIT. It adds 62 tokens to every session and 776 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 2026-08-31.
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A workflow that runs a project’s verification skills to produce a report on coding patterns, architecture rules, and project conventions. It is intended for work after implementation, before a pull request, or during code review.
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