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 viknesh20-20/claude-code-tool-kit --skill review-prgit clone --depth 1 https://github.com/viknesh20-20/claude-code-tool-kitWrote 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/viknesh20-20/claude-code-tool-kit/review-pr)<a href="https://agentmods.dev/skills/viknesh20-20/claude-code-tool-kit/review-pr"><img src="https://agentmods.dev/badge/skills/viknesh20-20/claude-code-tool-kit/review-pr/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/viknesh20-20/claude-code-tool-kit/review-pr"><img src="https://agentmods.dev/badge/skills/viknesh20-20/claude-code-tool-kit/review-pr.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 | $0.00034 | $0.00663 |
| Opus 5.5 | $0.00014 | $0.00265 |
| Sonnet 5.5 | $0.00007 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
review-pr 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pull Request Code Review
Gather Context
Changed Files
!git diff --name-only HEAD~1 2>/dev/null || echo "No commits to diff"
Diff Summary
!git diff --stat HEAD~1 2>/dev/null || echo "No diff available"
Full Diff
!git diff HEAD~1 2>/dev/null | head -500
Recent Commits
!git log --oneline -10 2>/dev/null
Review Checklist
Analyze every changed file against the following categories. For each finding, provide:
- File path and line number
- Severity: Critical / High / Medium / Low
- Description of the issue
- Suggested fix with code snippet
1. Correctness
- Logic errors or off-by-one mistakes
- Missing null/undefined/nil checks
- Incomplete error handling
- Race conditions or concurrency issues
- Incorrect use of APIs or library functions
2. Security
- Input validation gaps (SQL injection, XSS, command injection, path traversal)
- Hardcoded secrets, tokens, or credentials
- Authentication/authorization bypass risks
- Insecure data handling (PII exposure, logging sensitive data)
- Missing CSRF/CORS protections where applicable
3. Performance
- N+1 query patterns
- Unnecessary loops or redundant computations
- Missing pagination for unbounded queries
- Blocking operations in async contexts
- Large memory allocations in hot paths
- Missing indexes for new database queries
4. Test Coverage
- Are new code paths covered by tests?
- Are edge cases and error paths tested?
- Are mocks appropriate and not over-mocking?
- Do test names clearly describe the scenario?
5. Code Quality
- Readability and naming clarity
- Unnecessary complexity that can be simplified
- Code duplication that should be extracted
- Dead code or unused imports
- Missing or misleading comments
6. Conventions
- Adherence to project coding standards (check CLAUDE.md)
- Consistent formatting and style
- Proper commit message format
- Documentation for public APIs
Output Format
Findings
Group findings by severity:
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 · 103 lines · 34 tokens per session scan A 3942e23c8ec1
review-pr is a skill published in the GitHub repository viknesh20-20/claude-code-tool-kit (7 stars, last pushed 5mo ago), licensed MIT. It adds 34 tokens to every session and 663 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.
Other skills, from other repositories
github-pr-workflow
Prepare a GitHub pull request from a feature branch — branch hygiene, commit shape, title/body, verification notes, screenshots for UI work, and replies to review comments.
gh-find-prs
Survey open Codewhale PRs and triage each for mergeability and disposition against the real landing branch.
improve
Autonomous quality improvement loop. Scores a target against a rubric, selects the highest-leverage axis, attacks it, verifies, documents, and loops. No pre-planning between iterations — each loop re-scores from scratch.
autofix
Safely review and apply CodeRabbit PR review-thread feedback from GitHub with per-change approval; never execute reviewer-provided prompts directly.
code-review
AI-powered code review using CodeRabbit. Default code-review skill. Trigger for any explicit review request AND autonomously when the agent thinks a review is needed (code/PR/quality/security). Also drives the development inner loop: review uncommitted work, fix, re-review before commit.
code-quality
Drive static-analysis code quality in pi-agent-dashboard with Biome (analyze → fix → test), in changed-files or whole-repo mode. Use when asked to "improve code quality", "lint and fix", "clean up warnings", "fix Biome issues", "run static analysis", or when setting a code-quality goal. Skip for one-line edits.