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 shopwareLabs/ai-coding-tools --skill pr-analyzinggit clone --depth 1 https://github.com/shopwareLabs/ai-coding-toolsWrote 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/shopwarelabs/ai-coding-tools/pr-analyzing)<a href="https://agentmods.dev/skills/shopwarelabs/ai-coding-tools/pr-analyzing"><img src="https://agentmods.dev/badge/skills/shopwarelabs/ai-coding-tools/pr-analyzing/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/shopwarelabs/ai-coding-tools/pr-analyzing"><img src="https://agentmods.dev/badge/skills/shopwarelabs/ai-coding-tools/pr-analyzing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00056 | $0.01464 |
| Opus 5.5 | $0.00022 | $0.00586 |
| Sonnet 5.5 | $0.00011 | $0.00293 |
| Haiku 4.5 | $0.00006 | $0.00146 |
Grade A, and why
pr-analyzing 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PR Analyzing
Analyze a GitHub pull request by combining authoritative PR data from GitHub with semantic code research from ChunkHound. Produces a structured analysis of scope, impact, and architectural context.
Do not activate for generic code questions that do not reference a specific PR.
Prerequisites
Requires the chunkhound-integration companion plugin. The skill calls mcp__plugin_chunkhound-integration_ChunkHound__code_research at Step 3 and stops with an error if that tool is not callable — see Errors.
PR data is fetched from GitHub using whatever access the session has available (a GitHub MCP server, the gh CLI, or direct API calls).
Input
- Required: PR number
- Optional: Repository as
owner/name— defaults to the current repository context - Optional: Triage reasoning — context from the caller about why analysis was requested (e.g., "flagged because it touches 12 files across Checkout and Payment"). Treat as a hint for research focus, not required input.
Workflow
Step 1 — Fetch PR data
Fetch the following from GitHub, in order:
- PR metadata — title, body, author, labels, state
- Changed files — list with additions/deletions per file
- Unified diff
- Reviews — decisions and top-level review bodies
- Inline code review comments
Use whatever GitHub access is available in the session. Collect everything before Step 2. On error (PR not found, access denied, no GitHub access), stop and report to the user.
Step 2 — Assess scope
From the fetched data, determine:
- File count and spread — how many files changed and how many distinct directories or top-level areas they span
- Volume — total additions and deletions
- Labels — area labels, type labels (feature, bugfix, breaking change), and any flags
- Review complexity — number of reviews, inline comment volume, whether reviews are contentious or unanimous
This assessment drives how much research depth Step 3 warrants.
Step 3 — Research architectural impact
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 · 110 lines · 56 tokens per session scan A a1c577f2c6bd
pr-analyzing is a skill published in the GitHub repository shopwareLabs/ai-coding-tools (45 stars, last pushed 4d ago), licensed MIT. It adds 56 tokens to every session and 1,464 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 2026-08-30.
Other skills, from other repositories
audit-comment-residue
Read-only classifier that flags residue in code comments: history narration, plan or session references, conversational asides, ticket or PR back-references, origin notes, and workarounds with no link or removal condition. Use when asked to 'audit code comments' or report which comments only make sense with the chat…
improve
Codebase scan for module-level friction (shallow modules, seam leaks, locality gaps): an HTML candidate report, then a Design-It-Twice interview on the chosen one. Use when 'improve architecture', 'find deepening opportunities', 'shallow modules', 'design it twice', 'compare alternative interfaces', 'make code more…
code-review
CI code-review lane for a GitHub pull request. High-signal correctness and maintainability findings only, scoped out of security when a security lane exists. Use when: 'CI code review', 'claude-review lane', '/review:code-review', or a reusable workflow invokes the org code-review plugin command.
refactoring
Use when restructuring code without changing behavior -- extracting functions, renaming, moving files, reducing duplication, migrating between patterns (JS to TS, CJS to ESM), or addressing code smells. Covers safe refactoring workflows for any language.
review-pr
Comprehensive PR review -- full branch diff against base, commit-by-commit analysis, breaking change detection, conventional commit verification.
clean-code
Use when the user writes new Python, or asks for review, refactor, or cleanup of Python code, names, comments/docstrings, functions, or tests. Applies Robert Martin's complete Clean Code catalog -- naming, functions, comments, DRY, boundary conditions, and tests -- to the code being changed.