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 agentmods add skills/shopwarelabs/ai-coding-tools/issue-analyzingnpx skills add shopwareLabs/ai-coding-tools --skill issue-analyzinggit clone --depth 1 https://github.com/shopwareLabs/ai-coding-toolsWhat 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 | $0.00051 | $0.01390 |
| Opus 5 | $0.00026 | $0.00695 |
| Sonnet 5 | $0.00010 | $0.00278 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade A, and why
issue-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 2d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Issue Analyzing
Analyze a GitHub issue by combining authoritative issue data from GitHub with semantic code research from ChunkHound. Produces a structured analysis of problem scope, affected code area, and resolution status.
Do not activate for generic code questions that do not reference a specific issue.
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.
Issue 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: Issue number
- Optional: Repository as
owner/name— defaults to the current repository context - Optional: Triage reasoning — context from the caller about why analysis was requested. Treat as a hint for research focus.
Workflow
Step 1 — Fetch issue data
Fetch the following from GitHub: issue metadata (title, body, author, labels, state) and all comments.
Use whatever GitHub access is available in the session. If a single call returns everything together, that's fine; if comments are returned separately, fetch them as part of this step. On error (issue not found, access denied, no GitHub access), stop and report to the user.
Step 2 — Identify the affected code area
From the issue description and comments, extract signals about what part of the codebase is affected:
- File paths or component names mentioned directly in the description
- Class or function names referenced in the text (often in backticks or stack traces)
- Error messages that can be traced back to specific components
- Area labels (e.g.,
area/checkout,area/admin) that indicate scope - Linked PRs referenced as fixes — comments often contain "Fixed in #X" or "See PR #Y"
If the issue is a feature request, the "affected area" is the area that would need to change to implement it. If the body is too vague to identify a specific area, note this in the output and research at the area label's level instead.
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.
- 2d ago First seen · 100 lines · 51 tokens per session scan A be743837073f
issue-analyzing is a skill published in the GitHub repository shopwareLabs/ai-coding-tools (42 stars, last pushed yesterday), licensed MIT. It adds 51 tokens to every session and 1,390 once invoked, about $0.0003 per session on Opus 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.
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