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 mir-am/skills-agents-ai-coding --skill gh-issue-fixgit clone --depth 1 https://github.com/mir-am/skills-agents-ai-codingWrote 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/mir-am/skills-agents-ai-coding/gh-issue-fix)<a href="https://agentmods.dev/skills/mir-am/skills-agents-ai-coding/gh-issue-fix"><img src="https://agentmods.dev/badge/skills/mir-am/skills-agents-ai-coding/gh-issue-fix/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/mir-am/skills-agents-ai-coding/gh-issue-fix"><img src="https://agentmods.dev/badge/skills/mir-am/skills-agents-ai-coding/gh-issue-fix.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 | $0.00023 | $0.01173 |
| Opus 5 | $0.00012 | $0.00587 |
| Sonnet 5 | $0.00005 | $0.00235 |
| Haiku 4.5 | $0.00002 | $0.00117 |
Grade A, and why
gh-issue-fix 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 9d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What I do
- Fetch a GitHub issue by number or URL using the
ghCLI - Ask clarifying questions if the issue description is too vague to act on
- Explore the codebase to identify affected files and root causes
- Generate a structured implementation plan and present it to the user
- Wait for explicit user approval before making any code changes
- Create a new branch (if on
main/master) after plan approval - Implement the fix or feature following the approved plan
- Run relevant tests and summarize changes
- Optionally hand off to
git-commitandgit-prskills
When to use me
- A GitHub issue number or URL is provided by the user
- The user says "fix issue #123", "implement issue #456", or similar
- The user wants a structured, plan-first approach before touching code
Phase 1: Issue Intake
-
If the user provided an issue number or URL, use it directly. If not, ask:
"Which GitHub issue should I work on? Please provide the issue number or URL."
-
Fetch the issue details:
gh issue view <number> --json number,title,body,labels,comments,assignees,state -
Evaluate the issue description:
- If the title and body clearly describe the problem or feature, proceed to Phase 2.
- If the description is thin (e.g. one-liners, missing reproduction steps, no acceptance criteria), ask targeted clarifying questions before continuing. Examples:
- "Can you describe the expected vs actual behavior?"
- "Is there a specific file or area of the codebase involved?"
- "What does 'done' look like for this feature?"
- Collect answers and incorporate them into the plan.
Phase 2: Codebase Exploration
Use Read, Glob, and Grep tools to explore the codebase relevant to the issue:
-
Search for keywords from the issue title and body:
rg "<keyword>" -l -
Identify:
- Files most likely to need changes
- Related tests
- Entry points, interfaces, or API contracts involved
- Any existing error handling or edge cases nearby
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.
- 9d ago First seen · 161 lines · 23 tokens per session scan A b5382add9d72
gh-issue-fix is a skill published in the GitHub repository mir-am/skills-agents-ai-coding (2 stars, last pushed 4mo ago), licensed MIT. It adds 23 tokens to every session and 1,173 once invoked, about $0.0001 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 2026-08-31.
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file-todos
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lavra-work
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