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/p2ergmbh/agentic-coding/github-issue-refinenpx skills add P2ERGmbH/agentic-coding --skill github-issue-refinegit clone --depth 1 https://github.com/P2ERGmbH/agentic-codingWhat 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 | $0.00027 | $0.01398 |
| Opus 5 | $0.00014 | $0.00699 |
| Sonnet 5 | $0.00005 | $0.00280 |
| Haiku 4.5 | $0.00003 | $0.00140 |
Grade A, and why
github-issue-refine 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitHub Issue Refinement Workflow
This workflow guides you through refining an existing GitHub issue to improve its clarity, technical implementation details, and alignment with project rules.
Trigger
Use this workflow when the user asks to "refine an issue," "improve a ticket," "groom a task," or "update the issue description."
Phase 1: Research & Validation
- Fetch Issue Details:
- Retrieve the issue details:
gh issue view $ISSUE_NUMBER
- Retrieve the issue details:
- Analyze Current State:
- Identify the core goal and success criteria.
- Assess if the current implementation plan is specific enough.
- Check for missing contexts (UI constraints, etc.).
- Cross-Reference docs/rules:
- Verify that the proposed solution aligns with:
next.md: Are Server Actions and Client/Server component rules followed?ui.md&icons.md: Is the component and icon usage correct?testing.md: Is there a clear strategy for Vitest?figma.md: Explicitly read this file to ensure Figma implementations adhere to the parsing rules, cleanup requirements, and master component references.
- Verify that the proposed solution aligns with:
Phase 2: Technical Refinement
- Improve Implementation Steps:
- Refine generic steps into specific actions tied to concrete files and functions.
- Ensure every step has a "Reason" explaining WHY it's necessary.
- Add Additional Context:
- Examples: Provide snippets of expected data structures or code patterns.
- Diagrams: Use Mermaid syntax for complex state transitions or data flows.
- Documentation: Reference specific documentation paths (e.g., from
nextjs_docs) relevant to the implementation.
- Deep File Investigation:
- Use
list_code_definition_namesorsearch_filesto find related logic that might be affected but wasn't mentioned in the original issue.
- Use
- Visual Context & Attachments:
- Current State: If possible, use browser automation tools to take a screenshot of the current state of the application (the "before" state). Ensure you clean up your browser session afterward by closing the opened pages using
mcp_chrome-devtools_list_pagesandmcp_chrome-devtools_close_page. - Figma Design: If a Figma layout link is provided in the issue, use the Figma MCP tool to download an image of the Figma frame to visually demonstrate what the design should be (the "after" state).
- Issue Update & Image Hosting: Because
ghCLI cannot directly upload images to issues, create a new branch namedassets/issue-$ISSUE_NUMBER. Push the downloaded images to.github/assets/issue-$ISSUE_NUMBER/on that branch. Then, update the issue description with standard Markdown image links using the GitHub blob URL with?raw=true(e.g.,). This ensures the images render correctly even in private repositories.
- Current State: If possible, use browser automation tools to take a screenshot of the current state of the application (the "before" state). Ensure you clean up your browser session afterward by closing the opened pages using
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 · 92 lines · 27 tokens per session scan A ecf686ba60b7
github-issue-refine is a skill published in the GitHub repository P2ERGmbH/agentic-coding (9 stars, last pushed 2mo ago), licensed MIT. It adds 27 tokens to every session and 1,398 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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