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 Muriel-Salvan/x_aeon_agents --skill analyzing-github-issuegit clone --depth 1 https://github.com/Muriel-Salvan/x_aeon_agentsWrote 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/muriel-salvan/x_aeon_agents/analyzing-github-issue)<a href="https://agentmods.dev/skills/muriel-salvan/x_aeon_agents/analyzing-github-issue"><img src="https://agentmods.dev/badge/skills/muriel-salvan/x_aeon_agents/analyzing-github-issue/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/muriel-salvan/x_aeon_agents/analyzing-github-issue"><img src="https://agentmods.dev/badge/skills/muriel-salvan/x_aeon_agents/analyzing-github-issue.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 | $0.00062 | $0.01077 |
| Opus 5.5 | $0.00025 | $0.00431 |
| Sonnet 5.5 | $0.00012 | $0.00215 |
| Haiku 4.5 | $0.00006 | $0.00108 |
Grade A, and why
analyzing-github-issue 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 2mo 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Implementing a GitHub issue
Sequential steps to be followed when using this skill
When implementing a GitHub issue, follow those steps.
Create the analyzing-github-issue Execution Checklist (MANDATORY)
- Before executing anything, create a checklist named analyzing-github-issue Execution Checklist with all steps of these instructions.
- The analyzing-github-issue Execution Checklist must include all numbered steps explicitly.
- After completing each step of these instructions, mark the item in the analyzing-github-issue Execution Checklist as completed.
- Do not skip any item.
- If an item cannot be executed, explicitly explain why.
- Never mark the task as completed while any item from the analyzing-github-issue Execution Checklist remains open.
1. Inform the user
- Always tell the user "SKILL: I am implementing a GitHub issue" to inform the user that you are running this skill.
2. Get issue number (can be done during Plan mode)
- Always use
agent: ask_followup_questionto ask the user which GitHub issue should be analyzed, unless the user already gave you this information in the prompt.
3. Get issue requirements (can be done during Plan mode)
- Find this skill directory path, later referenced as {skill_path}.
- Always use
cli: ruby {skill_path}/scripts/issue_details {issue_number}to retrieve all the details of this GitHub issue.
Example with expected output as JSON:
ruby .cline/skills/analyzing-github-issue/scripts/issue_details 29
# => {"body":"# This is the GitHub issue body ...","comments":[],"labels":[],"number":29,"state":"OPEN","title":"This is the GitHub issue title","url":"https://github.com/my-user/my-repo/issues/29"}
Example of an error case with a non-existing GitHub issue:
ruby .cline/skills/analyzing-github-issue/scripts/issue_details 29
# GraphQL: Could not resolve to an issue or pull request with the number of 29. (repository.issue)
# .cline/skills/analyzing-github-issue/scripts/issue_details:9:in 'Kernel#system': Command failed with exit 1: gh issue view 29 --json=number,title,body,comments,labels,state,url (RuntimeError)
# from .cline/skills/analyzing-github-issue/scripts/issue_details:9:in '<main>'
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 117 lines · 62 tokens per session scan A 31e31532aa56
analyzing-github-issue is a skill published in the GitHub repository Muriel-Salvan/x_aeon_agents (2 stars, last pushed 3d ago), licensed BSD-3-Clause. It adds 62 tokens to every session and 1,077 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 2026-08-31.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…