github-skill-research

A research workflow for finding and comparing real repositories and documentation about coding-agent skills. It examines reusable instructions, trigger rules, workflows, output formats, safety rules, and project layouts.

In plain words
What is it for?
Use it when creating, improving, auditing, comparing, or templating agent skills. It helps research SKILL.md, AGENTS.md, CLAUDE.md, prompt libraries, and related workflow examples.
Why use it?
Designing an agent add-on from memory can miss useful patterns or copy a format tied to one tool. This workflow grounds the comparison in actual repositories and primary documentation.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/ntaffzii/skill-agents/github-skill-research
Any agent
npx skills add ntaffzii/Skill-Agents --skill github-skill-research
Clone the repo
git clone --depth 1 https://github.com/ntaffzii/Skill-Agents

Made for: Claude Code, Codex.

Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 477 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00061 $0.00477
Opus 5 $0.00030 $0.00238
Sonnet 5 $0.00012 $0.00095
Haiku 4.5 $0.00006 $0.00048

Measured 2d ago against content hash 2c26bd9d86d6, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

github-skill-research 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.

skills/research/github-skill-research/SKILL.md · 61 lines

What it actually says

GitHub Skill Research

Research reusable AI-agent skill patterns from real repositories and primary documentation.

Workflow

  1. Define the research target

    • Identify whether the user wants structure, trigger logic, workflow design, operating rules, examples, portability, or repository organization.
    • If the target is unclear, make a narrow useful assumption and state it.
  2. Search sources

    • Prioritize real repositories with SKILL.md, AGENTS.md, CLAUDE.md, prompt libraries, workflow playbooks, or MCP/tooling examples.
    • Prefer official documentation and maintained repositories over generic blog advice.
    • Include thananon/9arm-skills as a useful comparison point when evaluating concise skill structure.
    • See research-methodology for tiering sources (repo itself vs. a blog post about the repo) and tracking which candidates were actually checked vs. skipped.
  3. Analyze examples

    • Extract frontmatter style, trigger descriptions, workflow steps, output formats, safety rules, bundled resources, and repo layout.
    • Identify whether each pattern is portable across agents or tied to one tool.
  4. Compare against the user's repo

    • Mark patterns to copy, adapt, skip, or keep private.
    • Call out duplication, overbroad skills, stale instructions, and missing validation.
  5. Produce recommendations

    • Respond in the user's language.
    • Include links or local file paths.
    • Keep templates short enough to install or edit immediately.

Output Format

# AI Agent Skill Research

## Summary

## Useful Sources

## Patterns To Copy

## Patterns To Adapt

## Anti-patterns To Avoid

## Suggested Skill Template

## Action Items

Rules

  • Do not summarize sources without links or file paths.
  • Do not copy a skill verbatim unless the user asks.
  • Keep trigger logic in YAML description.
  • Prefer workflows with verification over advice-only prompts.
Changes

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.

  1. 2d ago First seen · 61 lines · 61 tokens per session scan A 2c26bd9d86d6

Subscribe to this mod's changes

github-skill-research is a skill published in the GitHub repository ntaffzii/Skill-Agents (4 stars, last pushed 4d ago), licensed MIT. It adds 61 tokens to every session and 477 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 2026-08-31.