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/ntaffzii/skill-agents/github-skill-researchnpx skills add ntaffzii/Skill-Agents --skill github-skill-researchgit clone --depth 1 https://github.com/ntaffzii/Skill-AgentsWhat 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 | $0.00061 | $0.00477 |
| Opus 5 | $0.00030 | $0.00238 |
| Sonnet 5 | $0.00012 | $0.00095 |
| Haiku 4.5 | $0.00006 | $0.00048 |
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.
What it actually says
GitHub Skill Research
Research reusable AI-agent skill patterns from real repositories and primary documentation.
Workflow
-
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.
-
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-skillsas 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.
- Prioritize real repositories with
-
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.
-
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.
-
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.
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 · 61 lines · 61 tokens per session scan A 2c26bd9d86d6
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.
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