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 mhlaghari/stuntman --skill wikigit clone --depth 1 https://github.com/mhlaghari/stuntmanWrote 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/mhlaghari/stuntman/wiki)<a href="https://agentmods.dev/skills/mhlaghari/stuntman/wiki"><img src="https://agentmods.dev/badge/skills/mhlaghari/stuntman/wiki.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
SkillSpector: 1 finding, up to low
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- low MCP Rug Pull · line 72 pip install without ==version installs the latest release, which could include malicious changes.Fix: Pin the version: pip install package==1.2.3
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 | $0.00114 | $0.01711 |
| Opus 5 | $0.00057 | $0.00856 |
| Sonnet 5 | $0.00023 | $0.00342 |
| Haiku 4.5 | $0.00011 | $0.00171 |
Grade A, and why
wiki scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
stuntman: wiki — stand up an LLM-wiki second brain + graphify
/scaffold gives one project a memory; /wiki gives a whole folder of projects
a shared brain: a navigable Obsidian vault of notes-about-projects, a graphify
knowledge graph, and a live MCP so any future session can ask "did I already solve this?"
across everything.
It's the Karpathy "LLM wiki" pattern, automated. Run it once in the target folder.
Dependencies
- graphify (
pip install graphifyy, or the user's/graphifyskill) — builds the graph. - mcp python package + the current host CLI (
claudeorcodex) — to wire the live query server. The skill installs/checks these along the way; if one is truly unavailable, do that step's fallback and tell the user.
What to do
1. Scaffold the skeleton (deterministic, idempotent)
Read host and tool setup. Set STUNTMAN_HOST to codex in
Codex or claude in Claude Code; use both when requested.
WIKI="$STUNTMAN_ROOT/bin/wiki"
"$WIKI" --host "$STUNTMAN_HOST" # optionally append /path/to/folder
Read its output: VAULT=…, MODE=single|folder, and (folder mode) the PROJECTS: list.
Never clobbers — re-running only fills gaps. $VAULT is <folder>-wiki/.
2. Ensure graphify is installed
Resolve an interpreter that can import graphify (uv tool / pipx / venv / system). If none,
install it in a dedicated virtual environment or with uv/pipx. Save the interpreter path to
$VAULT/graphify-out/.graphify_python for later steps (create that directory first).
3. Populate the notes from reality
Write notes that a reader with zero memory of the codebase can use. Follow the schema in
the vault's AGENTS.md (Codex) or CLAUDE.md (Claude Code), including frontmatter + Summary / Architecture / Connections / Notable.
Every note's frontmatter needs type: and a one-line description: (OKF v0.1 — this is what
makes the vault portable across agent tooling). Wikilinks must target real page basenames
(kebab-case filenames, not Title Case) — broken links render as ghost nodes in Obsidian.
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 Changed · +11 lines · +2 tokens per session dcfd072b2732
- 8d ago First seen · 91 lines · 112 tokens per session scan A 31b6ca922c57
wiki is a skill published in the GitHub repository mhlaghari/stuntman (13 stars, last pushed yesterday), licensed MIT. It adds 114 tokens to every session and 1,711 once invoked, about $0.0006 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 2026-08-30.
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