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 GeonheeYe/multi-agent-dotfiles --skill llm-wikigit clone --depth 1 https://github.com/GeonheeYe/multi-agent-dotfilesWrote 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/geonheeye/multi-agent-dotfiles/llm-wiki)<a href="https://agentmods.dev/skills/geonheeye/multi-agent-dotfiles/llm-wiki"><img src="https://agentmods.dev/badge/skills/geonheeye/multi-agent-dotfiles/llm-wiki.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.01103 |
| Opus 5 | $0.00019 | $0.00551 |
| Sonnet 5 | $0.00008 | $0.00221 |
| Haiku 4.5 | $0.00004 | $0.00110 |
Grade A, and why
llm-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 6d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 6d ago First seen · 97 lines · 39 tokens per session scan A 36d158efc0d3
llm-wiki is a skill published in the GitHub repository GeonheeYe/multi-agent-dotfiles (3 stars, last pushed 6d ago), with no licence file. It adds 39 tokens to every session and 1,103 once invoked, about $0.0002 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.
Other skills, from other repositories
karpathy-llm-wiki
Use when building or maintaining a personal LLM-powered knowledge base. Triggers: ingesting sources into a wiki, querying wiki knowledge, linting wiki quality, 'add to wiki', 'what do I know about', or any mention of 'LLM wiki' or 'Karpathy wiki'.
lean-ctx
Local context tooling for AI agents. Use it to select, shape, reuse, recover, and inspect context before inference when reading files, running shell commands, searching code, or exploring directories.
lean-ctx
Local context tooling for AI agents. Use it to select, shape, reuse, recover, and inspect context before inference when reading files, running shell commands, searching code, or exploring directories.
audit-history
Use when reviewing past agent sessions, auditing memory health, identifying repeated corrections or friction, cleaning up stale memories, proposing new skills and rules from usage patterns, or identifying mechanical improvements (testing, linting, static analysis, tooling) that could improve outcomes.
deep-research
Use when asked to research deeply, compare options or vendors, fact-check a claim, investigate an unfamiliar topic, prepare a report or briefing, build a knowledge base or reference doc, or trace a claim to its primary source.
me-card
Render the user's saved /.me/ context as a single ASCII-art infographic card. Use when the user asks to "show my info", "print my card", "render my profile", "me card", "ascii infographic of my dot-me", or invokes the /me-card shortcut. Read-only — never writes to /.me/.