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/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/geonheeye/multi-agent-dotfiles/llm-wiki"><img src="https://agentmods.dev/badge/skills/geonheeye/multi-agent-dotfiles/llm-wiki.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 | $0.00039 | $0.01103 |
| Opus 5.5 | $0.00016 | $0.00441 |
| Sonnet 5.5 | $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 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.
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.
- 2mo 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 25d ago), with no licence file. It adds 39 tokens to every session and 1,103 once invoked, about $0.0002 per session on Opus 5.5. 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
Use LeanCTX Context Gateway tools to select task-relevant code context, apply supported controls, reuse local knowledge and inspect recorded evidence. Use when lean-ctx MCP tools (ctx) are available or file, search or command output needs a focused view.
lean-ctx
Use LeanCTX Context Gateway tools to select task-relevant code context, apply supported controls, reuse local knowledge and inspect recorded evidence. Use when lean-ctx MCP tools (ctx) are available or file, search or command output needs a focused view.
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.
update-memory-bank
Sync memory bank with repository state.
pn-continual-learning
Incrementally extract recurring user corrections and durable workspace facts from transcript changes, then update AGENTS.md with plain bullet points only. Use when the user asks to mine previous chats, maintain AGENTS.md memory, or build a self-learning preference loop.