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 flsteven87/llm-wiki-mcp --skill wiki-querygit clone --depth 1 https://github.com/flsteven87/llm-wiki-mcpWrote 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/flsteven87/llm-wiki-mcp/wiki-query)<a href="https://agentmods.dev/skills/flsteven87/llm-wiki-mcp/wiki-query"><img src="https://agentmods.dev/badge/skills/flsteven87/llm-wiki-mcp/wiki-query/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/flsteven87/llm-wiki-mcp/wiki-query"><img src="https://agentmods.dev/badge/skills/flsteven87/llm-wiki-mcp/wiki-query.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 | $0.00243 | $0.01517 |
| Opus 5 | $0.00121 | $0.00758 |
| Sonnet 5 | $0.00049 | $0.00303 |
| Haiku 4.5 | $0.00024 | $0.00152 |
Grade A, and why
wiki-query 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 9d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
wiki-query
Answer a question against an existing Karpathy-style LLM wiki. The pattern this skill follows is described in https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f.
Karpathy's query description, verbatim:
You ask questions against the wiki. The LLM searches for relevant pages, reads them, and synthesizes an answer with citations.
And on filing answers back:
good answers can be filed back into the wiki as new pages. A comparison you asked for, an analysis, a connection you discovered — these are valuable and shouldn't disappear into chat history.
Filing is a judgment call, not an automatic step. Pure fact lookup or restatement of an existing page stays in chat. Cross-page analyses and new connections earn a page.
Prerequisites
- A wiki exists. If not, run
wiki-initfirst. - The llm-wiki-mcp server is wired into this session. If
wiki_inventoryis not callable, tell the user to add the server to their MCP client config and restart.
Pre-flight
Read CLAUDE.md. This is the authoritative schema — page
categories, frontmatter fields, link conventions, and operation
vocabulary. The user may have evolved it since wiki-init. Honor
the current state, not the defaults.
Flow
1. Scope
wiki_inventory()
No scan_for argument — this is a cheap call that returns every
page's slug, frontmatter, body length, and computed in/out links.
Combine it with a host Read of index.md to pick candidate
slugs. Prefer pages whose title, tags, or category directly match
the question. When in doubt, widen the candidate set — reading is
cheap.
2. Read
wiki_read(slug=...)
Pull the full body of each candidate. If a page cites a neighbor
([[other-slug]]) that clearly bears on the question, widen and
read it too. Two or three reads are normal; ten means the question
is too broad and you should narrow it with the user first.
3. Synthesize with citations
Write the answer in the primary language declared in
CLAUDE.md. Cite every non-trivial claim with [[slug]]
pointing at the page the claim came from. Only cite slugs that
appeared in the pages list returned by step 1 — this is the
deterministic guarantee that your citations are not hallucinations.
If a claim deserves a citation but no page supports it, say so
explicitly ("the wiki does not currently cover X") rather than
inventing a slug.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 141 lines · 243 tokens per session scan A 627bd539d8c8
wiki-query is a skill published in the GitHub repository flsteven87/llm-wiki-mcp (1 stars, last pushed 5mo ago), licensed MIT. It adds 243 tokens to every session and 1,517 once invoked, about $0.0012 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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