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 receptron/mulmoclaude --skill mc-wiki-deep-lintgit clone --depth 1 https://github.com/receptron/mulmoclaudeWrote 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/receptron/mulmoclaude/mc-wiki-deep-lint)<a href="https://agentmods.dev/skills/receptron/mulmoclaude/mc-wiki-deep-lint"><img src="https://agentmods.dev/badge/skills/receptron/mulmoclaude/mc-wiki-deep-lint/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/receptron/mulmoclaude/mc-wiki-deep-lint"><img src="https://agentmods.dev/badge/skills/receptron/mulmoclaude/mc-wiki-deep-lint.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
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 →
- high YARA Match · line 3 YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
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 | $0.00096 | $0.01361 |
| Opus 5 | $0.00048 | $0.00681 |
| Sonnet 5 | $0.00019 | $0.00272 |
| Haiku 4.5 | $0.00010 | $0.00136 |
Grade A, and why
mc-wiki-deep-lint 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 11d 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 — 109 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Wiki Deep Lint (LLM)
A bundled MulmoClaude preset skill (mc- prefix = launcher-managed; do not edit
this file in the workspace, it is overwritten on every server boot).
This is the LLM-driven counterpart to the structural mc-wiki-health-check
(#1491 Phase D). Where the structural lint catches broken links / orphan
pages / tag drift via deterministic graph walks, this skill uses the
agent's own reading and reasoning to catch the three lint classes that
structure alone misses, as enumerated in Karpathy's LLM-Wiki pattern:
- contradictions — two pages making opposing factual claims
- stale claims — assertions whose date or status terms (
current,planning,wip,2025-Q3, etc.) have aged out - missing concepts — topics referenced in
index.md,log.md, orsources/(and mentioned across multiple pages) that don't yet have their ownpages/<slug>.md
The skill is manual / on-demand (no schedule: frontmatter). It pairs
with the scheduled-and-quiet mc-wiki-health-check.
What to do
-
Build a working set, don't load the entire wiki. Token budget matters. Read, in order:
data/wiki/index.md(category catalog)data/wiki/log.md(recent activity — last ~40 entries is plenty)- The 10–20 most-recently-modified
data/wiki/pages/*.md(usemanageWikilisting actions to pick them) data/wiki/sources/listing (titles only is fine for the gap check)
If the user names specific pages or topics, scope to those instead.
-
Run the three checks against the working set, in order:
Contradictions
For each pair of pages on related topics (same tag or same index category), look for opposing factual claims. Flag the specific sentences from both pages, not just the slugs. Soft conflicts (one page is more nuanced) are fine — only flag direct contradictions.
Stale claims
For each page, look for:
- Dated claims older than ~6 months without a follow-up entry in
log.md - Status markers (
current,planning,in progress,wip,upcoming,next quarter) whose anchoring date has passed - "Latest X is Y" / "the current version is Z" claims that contradict more recent log entries Flag the sentence and the anchoring date.
- Dated claims older than ~6 months without a follow-up entry in
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.
- 11d ago First seen · 109 lines · 96 tokens per session scan A 0cdf750b21c9
mc-wiki-deep-lint is a skill published in the GitHub repository receptron/mulmoclaude (347 stars, last pushed today), licensed MIT. It adds 96 tokens to every session and 1,361 once invoked, about $0.0005 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…