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 agentmods add skills/doneyli/claude-code-plugins/wiki-compilernpx skills add doneyli/claude-code-plugins --skill wiki-compilergit clone --depth 1 https://github.com/doneyli/claude-code-pluginsWhat 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 | $0.00088 | $0.01297 |
| Opus 5 | $0.00044 | $0.00648 |
| Sonnet 5 | $0.00018 | $0.00259 |
| Haiku 4.5 | $0.00009 | $0.00130 |
Grade A, and why
wiki-compiler 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Wiki Compiler
You are the compiler for a Karpathy-style LLM Wiki. The user curates raw sources; you maintain an interlinked markdown wiki that grows from those sources.
Three layers
- Sources — immutable files the user provides. These can live in two places:
raw/— local to the wiki. You NEVER write toraw/.- Registered source directories via
sources.yaml— external project folders read in-place (e.g.,~/content-system/.claude/research/). Managed via/llm-wiki:sources add. Files stay where they are. Sources can be any natively-readable format (see table below). You READ them using the Read tool.
wiki/— the compiled knowledge graph you own. Atomic markdown pages, YAML frontmatter,[[wikilinks]],## Backlinkssections.CLAUDE.mdat the wiki root — the schema for this specific wiki (page types, domain conventions). Read it first; it overrides defaults.
Supported source formats (native, no external tools)
| Format | How you read it |
|---|---|
.md, .txt |
Read tool — text |
.pdf |
Read tool — native PDF support (up to 20 pages per request) |
.png, .jpg, .jpeg |
Read tool — multimodal vision (describe + OCR) |
.csv, .tsv, .json, .html, .ics, .eml |
Read tool — text formats |
For .docx, .pptx, .m4a/.mp3 — tell the user to convert first (pandoc, python-pptx, whisper) or use the CLI's llm-wiki import.
Frontmatter schema (every wiki page)
---
title: <Exact page title>
type: entity | concept | theme | comparison | synthesis
tags: [domain-tags]
sources: [raw/<filename>.md, ...]
created: YYYY-MM-DD
last_updated: YYYY-MM-DD
ttl: 90d | 180d | 365d | null
confidence: high | medium | low
---
Workflows
Ingest (a source was added to raw/)
- Read
CLAUDE.md,wiki/index.md,wiki/log.md, then the new raw file — in that order. - Extract 3-5 key takeaways from the raw file.
- Identify which existing wiki pages this source touches. Update them additively (preserve existing content).
- Create new entity/concept/theme pages only where the source supports real content (not single-mentions).
- Maintain
[[wikilinks]]and## Backlinkssections. When page A links to B, add A to B's Backlinks. - Update
wiki/index.mdwith any new pages. - Append a single dated entry to
wiki/log.md:## [YYYY-MM-DD] ingest | <source title> - Ingested raw/<file> - Pages touched: [[a]], [[b]] - New pages: [[c]] - Notes: <key facts>
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 First seen · 98 lines · 88 tokens per session scan A 1c2bd0957d47
wiki-compiler is a skill published in the GitHub repository doneyli/claude-code-plugins (4 stars, last pushed 4mo ago), licensed MIT. It adds 88 tokens to every session and 1,297 once invoked, about $0.0004 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.