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 instructions/mit-han-lab/kernelwiki/claude-mdgit clone --depth 1 https://github.com/mit-han-lab/KernelWikiWhat 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 | $0.01556 | $0.01556 |
| Opus 5 | $0.00778 | $0.00778 |
| Sonnet 5 | $0.00311 | $0.00311 |
| Haiku 4.5 | $0.00156 | $0.00156 |
Grade A, and why
KernelWiki CLAUDE.md 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 3d 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Blackwell Kernel Optimization Knowledge Base — Schema
A structured knowledge base of GPU kernel optimization for NVIDIA Blackwell (SM100) and Hopper (SM90), optimized for LLM agent retrieval.
Navigation
Recommended: use the skill scripts
python3 scripts/query.py "<keywords>" [--tag X --type Y]
python3 scripts/get_page.py <id-or-path> [--follow-sources]
python3 scripts/grep_wiki.py "<pattern>" [--only wiki]
See SKILL.md (skill entry point) and references/examples.md for query patterns.
Direct navigation (when skill not available)
- Start: Read
index.mdfor curated top-level navigation - By architecture: Read
queries/by-architecture.md→ choose exact, family-only, or validated-unknown evidence - By problem: Read
queries/by-problem.md→ find symptom → follow links to pattern pages - By technique: Read
queries/by-technique.md→ find technique → follow to technique page - By hardware: Read
queries/by-hardware-feature.md→ find feature → follow to hardware page - By kernel type: Read
queries/by-kernel-type.md→ find type → follow to kernel pages - By language: Read
queries/by-language.md→ find DSL → follow to language page - By repo: Read
queries/by-repo.md→ find repository → see all PRs - Deep dive: From any wiki page, follow
sources:IDs to raw source data
Three-Layer Architecture
Layer 1: Sources (sources/)
Raw data. Each file has YAML frontmatter with a unique id.
sources/prs/{repo}/PR-{N}.md— One file per relevant PRsources/contests/{contest}/*.md— Competition problem definitions, organizer speed-of-light data, and dated leaderboard/results snapshots (no per-submission solutions)sources/docs/*.md— Official-document and research-paper summaries (source_category: official-docorpaper);sources/docs/images/holds reproduced figure assetssources/blogs/*.md— Community blog post summaries
Layer 2: Wiki (wiki/)
Synthesized knowledge pages. Cross-referenced by id.
wiki/hardware/— Hardware feature pages (tcgen05, TMEM, CLC, TMA, etc.)wiki/techniques/— Optimization techniques (warp specialization, pipelining, etc.)wiki/patterns/— Problem → solution diagnosis (low SM util → CLC, etc.)wiki/kernels/— Kernel case studies (FlashAttention-4, DeepGEMM, etc.)wiki/languages/— DSL/language guides (CuTe DSL, Triton, CUDA C++, PTX)wiki/migration/— Hopper → Blackwell migration patterns
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.
- 3d ago First seen · 122 lines · 1,556 tokens per session scan A bb104cd6faa9
KernelWiki CLAUDE.md is an instructions file published in the GitHub repository mit-han-lab/KernelWiki (419 stars, last pushed 7d ago), licensed MIT. It adds 1,556 tokens to every session, about $0.0078 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.
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kicad-happy GEMINI.md
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brilliant_sdk AGENTS.md
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