ml-llm-wiki
Skill167 starsChanged 10 days agoApache-2.0View on GitHub
Use when answering questions from this machine-learning knowledge base. Triggers: questions about transformers, attention cost and efficiency, and long-context scaling; 'what do we know about attention', 'check the ML wiki'. Read-only querying of compiled knowledge; to add, update, supersede, lint, audit, or critique, use the llm-wiki skill instead.
We split this skill into the rules it gives an agent
7 rules. About 0 are common, 1 is distinctive, and 6 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
6
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 10
Keep this current: as the wiki grows, update the name and description above so they describe what it actually covers and trigger on the …
- Very specific: line 22
Read wiki/index.md - the catalogue, grouped by topic.
- Very specific: line 23
Read the articles it points to.
- Very specific: line 24
If a local/ directory exists, search it too and fold in any relevant personal notes, labelling each hit as local/ (uncommitted) so it is …
- Very specific: line 26
If a cited article has status: stale, say so and point to its replacement.
- Very specific: line 27
If the wiki has no answer, check wiki/gaps.md - the question may already be a tracked gap.
- Distinctive: line 39
Use sub-agents with well defined goals, scope and context to parallelise work and reduce context rot in the main conversation.
≈ “Common” is an estimate: at least 1 in 5 of the 308,232 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Machine Learning Wiki
A self-contained markdown knowledge base on transformer architectures, attention cost and efficiency, and long-context scaling. This skill is for querying it: the knowledge is already compiled into articles under wiki/, so read those rather than re-deriving from scratch.
Keep this current: as the wiki grows, update the name and description above so they describe what it actually covers and trigger on the right questions.
(Sample note: this example wiki lives in examples/ within the llm-wiki repo. To load it as a skill, place the directory in your skills path named ml-llm-wiki, so the directory matches the name above.)
Maintenance and deeper analysis - ingesting sources, superseding stale knowledge, linting, auditing, critiquing reasoning - is not done here. Use the llm-wiki skill, which owns the write workflow and the file format. The llm-wiki skill is required to keep this wiki current; without it the wiki is still readable, but do not hand-edit articles outside the conventions in wiki/README.md.
What's inside
One topic so far, machine-learning: how attention works, why its memory cost was once thought to be a hard quadratic limit and why that turned out to be an implementation artefact, and what makes long context practical.
How to query
- Read
wiki/index.md- the catalogue, grouped by topic. Start here to find relevant articles. - Read the articles it points to. Follow body links for related material;
grep -rl "<article>.md" wiki/lists pages that link to a given article (backlinks). - If a
local/directory exists, search it too and fold in any relevant personal notes, labelling each hit aslocal/ (uncommitted)so it is never mistaken for shared, committed knowledge.local/is the user's own, gitignored and absent from the index. - Answer from the wiki's content in preference to general knowledge. Cite articles with markdown links, e.g.
[Attention Efficiency](wiki/machine-learning/attention-efficiency.md). - If a cited article has
status: stale, say so and point to its replacement. Here,attention-cost.mdis stale and superseded byattention-efficiency.md. - If the wiki has no answer, check
wiki/gaps.md- the question may already be a tracked gap. Recording a new gap is a write, so it goes through the llm-wiki skill, not here.
Conventions
wiki/README.md explains the format - frontmatter, the raw/wiki split, and supersession-not-deletion - for anyone reading without a skill. Articles carry status: current | stale; stale pages are kept on purpose and point at their replacement.
Updating
To add a source, change an article, supersede knowledge, lint, audit, or critique, invoke the llm-wiki skill. It is required for all writes and keeps the format consistent. This skill deliberately does not modify the wiki.
Tips
- Use sub-agents with well defined goals, scope and context to parallelise work and reduce context rot in the main conversation.
In this repository71 other files it gives its agents.
- Rules/CLAUDE.mdCLAUDE.md
- Skills/ambitious-model-use/CLAUDE.mdCLAUDE.md
- Skills/authoring-claude-md/CLAUDE.mdCLAUDE.md
- Skills/bento-slides/CLAUDE.mdCLAUDE.md
- Skills/extract-wisdom/CLAUDE.mdCLAUDE.md
- Skills/ghostty-config/CLAUDE.mdCLAUDE.md
- Skills/llm-wiki/CLAUDE.mdCLAUDE.md
- Skills/llm-wiki/examples/CLAUDE.mdCLAUDE.md
- Skills/openspec-bootstrap/CLAUDE.mdCLAUDE.md
- Skills/openspeccer/CLAUDE.mdCLAUDE.md
- Skills/rewrite-slop/CLAUDE.mdCLAUDE.md
- Skills/skill-creator-primer/CLAUDE.mdCLAUDE.md
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Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

