ml-llm-wiki

name	ml-llm-wiki
description	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, or audit, use the llm-wiki skill instead.
context	fork
allowed-tools	Read Grep Glob Agent

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 - ingesting sources, superseding stale knowledge, linting, auditing - 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

1. Read wiki/index.md - the catalogue, grouped by topic. Start here to find relevant articles.
2. 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).
3. 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).
4. If a cited article has status: stale, say so and point to its replacement. Here, attention-cost.md is stale and superseded by attention-efficiency.md.
5. 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, or audit, 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.