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ml-llm-wiki

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.

Informações da origem

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sammcj/agentic-coding
Última atividade na origem
8 de setembro de 2026 às 00:44
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inglês
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162
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24

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SKILL.md
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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, audit, or critique, use the llm-wiki skill instead.
# 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 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. If a `local/` directory exists, search it too and fold in any relevant personal notes, labelling each hit as `local/ (uncommitted)` so it is never mistaken for shared, committed knowledge. `local/` is the user's own, gitignored and absent from the index. 4. 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)`. 5. 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`. 6. 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.
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