How do I install the Ml LLM Wiki skill?

Run npx skillmds add sammcj/ml-llm-wiki in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Ml LLM Wiki safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Ml LLM Wiki?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Ml LLM Wiki free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Ml LLM Wiki?

sammcj (@sammcj) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Ml LLM Wiki by sammcj — Agent Skill

Name: Ml LLM Wiki
Rating: 4.2 (5 reviews)
Author: sammcj

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.

sammcj 6ff7113 14 files · 26.2 KB Updated Sep 9, 2026

File contents

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 as local/ (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.md is stale and superseded by attention-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.

---name: ml-llm-wikidescription: 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.

Frequently asked questions