---
id: sammcj/agentic-coding/examples
version: "08c44486"
license: Apache-2.0
install: manual
updated: 2026-07-27
---

# ml-llm-wiki — Search a self-contained markdown wiki covering transformer architectures, attention cost and efficiency, and long-context scaling. The skill provides read-only access to pre-compiled articles; use the companion llm-wiki skill to add or update content.

Publisher: sammcj · Stars: 153 · Updated: 2026-07-27

Install (manual): `git clone https://github.com/sammcj/agentic-coding`

## SKILL.md

# 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.


[View on SkillFed](https://skillfed.io/sammcj/agentic-coding/examples) · [View on GitHub](https://github.com/sammcj/agentic-coding)