Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sammcj/agentic-coding --skill examplesgit clone --depth 1 https://github.com/sammcj/agentic-codingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sammcj/agentic-coding/examples)<a href="https://agentmods.dev/skills/sammcj/agentic-coding/examples"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/examples/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sammcj/agentic-coding/examples"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/examples.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00080 | $0.00765 |
| Opus 5.5 | $0.00032 | $0.00306 |
| Sonnet 5.5 | $0.00016 | $0.00153 |
| Haiku 4.5 | $0.00008 | $0.00076 |
Grade A, and why
ml-llm-wiki scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 40 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- CLAUDE.md 2.2 KB
- local/prep/2026-06-04-attention-talk-prep.md 1.2 KB
- raw/machine-learning/2017-06-12-attention-is-all-you-need.md 1.7 KB
- raw/machine-learning/2022-05-27-flashattention.md 1.5 KB
- README.md 2.9 KB
- wiki/gaps.md 626 B
- wiki/index.md 855 B
- wiki/log.md 1.2 KB
- wiki/machine-learning/attention-cost.md 1.5 KB
- wiki/machine-learning/attention-efficiency.md 2.0 KB
- wiki/machine-learning/transformer-architectures.md 1.9 KB
- wiki/machine-learning/why-transformers-scale.md 2.5 KB
- wiki/README.md 2.7 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 40 lines · 80 tokens per session scan A fc14c58aea82
ml-llm-wiki is a skill published in the GitHub repository sammcj/agentic-coding (164 stars, last pushed 3d ago), licensed Apache-2.0. It adds 80 tokens to every session and 765 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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