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 agentmods add skills/wardawgmalvicious/agent-config/learnnpx skills add wardawgmalvicious/agent-config --skill learngit clone --depth 1 https://github.com/wardawgmalvicious/agent-configWrote 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/wardawgmalvicious/agent-config/learn)<a href="https://agentmods.dev/skills/wardawgmalvicious/agent-config/learn"><img src="https://agentmods.dev/badge/skills/wardawgmalvicious/agent-config/learn.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00163 | $0.02128 |
| Opus 5 | $0.00081 | $0.01064 |
| Sonnet 5 | $0.00033 | $0.00426 |
| Haiku 4.5 | $0.00016 | $0.00213 |
Grade A, and why
learn 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 yesterday.
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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learn: capture session learnings into skills and rules
Turn something discovered during this session into a durable, verified edit to the guidance that should have covered it. The goal is that the next session never has to rediscover it.
This skill proposes; it does not commit. Edits land only after the
user approves the diff, and committing is handed to /commit.
The inverse skill is /author-skill: /learn folds a learning into
guidance that already exists, /author-skill creates guidance that has
no home yet. If the right destination for a learning turns out to be a
skill this repo does not have, that is an /author-skill job.
This repo lives at C:\Repos\Personal\agent-config; ~/.claude/skills
holds one junction per skill into it, so /learn may fire from a
session in any repo. All repo-relative paths and commands below (grep, the lint
script, /commit) run from that repo root, not the session's cwd.
Step 1 — Identify what was learned
Reflect on the session, not just the last message. Candidate learnings:
- A skill or rule said X; reality was Y (doc-vs-reality gap).
- A step was missing and cost debugging time.
- An error message whose cause was non-obvious.
- A constraint / limit / version change not documented anywhere here.
- A user correction that reveals a general principle.
For each candidate, state in one or two sentences: problem → root cause → correct approach → generalization. Drop anything that is one-off, already obvious from the code, or only matters to this conversation. Confirm the list with the user before proceeding if it contains more than one item or you're unsure which matters.
Step 2 — Identify which guidance was in use (automatic)
Do not ask the user which skill was used. Reconstruct it:
- Skills invoked this session — every
Skilltool call and every skill whose content appears in context (the<command-name>/ loaded-skill blocks). Record thename:of each. - Rules auto-loaded — any
rules/coding-*.mdcontent present in context, triggered by files in session scope (paths:globs). - Global CLAUDE.md or rule sections relied on — e.g. the
uvguidance (claude/CLAUDE.md) or the Fabric serialization rule (rules/fabric-git-serialization.md). - Tools used — MCP servers / CLIs (
fab,pbir, fabric-cicd, Fabric REST) point at the skill that owns them even if it wasn't explicitly invoked. Map by the skill'sdescription. - If nothing was loaded but a skill should have triggered, that is
itself a learning: the fix is the skill's
description(trigger phrases), not its body.
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.
- yesterday Changed · +3 lines fb9da53b374c
- 5d ago First seen · 178 lines · 163 tokens per session scan A 0ff1215fd3ed
learn is a skill published in the GitHub repository wardawgmalvicious/agent-config (1 stars, last pushed today), licensed MIT. It adds 163 tokens to every session and 2,128 once invoked, about $0.0008 per session on Opus 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 2026-08-31.
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