learn

learn is a skill for Claude Code, Codex from berekvolgyipeter/dotclaude. It costs 119 tokens per session (2,628 once invoked), scanned A, original, MIT.

A learning workflow that examines feedback and mistakes from a conversation and updates project instructions, personal rules, skills, commands, or agents when a recurring improvement is justified.

In plain words
What is it for?
Use it after a correction or repeated failure to decide whether a lasting instruction should be added or revised.
Why use it?
It helps prevent the same misunderstanding or agent behavior from happening again by turning useful feedback into a documented rule.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/berekvolgyipeter/dotclaude/learn
Any agent
npx skills add berekvolgyipeter/dotclaude --skill learn
Clone the repo
git clone --depth 1 https://github.com/berekvolgyipeter/dotclaude

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for learn

README.md
[![agentmods](https://agentmods.dev/badge/skills/berekvolgyipeter/dotclaude/learn.svg)](https://agentmods.dev/skills/berekvolgyipeter/dotclaude/learn)
Your own site
<a href="https://agentmods.dev/skills/berekvolgyipeter/dotclaude/learn"><img src="https://agentmods.dev/badge/skills/berekvolgyipeter/dotclaude/learn.svg" alt="Measured on agentmods" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,628 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00119 $0.02628
Opus 5 $0.00060 $0.01314
Sonnet 5 $0.00024 $0.00526
Haiku 4.5 $0.00012 $0.00263

Measured 3d ago against content hash 651ba3394d6a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 3d 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.

skills/learn/SKILL.md · 190 lines

How it starts

The opening of the file, as written. The whole thing — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Triage first

Before diving into analysis, check if there's anything to learn. Most conversations have zero learnings; a few have one or two. Fabricating learnings from uneventful interactions creates noise that drowns out real insights — say so and stop if the conversation was routine.

A single offhand correction in an otherwise smooth conversation may not warrant a rule. Focus on patterns that would recur across future sessions.

Analysis Process

Learnings live at two levels:

  • User-level (~/.claude/) — shared across all projects (rules, skills, commands, agents, output styles, templates)
  • Project-level (.claude/) — specific to the current project (CLAUDE.md, rules, skills, commands, agents)

Step 1: Scan for Learnings

Examine the conversation for learning signals and identify rule gaps in a single pass:

  • User corrections: "Actually, you should...", "No, the correct way is...", "Don't do X, do Y instead" — includes cases where the user had to repeat themselves or rephrase
  • Agent mistakes: Repeated errors, wrong assumptions, misunderstandings, or violations of existing rules (needs new rule or emphasis/clarification of existing one)
  • Emergent patterns: Something worked well that should be documented, or a workflow crystallized that future sessions would benefit from. Positive learnings go to the same target files as fixes — add them as recommended patterns rather than corrective rules.
  • Tool invocations: Instances of Skill tool calls (skills/commands) and Task tool calls (agents) — these feed into Step 2

Step 2: Evaluate Used Skills, Commands, and Agents

Scan the conversation for any invocations of custom tools:

SkillsSkill tool calls where the skill parameter matches a name in ~/.claude/skills/ or .claude/skills/

Commands — slash command invocations like /fix-review, /to-plan, etc., which expand via the Skill tool

AgentsTask tool calls where subagent_type matches a name in ~/.claude/agents/ or .claude/agents/

Read the full file on GitHub · 190 lines

Changes

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.

  1. 3d ago First seen · 190 lines · 119 tokens per session scan A 651ba3394d6a

Subscribe to this mod's changes

learn is a skill published in the GitHub repository berekvolgyipeter/dotclaude (2 stars, last pushed 22d ago), licensed MIT. It adds 119 tokens to every session and 2,628 once invoked, about $0.0006 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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