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 commands/osamaatef1/claude-learn-plugin/learn-savegit clone --depth 1 https://github.com/osamaatef1/claude-learn-pluginWrote 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/commands/osamaatef1/claude-learn-plugin/learn-save)<a href="https://agentmods.dev/commands/osamaatef1/claude-learn-plugin/learn-save"><img src="https://agentmods.dev/badge/commands/osamaatef1/claude-learn-plugin/learn-save.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 | $0.00000 | $0.00580 |
| Opus 5 | $0.00000 | $0.00290 |
| Sonnet 5 | $0.00000 | $0.00116 |
| Haiku 4.5 | $0.00000 | $0.00058 |
Grade A, and why
learn-save 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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/learn-save — Cache Codebase Analysis
You are a codebase analysis expert. Your job is to thoroughly analyze this project and save the results to CODEBASE.md so future /learn and /learn-topic sessions can skip the exploration phase entirely.
Step 1 — Full Codebase Exploration
Silently analyze the entire project:
- Read the root directory structure (all top-level files and folders)
- Read all manifest files:
package.json,pyproject.toml,Cargo.toml,go.mod,composer.json, etc. - Read
README.mdif present - Identify the primary language(s) and framework(s)
- Identify all entry points (e.g.,
main.py,index.ts,app.js,main.go) - Read every significant source file (skip generated files, lock files, and vendored dependencies)
- Identify configuration files (
.env.example,docker-compose.yml, CI configs, etc.) - Map out the full architecture: folders, layers, patterns, data flow
- Identify the 5–8 most important files and their responsibilities
- Note any non-obvious design decisions, patterns, or gotchas
Step 2 — Write CODEBASE.md
Write a CODEBASE.md file in the project root with the following structure:
# Codebase Analysis
<!-- Generated by /learn-save. Used by /learn and /learn-topic to skip re-exploration. -->
## Project Purpose
[What problem this project solves. Who uses it. Why it exists.]
## Language & Frameworks
[Primary language(s), frameworks, and key libraries]
## Architecture
[The architectural pattern in use. An ASCII diagram if helpful. Why this structure was chosen.]
## Folder Structure
[Each top-level folder and its role]
## Entry Points
[The main entry files and what they do]
## Key Files
[The 5–8 most important files, each with: path, single responsibility, what depends on it, what it depends on]
## Data Flow
[How a typical request/operation flows through the system end to end]
## Core Patterns & Concepts
[The 2–4 most important design patterns or abstractions in use, with brief explanations]
## Configuration
[Key config files and environment variables]
## Gotchas & Non-Obvious Details
[Anything a new developer would likely stumble on]
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.
- 3d ago First seen · 69 lines · 0 tokens per session scan A c5adf0f0ae7f
learn-save is a command published in the GitHub repository osamaatef1/claude-learn-plugin (3 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 580 tokens. 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.
Other commands, from other repositories
review
Cold re-quiz on code that already shipped — your own session commits, not the change in front of you.
learn-story-flow
Learn story-flow concepts with interactive guidance for junior developers.
no-vibe
Enter no-vibe mode in OpenCode (tutor mode, no direct project file writes).
teach-me-testing
Teach testing progressively through structured sessions. Use when user says ""lets learn testing"" or ""I want to study test practices"".
setup-bigquery.es
Command "setup-bigquery.es" from minicoohei/ai-agent-camp, covering configuración de autenticación bigquery / gcp, step 0: verificar el progreso de configuración, lo que hará en esta sesión, verificación de preparación and step 1: instalación de gcloud cli.
setup-content
Lesson command — 教材コンテンツの初回セットアップ.