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.
git clone --depth 1 https://github.com/endorphin-ai/hasbrains-agent-kitWrote 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/endorphin-ai/hasbrains-agent-kit/repo-to-course)<a href="https://agentmods.dev/commands/endorphin-ai/hasbrains-agent-kit/repo-to-course"><img src="https://agentmods.dev/badge/commands/endorphin-ai/hasbrains-agent-kit/repo-to-course/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/commands/endorphin-ai/hasbrains-agent-kit/repo-to-course"><img src="https://agentmods.dev/badge/commands/endorphin-ai/hasbrains-agent-kit/repo-to-course.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00024 | $0.03665 |
| Opus 5.5 | $0.00010 | $0.01466 |
| Sonnet 5.5 | $0.00005 | $0.00733 |
| Haiku 4.5 | $0.00002 | $0.00366 |
Grade A, and why
repo-to-course 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 — 513 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Repo → Course Generator
Turn the repository into a structured tutorial course. Work through the phases below in order — each phase's output feeds the next. Do not skip Phase 1 or 2; they are what keep the tutorials grounded in the real codebase.
Inputs
- Output format:
$1— one ofmarkdown,github-pages,html-interactive. If not provided, pick using this table (or ask if the audience is unclear):
| Scenario | Format | Why |
|---|---|---|
| Internal docs / wiki | markdown |
Paste into Notion, Confluence, GitHub wiki |
| Open-source project | github-pages |
Free hosting, auto-deploys, looks professional |
| Workshop / offline | html-interactive |
One file, works offline, share via Slack/email |
| Client deliverable | html-interactive |
Self-contained, impressive, no setup needed |
- Repo:
$2— path to the repository to teach. Defaults to the current working directory. - Any further arguments are customization instructions (see Customization Hooks at the end).
Shortcut for small repos (< 50 files): you may compress Phases 1–3 into a single pass, but still apply every rule below and still run Phase 4.
Phase 1: Repo Analysis
You are a senior developer educator preparing to build a tutorial course from a codebase. Analyze the repository thoroughly and produce a structured knowledge map.
Step 1 — File Inventory
List every file and directory. Classify each as:
- Core: Essential to understanding what the project does (main logic, models, routes, services)
- Supporting: Helpers, utilities, middleware, config, types
- Peripheral: Tests, CI/CD, docs, scripts, lockfiles
Step 2 — Architecture Map
- Identify the entry point(s) (e.g., main.go, index.ts, app.py)
- Trace the request/data flow from entry to output
- Map which files import/depend on which (dependency graph)
- Identify layers (e.g., HTTP → service → repository → database)
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 · 513 lines · 24 tokens per session scan A 79a691cc1265
repo-to-course is a command published in the GitHub repository endorphin-ai/hasbrains-agent-kit (9 stars, last pushed 15d ago), licensed MIT. It adds 24 tokens to every session and 3,665 once invoked, about $0.0001 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 2026-08-31.
Other commands, from other repositories
dare-welcome
Mostra o banner do DARE e um guia de início rápido com o fluxo Design → Architecture → Review → Execute.
audit-quiz-coverage
Find quiz coverage gaps from recent guide/CHANGELOG/CC-releases changes and propose new questions.
stats
Show real token usage, delegation rate, and actual savings for the current Hydra session (parses Claude Code session JSONL — no AI estimation).
debug
Systematic debugging with automated investigation and 4-phase methodology. Default: inline evidence gathering and diagnosis. --deep: spawns systematic-debugger agent for full autonomous debugging. Use for errors, stack traces, test failures, or unexpected behavior.
stitch-generate
Full Stitch pipeline — author prompts, generate screens via MCP, fetch images and code.
verify-upgrade
Verify Spring Boot project upgrade readiness with parallel multi-skill analysis.