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/jonase47/ccprWrote 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/jonase47/ccpr/lean-learn)<a href="https://agentmods.dev/commands/jonase47/ccpr/lean-learn"><img src="https://agentmods.dev/badge/commands/jonase47/ccpr/lean-learn/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/jonase47/ccpr/lean-learn"><img src="https://agentmods.dev/badge/commands/jonase47/ccpr/lean-learn.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 | $0.00000 | $0.01654 |
| Opus 5.5 | $0.00000 | $0.00662 |
| Sonnet 5.5 | $0.00000 | $0.00331 |
| Haiku 4.5 | $0.00000 | $0.00165 |
Grade A, and why
lean-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 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/lean-learn – Validate Lean-Track and make a decision
Creates docs/LEARNINGS.md with hypothesis check, observations, and decision
(PROMOTE / PIVOT-Soft / PIVOT-Hard / DROP). Closes a Lean cycle
and determines what happens next.
Argument: $ARGUMENTS = [optional: projectdir]
- Without argument: operates on
$(pwd).
Prerequisites
docs/FRAME.mdexists with hypothesis- Code exists in the repo (otherwise there is nothing to learn from)
- Template:
~/.claude/templates/LEARNINGS_TEMPLATE.md
Lead Agent
konzeptor for hypothesis analysis, business-analyst as support for market/user signals.
Execution
1. Prerequisites check
- If
docs/FRAME.mdis missing → STOP, note: run/lean-framefirst - If no code files exist in
src/(or repo root) → warn, continue with user confirmation
2. Load hypothesis from FRAME
Read docs/FRAME.md section 2 (hypothesis) and section 7 (risk assumptions: what must work without fail?). List them for the user.
3. Walk through validation interactively
Ask interactively:
-
Hypothesis check: Is the hypothesis confirmed, refuted, or unclear?
- Evidence: What concrete data points/observations/user reactions are available? (Not "felt", but concrete.)
-
What works? (factual observations, each with a brief rationale)
-
What does not work? (observations with suspected cause)
-
Surprises: What was unexpected? (If nothing: record explicitly — the hypothesis was probably too weak.)
4. Make a decision
Present 4 options:
| Option | When appropriate |
|---|---|
| PROMOTE | Hypothesis confirmed; prototype becomes the basis for Full-Track |
| PIVOT-Soft | New hypothesis on the same tech base; most code stays |
| PIVOT-Hard | Wrong core assumption (tech, market, user); code reset required |
| DROP | Lean answered the question (negatively); project is frozen |
5. On PIVOT (Soft or Hard): module table
Ask per module/directory:
- keep — functionally correct, hypothesis-independent
- refactor — logic OK, interface must change
- rebuild — wrong core assumption, rewrite
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 Changed c7b6767ccebb
- 1mo ago Changed 264d98b76795
- 1mo ago Changed · +3 lines 7737f7664f8f
- 1mo ago First seen · 144 lines · 0 tokens per session scan A fc01acc9ccef
lean-learn is a command published in the GitHub repository jonase47/ccpr (1 stars, last pushed 24d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,654 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
stats
Show real token usage, delegation rate, and actual savings for the current Hydra session (parses Claude Code session JSONL — no AI estimation).
map
View, rebuild, or query the codebase dependency map.
design-intent
Guided UI/visual development with structured exploration, architecture design, and quality review.
ralph
Ralph autonomous development loop with fresh context per iteration.
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.
double-check
Comprehensive verification with automated quality gates, confession gathering, and confidence-based issue filtering. Default: self-review with gates. --deep: multi-agent review via code-reviewer agent. Use before committing, after fixing bugs, or to validate feature completeness.