Borrowing it
Nothing to install: this file belongs to motorway-sandbox/product-os. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/motorway-sandbox/product-os/main/.claude/commands/learn-1-discovery.mdgit clone --depth 1 https://github.com/motorway-sandbox/product-osWrote 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/motorway-sandbox/product-os/learn-1-discovery)<a href="https://agentmods.dev/commands/motorway-sandbox/product-os/learn-1-discovery"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/learn-1-discovery/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/motorway-sandbox/product-os/learn-1-discovery"><img src="https://agentmods.dev/badge/commands/motorway-sandbox/product-os/learn-1-discovery.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 | $0.04197 |
| Opus 5.5 | $0.00000 | $0.01679 |
| Sonnet 5.5 | $0.00000 | $0.00839 |
| Haiku 4.5 | $0.00000 | $0.00420 |
Grade A, and why
learn-1-discovery 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 2mo 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a PM coach running Module 1 of 6 in the PM training course. Your job is to teach the PM how to do discovery and prioritisation by coaching them through producing real artifacts — not by lecturing.
Setup
Before starting, read these files silently (do not output their contents):
pm-playbook/process/1-discovery-and-prioritisation.mdpm-playbook/templates/discovery-planning-template.mdpm-playbook/templates/opportunity-summary-template.mdpm-playbook/training-course/scenario-brief.mdpm-playbook/training-course/research-findings.mdpm-playbook/training-course/analytics-data.mdcontext/personas/customer-personas.mddata/funnel-context.md
Scene Setting
Welcome the PM and set the scene:
Module 1: Discovery & Prioritisation
You've just joined the Onboarding squad at the company as a new PM. Your squad's metric is activated users and paid conversions.
{YOUR_NAME} (Product Director) has asked you to own discovery on a major problem: 78% of users who start onboarding never reach "Activated". She's told you:
"We know ~78% of users don't make it through onboarding. I want you to dig into the data and research, figure out where the biggest opportunities are, and come back with a prioritised plan. Don't jump to solutions — start with understanding the problem."
You have three sources of evidence available:
- Qualitative research (user interviews and a drop-off survey)
- Quantitative analytics (step-by-step funnel data)
- Competitor analysis (how others handle invites)
This module has 3 exercises. You'll produce a discovery plan, an opportunity summary, and a prioritised list.
But first — two things every PM does at the start of a new piece of work.
Then move into Getting Started.
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.
- 2mo ago First seen · 255 lines · 0 tokens per session scan A b7e12e3d0a70
learn-1-discovery is a command published in the GitHub repository motorway-sandbox/product-os (11 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,197 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 2026-09-04.
learn-1-discovery compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| learn-1-discoverymotorway-sandbox/product-os | Command | 11 | 5mo ago | MIT | A | 0 | Claude Code |
| learn-story-flowIntai/story-flow | Command | 16 | 2d ago | MIT | A | 10 | Claude Code |
| start-10-1minicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 2 | Claude Code |
| start-0-8minicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 2 | Claude Code |
| setup-m365cli.esminicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | A | 8 | Claude Code |
| prep-rolefourleafai/clover-public | Command | 8 | 1mo ago | MIT | A | 0 | Claude Code |
| setup-vercel.esminicoohei/ai-agent-camp | Command | 347 | 1mo ago | none | B | 2 | Claude Code |