Borrowing it
Nothing to install: this file belongs to karlng279/ai-ready-product-workflow-v2. 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/karlng279/ai-ready-product-workflow-v2/main/.claude/commands/pm-discovery.mdgit clone --depth 1 https://github.com/karlng279/ai-ready-product-workflow-v2Wrote 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/karlng279/ai-ready-product-workflow-v2/pm-discovery)<a href="https://agentmods.dev/commands/karlng279/ai-ready-product-workflow-v2/pm-discovery"><img src="https://agentmods.dev/badge/commands/karlng279/ai-ready-product-workflow-v2/pm-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/karlng279/ai-ready-product-workflow-v2/pm-discovery"><img src="https://agentmods.dev/badge/commands/karlng279/ai-ready-product-workflow-v2/pm-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 | $0.01396 |
| Opus 5 | $0.00000 | $0.00698 |
| Sonnet 5 | $0.00000 | $0.00279 |
| Haiku 4.5 | $0.00000 | $0.00140 |
Grade A, and why
pm-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 11d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/pm-discovery
Start a product discovery sprint session. Maps opportunities, assumptions, and experiments using Teresa Torres' Continuous Discovery framework.
Usage
/pm-discovery [product or feature name]
If a product/feature name is provided via $ARGUMENTS, use it. Otherwise ask: "What opportunity or problem are we discovering for?"
What This Command Does
Activates the pm-product-discovery skill and guides a structured discovery session. The output is a discovery artifact stored in features/{feature-name}/pm/discovery.md.
This command is most useful:
- After
/pm-strategy(to validate strategic assumptions) - Before
/po-pipeline(to ground requirements in validated user needs) - When you have a hypothesis but need to structure what to test
Session Flow
Step 1 — Discovery Context
Ask the user (sequentially):
- "What outcome are you trying to achieve? (business goal, not feature)"
- "What do you currently know about users' pain in this area? Any existing research or data?"
- "What are the key assumptions you're making that, if wrong, would invalidate this direction?"
- "Have you talked to any users? If yes, what did you hear?"
Accept incomplete answers. The Opportunity Solution Tree will surface gaps.
Step 2 — Opportunity Solution Tree (Teresa Torres)
Activate pm-product-discovery skill. Read pm-framework/product-discovery/rules.md before generating output.
Build the OST in this structure:
Desired Outcome (business metric)
└── Opportunity 1: [unmet user need / pain / job]
├── Opportunity 1a: [more specific need]
│ ├── Solution A: [potential solution]
│ └── Solution B: [potential solution]
└── Opportunity 1b: [more specific need]
└── Solution C: [potential solution]
└── Opportunity 2: [unmet user need]
└── ...
Rules for the OST:
- Opportunities = user needs, pains, or desires (not solutions, not features)
- Solutions = concrete product ideas that address one or more opportunities
- Start broad, then drill down to specific sub-opportunities
- Aim for 2–4 top-level opportunities, 2–3 sub-opportunities each
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.
- 11d ago First seen · 164 lines · 0 tokens per session scan A 0d302a35049b
pm-discovery is a command published in the GitHub repository karlng279/ai-ready-product-workflow-v2 (6 stars, last pushed 7d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,396 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
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.