ai-ready-product-workflow-v2: Command for Claude Code

.claude/commands/pm-discovery.md

pm-discovery is a command for Claude Code from karlng279/ai-ready-product-workflow-v2. It costs 0 tokens per session (1,396 once invoked), scanned A, original, MIT.

A command for running a structured product-discovery session: a process for learning which user problem is worth solving before building a feature.

In plain words
What is it for?
Use `/pm-discovery` with a product or feature name to create a discovery document in `features/{feature-name}/pm/discovery.md`.
Why use it?
It helps turn an untested product idea into clear opportunities, assumptions, and experiments instead of jumping straight into requirements or code.

Command for Claude Code

Written for Claude Code: $ARGUMENTS substitution.

This is karlng279/ai-ready-product-workflow-v2's own configuration. It tells Claude Code how to work on ai-ready-product-workflow-v2 itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything ai-ready-product-workflow-v2 configures →

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/karlng279/ai-ready-product-workflow-v2/main/.claude/commands/pm-discovery.md
Clone the repo
git clone --depth 1 https://github.com/karlng279/ai-ready-product-workflow-v2

Made for: Claude Code.

Wrote 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.

agentmods badge for pm-discovery

README.md
[![agentmods](https://agentmods.dev/badge/commands/karlng279/ai-ready-product-workflow-v2/pm-discovery/github.svg)](https://agentmods.dev/commands/karlng279/ai-ready-product-workflow-v2/pm-discovery)
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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.

agentmods 80×15 button for pm-discovery

Your own site · 80×15
<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>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,396 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 11d ago against content hash 0d302a35049b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

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.

.claude/commands/pm-discovery.md · 164 lines

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):

  1. "What outcome are you trying to achieve? (business goal, not feature)"
  2. "What do you currently know about users' pain in this area? Any existing research or data?"
  3. "What are the key assumptions you're making that, if wrong, would invalidate this direction?"
  4. "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

Read the full file on GitHub · 164 lines

Changes

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.

  1. 11d ago First seen · 164 lines · 0 tokens per session scan A 0d302a35049b

Subscribe to this mod's changes

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.