pm-product-discovery

pm-product-discovery is a skill for Claude Code, Codex from karlng279/ai-ready-product-workflow-v2. It costs 0 tokens per session (1,737 once invoked), scanned A, original, MIT.

A product-discovery guide for learning what customers need before deciding what to build. It uses methods such as Jobs-to-be-Done, which examines the task a customer is trying to accomplish, and opportunity trees that connect needs to possible solutions.

In plain words
What is it for?
Use it for customer research, interview planning, opportunity-solution trees, assumption mapping, and experiment design.
Why use it?
It helps avoid jumping from a vague idea straight to a feature. It gives teams a way to map customer problems, assumptions, and small tests before committing to development.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it for customer research, interview planning, opportunity-solution trees, assumption mapping, and experiment design.

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Install with agentmods
npx agentmods add skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery
Install

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.

Any agent
npx skills add karlng279/ai-ready-product-workflow-v2 --skill pm-product-discovery
Clone the repo
git clone --depth 1 https://github.com/karlng279/ai-ready-product-workflow-v2

Made for: Claude Code, Codex.

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-product-discovery

README.md
[![agentmods](https://agentmods.dev/badge/skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery/github.svg)](https://agentmods.dev/skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery)
Your own site
<a href="https://agentmods.dev/skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery"><img src="https://agentmods.dev/badge/skills/karlng279/ai-ready-product-workflow-v2/pm-product-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.

agentmods 80×15 button for pm-product-discovery

Your own site · 80×15
<a href="https://agentmods.dev/skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery"><img src="https://agentmods.dev/badge/skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,737 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.01737
Opus 5 $0.00000 $0.00869
Sonnet 5 $0.00000 $0.00347
Haiku 4.5 $0.00000 $0.00174

Measured 13d ago against content hash 43ae8824e0cb, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

pm-product-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 13d 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.

skills/pm-product-discovery/SKILL.md · 185 lines

How it starts

The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.

pm-product-discovery

You are an expert product discovery practitioner trained in Teresa Torres's Continuous Discovery Habits methodology. When this skill is active, apply the methodology below to all discovery-related work.

Knowledge Base

Full rules, templates, and examples live in pm-framework/product-discovery/:

  • rules.md — mandatory rules and quality standards
  • templates/opportunity-solution-tree.md — OST template
  • templates/customer-interview-guide.md — JTBD interview guide
  • templates/assumption-map.md — Assumption mapping template
  • examples/example-ost.md — Worked example (ShipTrack: D30 retention opportunity)

Always read the relevant file before producing an artifact.


Core Methodology

The Opportunity Solution Tree (Teresa Torres)

The OST is the primary discovery artifact. Structure:

Desired Outcome (the business metric you want to move)
└── Opportunity 1 (unmet customer need / pain / desire)
    ├── Solution A (potential way to address the opportunity)
    │   └── Experiment (how to test Solution A before building)
    └── Solution B
        └── Experiment
└── Opportunity 2
    └── ...

Rules:

  • Desired Outcome first — never start with a solution. The outcome is a business metric (e.g., "Increase D30 retention from 38% to 60%").
  • Opportunities are customer needs, not features. Written from the customer's perspective: "I don't know a shipment is delayed until the customer calls me."
  • Solutions are hypotheses, not commitments. Each solution must be paired with at least one experiment before building.
  • Experiments before engineering — every solution needs a test that validates the core assumption before writing production code.

Continuous Discovery Cadence (Teresa Torres)

  • Weekly customer interviews (minimum 1 per week, ideally 2–3)
  • Interviews are for opportunity mining, not solution validation
  • Each interview adds to the opportunity space — do NOT ask customers to validate your solutions
  • Run experiments in parallel with interviews — discovery never stops

Read the full file on GitHub · 185 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 13d ago First seen · 185 lines · 0 tokens per session scan A 43ae8824e0cb

Subscribe to this mod's changes

pm-product-discovery is a skill published in the GitHub repository karlng279/ai-ready-product-workflow-v2 (6 stars, last pushed 8d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,737 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.

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