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.
npx skills add karlng279/ai-ready-product-workflow-v2 --skill pm-product-discoverygit 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/skills/karlng279/ai-ready-product-workflow-v2/pm-product-discovery)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.01737 |
| Opus 5 | $0.00000 | $0.00869 |
| Sonnet 5 | $0.00000 | $0.00347 |
| Haiku 4.5 | $0.00000 | $0.00174 |
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.
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 standardstemplates/opportunity-solution-tree.md— OST templatetemplates/customer-interview-guide.md— JTBD interview guidetemplates/assumption-map.md— Assumption mapping templateexamples/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
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.
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.
- 13d ago First seen · 185 lines · 0 tokens per session scan A 43ae8824e0cb
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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