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GitHub stars License: CC BY-NC-SA 4.0 PRs Welcome Version Claude Code Plugin Skills
用 Codex 或 Claude 安装复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它先审查 Skill 页面再帮你安装。
复制前请先查看来源、License 和安全提示。
来源文件:README.md
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║ 70 battle-tested skills + 6 command workflows ║
║ Claude Code • Cursor • Codex • n8n • OpenClaw • and more ... ║
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║ v0.83 • July 17, 2026 • CC BY-NC-SA 4.0 ║
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70 battle-tested PM frameworks, ready for Claude, Codex, ChatGPT, and any agent that can read structured knowledge.
Generic AI output is a PM's worst enemy. When you tell your agent "write a PRD" without shared context, you get a generic document that no stakeholder trusts and no engineer can act on.
This library gives both you and your AI agent the same professional foundation: the why behind each framework, the failure modes to avoid, and the judgment to apply them correctly. You stop repeating yourself. Your agent stops guessing. The work gets better.
The goal is dual — functional and pedagogic in equal measure. Skills equip agents to do PM work at a professional level, and they teach the human PM the reasoning behind each framework — so you can explain it, adapt it, and pass it on. Neither is a byproduct of the other.
Navigate by what you're actually trying to accomplish:
Framing and strategy
Stakeholder alignment
Customer discovery and research
Prioritization and roadmapping
Writing PM deliverables
Validation and experimentation
Finance and growth
Market and competitive intelligence
Career and leadership transitions
AI product work
Choose your setup:
| I use... | Get this | Notes |
|---|---|---|
| Claude Desktop or Claude Web | pm-skills-starter-pack.zip | Unzip, then upload the individual skill ZIPs to Claude Skills |
| Claude Code | Plugin marketplace | claude /plugin marketplace add deanpeters/Product-Manager-Skills |
| Codex | pm-skills-codex.zip | Installs .agents/skills and AGENTS.md |
| Not sure | pm-skills-starter-pack.zip | Start here |
All downloads: GitHub Releases
Each pack below is a ZIP of upload-ready skill ZIPs — unzip, then upload individuals to Claude Skills:
| Pack | Download | What's inside |
|---|---|---|
| Starter | pm-skills-starter-pack.zip | Core skills across all categories |
| Discovery | 02-discovery-pack.zip | Research, interviewing, synthesis |
| Strategy | 03-strategy-pack.zip | Positioning, roadmapping, prioritization |
| Delivery | 04-delivery-pack.zip | PRDs, stories, epics |
| AI PM | 05-ai-pm-pack.zip | Context engineering, orchestration, readiness |
| Market Intel | 06-market-intel-pack.zip | The full Market Intelligence Suite: disciplines, investigation chain, frameworks, monitors |
| All skills | 99-all-skills-pack.zip | All 70 skills |
Not ready to wire skills into your agent setup? Run the local playground first and kick the tires in your browser.
pip install -r app/requirements.txt
streamlit run app/main.py
What you can do:
Multi-provider support: Anthropic, OpenAI, Ollama. API keys via environment variables only (no in-app key entry).
Docs: app/STREAMLIT_INTERFACE.md · app/.env.example
Feedback welcome via GitHub Issues or LinkedIn.
Skills are organized in three tiers that build on each other:
┌────────────────────────────────────────────────────────┐
│ WORKFLOW SKILLS (19) │
│ Complete end-to-end PM processes (days to weeks) │
│ Example: run a full discovery cycle or write a PRD │
└────────────────────────────────────────────────────────┘
↓ orchestrates
┌────────────────────────────────────────────────────────┐
│ INTERACTIVE SKILLS (27) │
│ Guided discovery — 3-5 questions, then recommendations│
│ Example: "Which prioritization framework fits here?" │
└────────────────────────────────────────────────────────┘
↓ uses
┌────────────────────────────────────────────────────────┐
│ COMPONENT SKILLS (24) │
│ Templates for specific PM deliverables (30-90 min) │
│ Example: write a user story with acceptance criteria │
└────────────────────────────────────────────────────────┘
Interactive skills use an Adaptive Decision Ladder. Instead of dumping a framework at you, an interactive skill asks 3-5 targeted questions about your specific context, then offers numbered recommendations — each with a clear "use this when" rationale. You pick a path. The skill executes it and explains the why as it goes. If you want to just learn the framework without doing the work, you can ask that too — the skill coaches you either way. This is ABC — Always Be Coaching — in practice.
Full catalog: catalog/INDEX.md — all 70 skills with descriptions, or browse skills/ directly.
Every SKILL.md follows the same structure:
| Section | What it contains |
|---|---|
| Frontmatter | name, description, type, intent, best_for, scenarios |
| Purpose | What this skill does and when to reach for it |
| Input | What you can bring (with example invocations) — inline input is used, not re-asked, and arriving empty-handed is fine: the skill walks you through it |
| Key Concepts | Frameworks, definitions, anti-patterns — with vocabulary explained |
| Application | Step-by-step instructions an agent (or human) can follow |
| Examples | Real-world cases showing both good and bad versions |
| Common Pitfalls | Named failure modes with consequences and corrections |
| References | Related skills and external frameworks |
The best_for frontmatter field lists 3-5 specific scenarios where the skill is most useful — helpful for quickly scanning whether a skill fits your situation.
Why no $ARGUMENTS templating? Other skill libraries use Claude Code's $ARGUMENTS substitution for input. We deliberately don't: it only expands in Claude Code (it renders as literal syntax in Claude Desktop/Web, Codex, and the Streamlit playground), and it teaches the human reader nothing. Instead, every skill has a plain-language ## Input section that works on every runtime — and makes clear you can show up with full context, partial context, or nothing at all and be guided through the rest. Full rationale in CONTRIBUTING.md.
Claude Code · Claude Desktop · Claude Web · OpenAI Codex · ChatGPT · Cursor · Windsurf · n8n · LangFlow · CrewAI · Gemini · any agent that reads structured markdown
See docs/Platform Guides for PMs.md for platform-specific setup.
| Document | Purpose |
|---|---|
| Using PM Skills 101 | Beginner-friendly orientation — setup without technical overload |
| Platform Guides for PMs | Tool-by-tool setup chooser for every supported platform |
| Using PM Skills with Claude | Claude Code + GitHub ZIP upload for Claude Desktop/Web |
| Using PM Skills with Codex | Local workspace + GitHub-connected Codex on ChatGPT |
| Using PM Skills with ChatGPT | GitHub app, Custom GPT Knowledge, and Project-based usage |
| Using PM Skills with Slash Commands 101 | Turn skills into reusable slash commands like /pm-story |
| Add-a-Skill Utility Guide | End-to-end guide for generating and validating new skills |
| Market Intelligence Suite Summary | The 14-skill competitive/market research suite: disciplines, chain, and which skill to run when |
| Building PM Skills | How raw PM content gets distilled into agent-ready skills |
| START_HERE.md | 60-second onboarding for local repo users |
v0.83 — July 17, 2026 · The Market Intelligence Suite
autonomous-investigation — the protocol for research that proceeds without you. Question budgets, a search-plan gate, Fact / Inference / Assumption labels on every claim, do-not-invent lists, and stable diffable schemas, so investigations can run on a schedule and you can diff this quarter against lasttam-sam-som-calculator (three entry modes, including autonomous bottom-up research) and company-intel (Executive Signal Refresh rerun pattern — Then/Now diffs and Dropped Language: what leaders stop saying is often the strongest signal)v0.82 — July 8, 2026
incoming-request-advisor (Interactive) — drop in a Slack ping, email, mandate, or escalation and get a structured breakdown that separates the literal ask from the real job-to-be-done, reads sender power and stake, and points you toward a reply. Ships with a copy/paste template so you can run it by hand too/dist — no terminal, no Releases tab. Read the plain-language README, scan the CATALOG, and download any skill or pack straight from the repo. Built for PMs who just want the skillsv0.81 — July 4, 2026
## Input section: what to bring, what happens to context you supply up front (it's used, not re-asked), and reassurance that arriving empty-handed is fine — the guided flow covers the restargument-hint autocomplete for Claude Code users; deliberately no $ARGUMENTS templating — it breaks on every other runtime and teaches the reader nothing (why)$ARGUMENTS in the bodyagent-orchestration-advisor (Interactive) — the multi-agent workflow design skill was referenced everywhere but only existed on an orphaned commit; recovered from git history and brought up to current standardsv0.80 — June 19, 2026
stakeholder-identification (Component) — comprehensive stakeholder brainstorm using allies/audiences/influencers, R/P/D marking, equity lens, and bias check; narrows to priority targetsstakeholder-mapping (Component) — two complementary grids (Power × Interest + Impact × Power); comparing outputs reveals who you're under-engaging relative to how much the product affects themstakeholder-engagement-advisor (Interactive) — per-stakeholder engagement planning via Adaptive Decision Ladder: three questions on profile, power/impact, and context deliver tailored message framing, medium, cadence, and a named next actionAll three adapted from the MITRE Innovation Toolkit via the companion repo MITRE ITK Skills — worth a bookmark if you work in discovery, facilitation, or cross-functional product strategy.
v0.79 — May 15, 2026
organic-growth-advisor — McKinsey Growth Pyramid triage for new segments, geographies, channels, or productspm-skill-creator — interactive skill for designing repo-compliant skills via guided conversation.claude-plugin/plugin.json that silently blocked Claude Code skill discoveryPM_MAX_INPUT) and path traversal protection to helper scriptsFound a gap? Have a PM framework worth formalizing? The bar is pedagogic — skills must teach the why, not just the how.
See CONTRIBUTING.md for guidelines, or open an issue to start a conversation.
CC BY-NC-SA 4.0 — non-commercial use with share-alike.
Everything in this repository — every skill, template, and doc — is licensed CC BY-NC-SA 4.0. There is no mix of licenses here.
Some skills note in their Provenance sections that they were adapted from product-manager-prompts, Dean's earlier prompt library. That repo has the same author, so there is no license conflict: a license grants permissions to other people, and a copyright holder is free to adapt and relicense their own work. Those Provenance lines are lineage — a breadcrumb back to where an idea started — not a license dependency. No third-party MIT-licensed text is incorporated anywhere in this library.
In plain terms:
The companion prompt library, product-manager-prompts, carries the same CC BY-NC-SA 4.0 license as of its v2.3.0, with its own plain-language permissions (stricter on commercial use) — see its LICENSING.md, which governs that repo.
name: lean-ux-canvas
argument-hint: "[business problem]"
description: Guide teams through Lean UX Canvas v2. Use when framing a business problem, surfacing assumptions, and defining what to learn next.
intent: >-
Guide product managers through creating **Jeff Gothelf's Lean UX Canvas (v2)**—a one-page facilitation tool that frames work around a **business problem to solve**, not a **solution to implement**. Use this to align cross-functional teams around core assumptions, craft testable hypotheses, and ensure learning happens every sprint by exposing gaps in understanding (problem, users, value, and why the solution should work).
type: interactive
best_for:
- "Framing a business problem before solutioning"
- "Surfacing assumptions in a cross-functional workshop"
- "Turning a vague initiative into hypotheses and learning goals"
scenarios:
- "Help me run a Lean UX Canvas workshop for onboarding drop-off"
- "Use Lean UX Canvas to frame a new AI product idea"
- "We have a business problem but too many assumptions. Run a Lean UX Canvas session."Guide product managers through creating Jeff Gothelf's Lean UX Canvas (v2)—a one-page facilitation tool that frames work around a business problem to solve, not a solution to implement. Use this to align cross-functional teams around core assumptions, craft testable hypotheses, and ensure learning happens every sprint by exposing gaps in understanding (problem, users, value, and why the solution should work).
This is not a roadmap or feature list—it's an "insurance policy" that turns assumptions into experiments before committing to full development. The canvas shifts conversations from outputs to outcomes and ensures teams build the right thing, not just build things right.
Works best with: The business problem you're framing — or the solution idea you're being handed, which the canvas will reframe as a problem. Also useful: Known users, evidence so far, and what the team already believes (assumptions to surface).
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The skill opens with Box 1: what business problem are you trying to solve?
Example invocation: Fill a Lean UX canvas: leadership wants 'an AI chatbot' — underlying problem seems to be support ticket volume growing 3x faster than the team.
The Lean UX Canvas (v2) is a structured, one-page template designed to help teams frame their work around a business problem, not a solution. It aligns cross-functional teams on:
Origin: Created by Jeff Gothelf, author of Lean UX (O'Reilly, 2013). Version 2 was released to improve clarity around business vs. user outcomes.
Key Insight: The canvas acts like an insurance policy—it exposes gaps in understanding before you build, ensuring you don't waste sprints on the wrong thing.
Layout (3 columns × 3 rows):
┌─────────────────────┬──────────────┬───────────────────────┐
│ 1. Business Problem │ │ 2. Business Outcomes │
│ │ │ │
├─────────────────────┤ 5. Solutions ├───────────────────────┤
│ 3. Users │ (tall box │ 4. User Outcomes │
│ │ spanning │ & Benefits │
├─────────────────────┤ rows 1-2) ├───────────────────────┤
│ 6. Hypotheses │──────────────┤ 8. Least Work / │
│ │ 7. Learn │ Experiments │
│ │ First │ │
└─────────────────────┴──────────────┴───────────────────────┘
The 8 Boxes (fill in this order):
Problem-First, Not Solution-First: Starts with "what changed in the world?" not "we should build X." This prevents solution-driven thinking.
Assumption-Driven: Makes hypotheses explicit before building. Every discipline surfaces their risks (technical feasibility, user value, business viability).
Experiment-Focused: Tests assumptions before committing resources. Small experiments beat big bets.
Cross-Functional Alignment: Shared canvas creates common language. Everyone sees the same gaps in understanding.
Box 2 (Business Outcomes) vs. Box 4 (User Outcomes):
Box 2 is metrics. Box 4 is human.
Solutions (Box 5) Are Hypotheses, Not Commitments: List candidate solutions (features, policies, even business model shifts). You're not committing to build all of them—you're exploring the solution space.
Hypotheses (Box 6) Are Testable: Use the template: "We believe [business outcome] will be achieved if [user] attains [benefit] with [solution]." Each hypothesis focuses on one solution.
✅ Use this when:
❌ Don't use this when:
Use workshop-facilitation as the default interaction protocol for this skill.
It defines:
Other (specify) when useful)This file defines the domain-specific assessment content. If there is a conflict, follow this file's domain logic.
Use template.md for the full fill-in structure.
This interactive skill walks through 8 adaptive questions (one per canvas box) in sequence. At each step, the agent offers guidance, examples, and options to help you fill the box correctly.
Agent asks:
Before we fill out the Lean UX Canvas, let's gather context. Please share:
Business Context:
User Context:
You can paste:
Agent asks:
What problem does the business have that you are trying to solve?
Describe:
Good examples:
Bad examples (too vague):
Agent offers 3 options:
skills/problem-statement/SKILL.md or skills/problem-framing-canvas/SKILL.md first]User response: [Selection or description]
Agent validates: Does this describe what changed and why it creates a problem? If not, ask clarifying questions.
Agent asks:
How will you know you solved the business problem? What will you measure?
Focus on measurable behavior change (leading indicators welcome). Ask yourself: "What will people be doing differently if the solution works?"
Examples of business outcomes:
Important: This is Box 2 (behavior change), not Box 4 (user benefits/empathy). Metrics go here. Emotions go in Box 4.
Agent offers 3 options:
User response: [Selection or description]
Agent validates: Are these measurable? Observable? Do they indicate behavior change (not just "increase revenue")?
Agent asks:
What types (i.e., personas) of users and customers should you focus on first?
Consider:
Why this matters: Teams tend to shortcut here ("everyone"). The canvas wants a shared vision of the user—and it's not always "the customer."
Examples:
Agent offers 3 options:
skills/proto-persona/SKILL.md or paste persona]skills/proto-persona/SKILL.md component skill]User response: [Selection or description]
Agent validates: Is this specific enough to imagine a real person? Or is it too broad ("all users")?
Agent asks:
Why would your users seek out your product or service? What benefit would they gain? What behavior change can we observe that tells us they've achieved their goal?
Focus on goals, benefits, emotions, empathy—not metrics (those go in Box 2).
Examples of user outcomes & benefits:
Why this matters: This is the empathy box. It's about human motivation, not just behavior change.
Agent offers 3 options:
skills/jobs-to-be-done/SKILL.md or skills/discovery-interview-prep/SKILL.md]User response: [Selection or description]
Agent validates: Does this explain why the user cares (not just what they'll do)?
Agent asks:
What can we make that will solve our business problem and meet the needs of our customers at the same time?
List features, initiatives, policies, systems, or even business model shifts that might work. Encourage a wide solution space: big/small, innovative, "weird," and non-technical solutions.
Examples:
Important: These are hypotheses, not commitments. You're exploring options, not committing to build everything.
Agent offers 3 options:
User response: [Selection or description]
Agent validates: Do you have at least 3 candidate solutions? (More options = better hypotheses later)
Agent asks:
Now let's create testable hypotheses by combining assumptions from Boxes 2-5.
Use this template:
We believe that [business outcome from Box 2] will be achieved if [user from Box 3] attains [benefit from Box 4] with [solution from Box 5].
Rules:
Example:
We believe that increasing mobile checkout conversion rate from 45% to 60% will be achieved if mobile-first millennials (25-35) attain faster, friction-free checkout with one-tap Apple Pay integration.
Agent offers:
Based on your inputs, here are suggested hypotheses (one per solution from Box 5):
Options:
User response: [Selection or description]
Agent validates: Does each hypothesis clearly state what you believe will happen if the solution works?
Agent asks:
For each hypothesis from Box 6, identify its riskiest assumptions. Then determine the riskiest one right now.
Types of risk:
Hint: Early on, focus risk on value more than feasibility (most of the time). Don't build something users don't want, even if it's technically feasible.
Agent offers:
Based on your hypotheses, here are the riskiest assumptions:
Which is the riskiest right now?
Options:
User response: [Selection]
Agent records: This is the assumption we'll test first.
Agent asks:
Design an experiment to validate or invalidate the riskiest assumption (from Box 7) as fast as you can.
Examples of experiment types:
Reminder: Experiment design is an art. The canvas nudges you to do it—expect to iterate on the experiment design separately.
Agent offers:
Based on your riskiest assumption, here are experiment options:
Which experiment will you run?
User response: [Select one or describe custom experiment]
Agent validates: Is this the smallest test that can validate/invalidate the assumption? (If it takes >2 weeks, it's too big—break it down.)
Agent summarizes:
Here's your completed Lean UX Canvas (v2):
┌─────────────────────────────────────────────────────────────┐
│ Lean UX Canvas (v2) │
│ Initiative: [Title] │
│ Date: [Today's date] │
│ Iteration: 1 │
└─────────────────────────────────────────────────────────────┘
┌─────────────────────┬──────────────┬───────────────────────┐
│ 1. Business Problem │ │ 2. Business Outcomes │
│ [Your input] │ │ [Your input] │
│ │ │ │
├─────────────────────┤ 5. Solutions ├───────────────────────┤
│ 3. Users │ │ 4. User Outcomes │
│ [Your input] │ [Your input] │ & Benefits │
│ │ │ [Your input] │
├─────────────────────┤──────────────┼───────────────────────┤
│ 6. Hypotheses │ 7. Learn │ 8. Experiments │
│ [Your input] │ First │ [Your input] │
│ │ [Your input] │ │
└─────────────────────┴──────────────┴───────────────────────┘
Next steps:
Agent asks:
Would you like me to:
skills/discovery-interview-prep/SKILL.md for customer interviews)?See examples/sample.md for full Lean UX Canvas examples.
Mini example excerpt:
**Box 1:** Mobile checkout conversion is 15% lower than desktop
**Box 2:** Increase mobile conversion from 45% to 60%
**Box 8:** Wizard-of-Oz test with one-tap checkout
Failure Mode: Box 1 says "We need to build X" instead of describing what changed.
Consequence: You build the solution someone already decided on, without validating the problem exists.
Fix: Ask: "What changed in the world? Why is this a problem now (vs. 6 months ago)?"
Failure Mode: Box 2 says "Increase revenue" or "Make users happy."
Consequence: No way to measure success; can't tell if experiments worked.
Fix: Define measurable behavior change. "Increase average order value from $50 to $75" or "Reduce support tickets by 30%."
Failure Mode: Box 3 says "All users" or "Everyone."
Consequence: Can't design targeted experiments; waste time on personas who won't adopt.
Fix: Pick one persona to start. You can expand later.
Failure Mode: Putting emotions in Box 2 and metrics in Box 4 (or vice versa).
Consequence: Misaligned hypotheses; unclear success criteria.
Fix: Box 2 = Behavior change (metrics). Box 4 = Goals, benefits, emotions (empathy).
Failure Mode: Listing one feature because stakeholders already decided.
Consequence: No exploration of alternatives; can't test which solution is best.
Fix: Force yourself to list 3+ solutions. Ask: "What else could solve this problem?"
Failure Mode: "We'll just build it and see what happens."
Consequence: Waste weeks/months building the wrong thing.
Fix: Design smallest experiment first. If you can't think of one, use skills/pol-probe-advisor/SKILL.md to choose a validation method.
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