AI PM Agent

AI-powered product management workflow agent. Use when the user wants to do product discovery, write PRDs, prioritize features, design experiments, plan laun...

Security Scan

This is an instruction-only product-management workflow agent whose declared behavior, required resources, and instructions are internally consistent and proportionate to its stated purpose.

Purpose & Capability

The name/description (PM workflows: discovery, PRDs, prioritization, experiments, GTM) matches the SKILL.md and the two reference files. The skill requests no binaries, no env vars, and no install — all reasonable for an instruction-only PM drafting agent.

Instruction Scope

The SKILL.md is explicit about spawning phase-specific subagents and reading/writing four project files (DISCOVERY.md, PRD.md, EXPERIMENT.md, GTM.md). That is consistent with its purpose. Note: the instructions give subagents latitude to ask questions and 'challenge assumptions' (expected for a PM workflow), and they read/write files in the project root — if the agent has broader workspace access in your environment it could potentially read other files, but the skill itself only references the listed files.

Install Mechanism

No install spec and no code files are included (instruction-only). This is lowest-risk for installation because nothing will be downloaded or written by an installer.

Credentials

The skill declares no required environment variables, credentials, or config paths. That aligns with the described functionality (document drafting and structured PM workflows).

Persistence & Privilege

always:false (no forced inclusion). disable-model-invocation:false (agent may invoke autonomously) — this is the platform default and appropriate for an interactive workflow agent. The skill does not request persistent system-wide privileges or modify other skills.

Assessment

This skill appears coherent and safe in structure, but review the following before installing: (1) The agent will create and read the four project files in the workspace root — avoid running it in a directory that contains sensitive or unrelated files. (2) Don't paste secrets, API keys, or proprietary data into problem statements or supporting documents the agent will read. (3) The skill spawns subagents and chooses internal models (Sonnet/Haiku); verify those model names map to your environment's expected models and policies. (4) Because autonomous invocation is allowed by default, run an initial dry-run with non-sensitive inputs and confirm outputs and prompts behave as expected before granting broader access. (5) The skill's content promises 'evidence-based' personas and market estimates — those will be generated by the model and can be fabricated; always validate critical claims with real data.

PM Agent — AI Product Management Workflow

Four agents covering the full PM lifecycle: Research → Define → Validate → Launch. Each phase uses proven frameworks and produces structured artifacts. Human checkpoints between phases.

Phases

#	Phase	Agent	Framework	Output
1	Research	Market & User Analyst	JTBD + Design Thinking	DISCOVERY.md
2	Define	Product Strategist	Opportunity Solution Tree + Amazon PRD	PRD.md
3	Validate	Experiment Designer	Design Sprint + Lean BML	EXPERIMENT.md
4	Launch	Go-to-Market Lead	Dual-Track Agile + OKR	GTM.md

How to Use

Full Workflow

"I want to build [product idea]" → run all 4 phases
"Run pm-agent on [problem statement]"

Each phase spawns a focused subagent with the right prompt. The subagent asks questions, challenges assumptions, and produces a structured artifact.

Partial Workflow

• "Just do a competitor analysis" → run Research only
• "Help me prioritize my backlog" → run Define (RICE/Kano section)
• "Write a PRD for this feature" → run Define with the feature description
• "Plan a design sprint" → run Validate only
• "Create a GTM plan" → run Launch only

Single Commands

• /research — JTBD interview analysis, market sizing, competitive landscape
• /define — Opportunity Solution Tree, PRD with Amazon Working Backwards
• /validate — Experiment design, prototype testing plan, BML metrics
• /launch — GTM strategy, OKRs, release checklist

Phase Details

Phase 1: Research (JTBD + Design Thinking)

Goal: Understand the problem before proposing solutions.

Spawn a subagent (Sonnet) with the Research prompt from references/prompts.md. It will:

1. JTBD Analysis — Extract the "job" users are hiring the product for
   • Push factors (pain with current solution)
   • Pull factors (attraction of new solution)
   • Trigger event (what moment starts the search)
2. Competitive Landscape — Map existing solutions and gaps
3. Market Sizing — TAM/SAM/SOM with reasoning
4. User Personas — 2-3 evidence-based personas (not fictional)
5. Write DISCOVERY.md — Consolidated research artifact

Key question: "What job is the user hiring this product to do?"

Phase 2: Define (Opp. Tree + Amazon PRD)

Goal: Define what to build and why, before how.

Spawn a subagent (Sonnet) with the Define prompt. It reads DISCOVERY.md and produces:

1. Opportunity Solution Tree — Visual hierarchy of outcome → opportunities → solutions
2. Prioritization — RICE scoring for top opportunities, Kano classification
3. Amazon PRD — Working Backwards: start with the press release, then FAQ
4. User Stories — INVEST-compliant stories with acceptance criteria
5. Write PRD.md — Complete product requirements document

Key rule: No solution before opportunity. No feature before user story.

Phase 3: Validate (Design Sprint + Lean)

Goal: Test assumptions before building.

Spawn a subagent (Sonnet) with the Validate prompt. It reads PRD.md and produces:

1. Assumption Map — Classify by risk (lethality × uncertainty)
2. Experiment Design — Lean BML cycle for riskiest assumptions
3. Prototype Plan — What to mock up and how to test with 5 users
4. Success Metrics — Quantitative pass/fail criteria per experiment
5. Write EXPERIMENT.md — Validation plan with test scripts

Key rule: Test the riskiest assumption first, not the easiest.

Phase 4: Launch (GTM + OKR)

Goal: Ship and measure.

Spawn a subagent (Haiku) with the Launch prompt. It reads PRD.md and EXPERIMENT.md and produces:

1. GTM Strategy — ICP, positioning, channel mix
2. OKRs — 3 measurable objectives with key results
3. Release Checklist — Pre-launch, launch day, post-launch tasks
4. Feedback Loop — How to collect and act on user signals
5. Write GTM.md — Launch plan with timelines

Key rule: Launch is not the end. It's the beginning of the BML cycle.

Model Selection

Phase	Model	Why
Research	Sonnet	Needs reasoning for market analysis
Define	Sonnet	Strategic decisions require depth
Validate	Sonnet	Experiment design needs critical thinking
Launch	Haiku	Mostly structured execution

Output Files

All phase outputs go to the project root:

• DISCOVERY.md — Research findings (JTBD, personas, competitive landscape)
• PRD.md — Product requirements (Opp. Tree, Amazon PRD, user stories)
• EXPERIMENT.md — Validation plan (assumptions, experiments, metrics)
• GTM.md — Launch plan (GTM, OKRs, checklist)

Each file is self-contained but references previous phases. You can run phases independently by providing the prerequisite context.

Frameworks Reference

For detailed framework guides (JTBD interview templates, RICE calculators, Amazon PRD templates), see references/frameworks.md.

Human-in-the-Loop

Each phase ends with a checkpoint:

• Approve — proceed to next phase as-is
• Edit — modify the artifact, then proceed
• Rerun — provide feedback, regenerate the phase

This mirrors real PM work: AI drafts, humans decide.