ai-product-manager-playbook

A comprehensive operating system for AI Product Management. Use this skill when planning, prototyping, evaluating, or launching AI-native products. It provid...

Security Scan

The skill is an instruction-only AI Product Management playbook with local templates and a simple telemetry analysis script; its files, requirements, and runtime instructions are consistent with the stated purpose.

Purpose & Capability

Name/description (AI PM playbook) match the included content: templates, references, and a feedback-loop script. There are no unexpected binaries, credentials, or external services requested.

Instruction Scope

SKILL.md instructs the agent to use local templates, references, and to run scripts/pm_feedback_loop.py on a provided telemetry JSON; the script only reads a local file, analyzes it, and prints suggestions. There are no instructions to read unrelated system files, access environment secrets, or transmit data externally.

Install Mechanism

No install spec and no code that downloads or executes remote artifacts. This is instruction-only with bundled markdown files and a local Python script.

Credentials

No required environment variables, credentials, or config paths are declared or referenced. The script only accepts a local telemetry JSON path as input.

Persistence & Privilege

always is false and the skill does not request persistent system presence or modify other skills or system configurations. The script does not write files or enable itself automatically.

Assessment

This skill appears coherent and low-risk: it provides local docs/templates and a simple Python script that reads a telemetry JSON and prints improvement suggestions. Before running: (1) review the telemetry JSON you pass in to ensure it contains no sensitive data or secrets, (2) confirm you trust the skill source (the package lists no homepage and the owner is unknown), and (3) note the feedback script only prints suggestions — it does not automatically edit templates or send data externally. If you need automatic updates or networked telemetry collection, require additional review because that would increase risk.

AI PM Playbook

Overview

The ai-pm-playbook skill operationalizes the best practices of AI Product Management into executable, agentic workflows. It is designed to help product managers transition from traditional, process-heavy roles to the "builder mentality" required in the AI era.

This skill provides a structured approach to the entire AI product lifecycle, ensuring that products are built rapidly, evaluated rigorously, and deployed responsibly.

Use this skill when:

• Prototyping a new AI feature or product.
• Planning a product roadmap in a rapidly changing AI landscape.
• Designing and running evaluations (Evals) for an AI model.
• Structuring a cross-functional AI product team.
• Developing a Go-To-Market (GTM) strategy for an AI product.
• Implementing ethical guardrails and red teaming for responsible AI.

The AI PM Operating System

This skill is built on the premise that AI automates low-value PM tasks (like writing detailed PRDs) and elevates the need for strategic vision, judgment, and technical fluency. The workflows below are designed to augment these higher-order skills.

Core Workflows

Choose the appropriate workflow based on your current product development phase:

1. Prototyping and Rapid Experimentation

Move from static PRDs to interactive, "production-ready" prototypes.

• Action: Decompose features, plan with AI, and build interactive prototypes.
• Reference: See references/prototyping_workflow.md for the step-by-step guide.

2. Roadmap Planning Under Uncertainty

Shift from feature-based roadmaps to outcome-oriented planning.

• Action: Define desired behaviors, use the Now/Next/Later framework, and apply the U.S.I.D.O. model.
• Reference: See references/roadmap_uncertainty.md for the planning framework.
• Template: Use templates/outcome_roadmap.md to structure your plan.

3. AI Evaluation and Metrics (Evals)

Move beyond basic accuracy to measure user experience, safety, and reliability.

• Action: Define evaluator roles, supply context, set goals, and establish scoring rubrics.
• Reference: See references/evaluation_metrics.md for the evaluation framework.
• Template: Use templates/ai_eval_rubric.md to design your evals.

4. Cross-Functional Collaboration

Structure your team for success in the complex world of AI development.

• Action: Implement a hybrid team structure, prioritize data readiness, and foster psychological safety.
• Reference: See references/cross_functional.md for organizational best practices.

5. Go-To-Market Strategy and Trust

Launch AI products that meet evolving customer expectations and build trust.

• Action: Define the 7 GTM pillars and prioritize transparency in data usage.
• Reference: See references/gtm_strategy.md for the launch framework.

6. Ethics, Safety, and Responsible Deployment

Ensure your AI products are safe, trustworthy, and aligned with human values.

• Action: Implement multi-layered guardrails and conduct rigorous red teaming.
• Reference: See references/responsible_ai.md for the safety framework.
• Template: Use templates/red_teaming_plan.md to structure your testing.

Self-Improving Loop

This skill incorporates a self-improving feedback loop to continuously refine your PM processes based on real-world execution data.

1. Collect Telemetry: After completing a major PM activity (e.g., a prototype sprint, an eval run, or a product launch), gather the outcomes, friction points, and user feedback.
2. Run the Loop: Execute scripts/pm_feedback_loop.py with the collected data.
3. Analyze and Adapt: The script will analyze the systemic friction and suggest updates to your templates, workflows, or evaluation rubrics to improve future performance.

Resources

• scripts/pm_feedback_loop.py: The engine for continuous improvement of PM processes.
• references/: Detailed guides for each of the 6 core workflows.
• templates/: Standardized formats for roadmaps, evals, and red teaming plans.