product-manager-skills_skill

This skill helps you diagnose SaaS metrics and critique PRDs to shape evidence-based roadmaps and AI product decisions.

Python

2.6k

GitHub Stars

6

Bundled Files

3 weeks ago

Catalog Refreshed

1 month ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstart where the catalogue uses aiagentskills.

npx veilstart add skill openclaw/skills --skill product-manager-skills

_meta.json475 B
package.json1.1 KB
README.md8.7 KB
README.zh-CN.md3.6 KB
SKILL.md14.3 KB
STARTER-PROMPTS.md2.8 KB

Overview

This skill is a senior product manager persona for diagnosing SaaS metrics, critiquing PRDs, planning roadmaps, running discovery, coaching PM career transitions, and pressure-testing AI product decisions. It bundles six knowledge domains, 12 templates, and 30+ frameworks into an opinionated, evidence-first interaction style that labels assumptions and names tradeoffs. The skill prioritizes measurable outcomes, rapid drafts, and actionable next steps.

How this skill works

It maps user intent to a domain framework (discovery, strategy, artifacts, finance, career, AI product) and pulls the matching template or diagnostic. For ambiguous requests it asks one clarifying question or delivers a best-guess draft with assumptions explicitly labeled. Every recommendation names tradeoffs, proposes measurable outcomes, and ends with a concrete next step.

When to use it

Diagnose SaaS metrics, churn, NRR, or unit economics
Critique or produce a PRD, user story, or roadmap
Run discovery: interview guides, problem framing, or PoL probes
Pressure-test AI product decisions or design agent workflows
Prepare for PM career transitions or executive onboarding

Best practices

Start with the smallest useful draft; expand only when asked
Label every assumption with [assumption] and distinguish knowns vs hypotheses
State outcomes as numeric targets with timeframe and owner
Name the tradeoff for any recommendation (speed vs quality, short-term revenue vs long-term retention)
Use one-question-at-a-time guided mode for discovery; prefer direct drafts for simple asks

Example use cases

Run a 2-week discovery plan and a PoL probe to validate user onboarding friction
Critique an existing PRD and rewrite the executive decision section with tradeoffs and measurable success metrics
Produce a Now/Next/Later roadmap tied to TAM/SAM/SOM and a prioritized feature investment case
Diagnosis workshop: business health scorecard highlighting churn drivers and unit economics fixes
Coach a senior PM for Director transition using altitude-horizon readiness and a 90-day onboarding plan

FAQ

I deliver a best-guess draft and mark assumptions with [assumption]. Ask me to switch to guided mode if you want step-by-step discovery.

Do you provide templates or filled artifacts?

Both. I load the matching template, fill it with specific content, and apply domain quality gates rather than dumping generic text.

How do you handle AI product tradeoffs?

I evaluate AI readiness, list failure modes, propose PoL probes for validation, and name tradeoffs (e.g., latency vs. accuracy, hallucination risk vs. scope).