Product Manager

SkillAI & models

Analyze features from user perspective and write user stories with acceptance criteria. Use when defining requirements, writing user stories, validating scope against project state, prioritizing features by impact, comparing approaches, analyzing user needs, or planning next work. Triggers: "user story", "acceptance criteria", "scope", "prioritize", "compare requirements", "user needs", "what to build next".

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Product Manager skill

What this skill tells your AI

The instructions your AI receives, as published by joaquimscosta/arkhe-claude-plugins in plugins/roadmap/skills/pm/SKILL.md and read by ahel’s review.

Analyze features from the user perspective, write user stories, validate scope, and prioritize work.

Context Discovery

Run the shared context discovery protocol in CONTEXT_DISCOVERY.md. Execute all phases in order. Store results for use in analysis below.

Arguments

Parse from $ARGUMENTS:

ModeDescription
stories <feature>Generate user stories with Given/When/Then acceptance criteria
prioritizeMoSCoW prioritization with value/effort matrix
scope <feature>Feasibility and scope assessment
validateCross-reference codebase against project goals
needsAnalyze user pain points and unmet needs
compare <A> vs <B>Structured comparison of two approaches
nextRecommend what to build next
(none)Ask what the user needs help with

Mode Execution

ModeProduces
stories <feature>User stories (As a/I want/So that) with Given/When/Then AC, grouped by Must/Should/Could
prioritizeMoSCoW + value/effort matrix table, ranked by impact
scope <feature>Assessment: User Value, Project Fit, Dependencies, Risks, Recommendation
validateGoals vs reality cross-reference — scope creep, missing features, readiness gaps
needsUser Profiles, Pain Points table, Unmet Needs, Validation Questions
compare <A> vs <B>Dimension table (value, effort, deps, risk, fit) with clear recommendation
next1-3 prioritized features from gaps, specs pipeline, and maturity analysis

See WORKFLOW.md for detailed execution steps and output templates per mode.

Module Maturity Scale

Use the shared vocabulary in MATURITY_SCALE.md.

Output Rules

  • Default: conversational — output goes to chat
  • User-focused — every recommendation ties back to user outcomes
  • Grounded — cite specific docs, gaps, or specs when making claims
  • Honest — flag unknowns and open questions rather than guessing

File Persistence

After producing the analysis, ask the user:

Save this analysis to {output_dir}/requirements/{filename}.md?

Where {output_dir} comes from .arkhe.yaml (default: arkhe/roadmap).

ModeFilename Pattern
stories <feature>{feature-slug}-stories.md
prioritize{YYYY-MM-DD}-priorities.md
scope <feature>scope-{feature-slug}.md
validate{YYYY-MM-DD}-validation.md
needs{YYYY-MM-DD}-needs.md
compare <A> vs <B>{a-slug}-vs-{b-slug}.md
next{YYYY-MM-DD}-next.md

Deep Mode (--deep)

When $ARGUMENTS contains --deep, run the full multi-agent pipeline instead of conversational analysis. This produces reviewed, confidence-scored artifacts with cross-perspective validation.

See WORKFLOW.md § Deep Pipeline for the 5-phase execution protocol.

Patterns applied: Pipeline, Confession, Critic-Actor, Specification-First (for stories), Confidence-Gated Completion.

Lane Discipline

See the PM section of LANE_DISCIPLINE.md. Stay in your lane.

References

Signals

GitHub stars
21
Forks
4
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
pm-joaquimscosta
Source
github.com/joaquimscosta/arkhe-claude-plugins