workshop-facilitation

deanpeters/product-manager-skills · updated Apr 8, 2026

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$npx skills add https://github.com/deanpeters/product-manager-skills --skill workshop-facilitation
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summary

Structured one-step-at-a-time facilitation pattern for interactive workshops and guided sessions.

  • Supports three entry modes: Guided (single question per turn), Context Dump (paste known details and skip redundancies), and Best Guess (infer missing context with labeled assumptions)
  • Provides real-time progress visibility with labels like Context Qx/8 and Scoring Qx/5 , plus enumerated recommendations only at decision points to avoid interaction drag
  • Handles flexible multi-select respo
skill.md

Purpose

Provide the canonical facilitation pattern for interactive skills: one step at a time, with clear progress, adaptive recommendations at decision points, and predictable interruption handling.

Key Concepts

  • One-step-at-a-time: Ask a single targeted question per turn.
  • Session heads-up + entry mode: Start by setting expectations and offering Guided, Context dump, or Best guess mode.
  • Progress visibility: Show user-facing progress labels like Context Qx/8 and Scoring Qx/5.
  • Decision-point recommendations: Use enumerated options only when a choice is needed, not after every answer.
  • Quick-select response options: For regular context/scoring questions, provide concise numbered answer options plus Other (specify) when useful.
  • Flexible selection parsing: Accept #1, 1, 1 and 3, 1,3, or custom text, then synthesize multi-select choices.
  • Context-aware progression: Build on previous answers and avoid re-asking resolved questions.
  • Interruption-safe flow: Answer meta questions directly (for example, "how many left?"), restate status, then resume.
  • Fast path: If the user requests a single-shot output, skip multi-turn facilitation and deliver a condensed result.

Application

  1. Start with a brief heads-up on estimated time and number of questions.
  2. Ask the user to choose an entry mode:
    • 1 Guided mode (one question at a time)
    • 2 Context dump (paste known context; skip redundancies)
    • 3 Best guess mode (infer missing details and label assumptions)
  3. Run one question per turn and wait for an answer before continuing.
  4. Keep questions plain-language; include a short example response format when helpful.
  5. Show progress each turn:
    • Context Qx/8 during context collection
    • Scoring Qx/5 during assessment/scoring
  6. Ask follow-up clarifications only when they materially improve recommendation quality.
  7. For regular context/scoring questions, offer quick-select numbered response options when practical:
    • Keep options concise and mutually exclusive when possible.
    • Include Other (specify) if likely answers are open-ended.
    • Accept multi-select responses like 1,3 or 1 and 3.
  8. Provide numbered recommendations only at decision points:
    • after context synthesis,
    • after maturity/profile synthesis,
    • during priority/action-plan selection.
  9. Accept numeric or custom choices, synthesize multi-select choices, and continue.
  10. If interrupted by a meta question, answer directly, then restate progress and pending question.
  11. If the user says stop/pause, halt immediately and wait for explicit resume.
  12. End with a clear summary, decisions made, and (if best guess mode was used) an Assumptions to Validate list.

Examples

Opening: "Quick heads-up: this should take about 7-10 minutes and around 10 questions. How do you want to start?

  1. Guided mode
  2. Context dump
  3. Best guess mode"

User: "2"

Facilitator: "Paste what you already know. I’ll skip answered areas and ask only what’s missing."

Decision point after synthesis:

  1. Prioritize Context Design (Recommended)
  2. Prioritize Agent Orchestration
  3. Prioritize Team-AI Facilitation

User: "1 and 3"

Facilitator: "Great. We’ll run Context Design first, with Team-AI Facilitation in parallel."

Common Pitfalls

  • Asking multiple questions in the same turn.
  • Offering recommendations after every answer (creates interaction drag).
  • Using shorthand labels without plain-language questions.
  • Hiding progress, so users don't know how much remains.
  • Ignoring the user's chosen option or custom direction.
  • Failing to label assumptions when running in best-guess mode.

References

  • Use as the source of truth for interactive facilitation behavior.
  • Apply alongside workshop skills in skills/*-workshop/SKILL.md and advisor-style interactive skills.
how to use workshop-facilitation

How to use workshop-facilitation on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add workshop-facilitation
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/deanpeters/product-manager-skills --skill workshop-facilitation

The skills CLI fetches workshop-facilitation from GitHub repository deanpeters/product-manager-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/workshop-facilitation

Reload or restart Cursor to activate workshop-facilitation. Access the skill through slash commands (e.g., /workshop-facilitation) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.575 reviews
  • Kaira Sanchez· Dec 16, 2024

    workshop-facilitation has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Arjun Wang· Dec 8, 2024

    workshop-facilitation reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Nia Torres· Dec 8, 2024

    Registry listing for workshop-facilitation matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Kaira Ramirez· Dec 4, 2024

    workshop-facilitation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Michael Jain· Nov 27, 2024

    workshop-facilitation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Valentina Desai· Nov 23, 2024

    workshop-facilitation reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Chen Li· Nov 7, 2024

    Solid pick for teams standardizing on skills: workshop-facilitation is focused, and the summary matches what you get after install.

  • Valentina Torres· Nov 7, 2024

    Keeps context tight: workshop-facilitation is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Michael Okafor· Oct 26, 2024

    We added workshop-facilitation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Valentina Bhatia· Oct 26, 2024

    workshop-facilitation has been reliable in day-to-day use. Documentation quality is above average for community skills.

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