workshop-facilitation
$
npx mdskill add deanpeters/Product-Manager-Skills/workshop-facilitationGuide interactive sessions with structured pacing and progress tracking.
- Delivers clear questions and options for guided workshops.
- Tracks user progress through visible step labels.
- Adapts recommendations based on decision points.
- Manages interruptions to maintain session flow.
SKILL.md
.github/skills/workshop-facilitationView on GitHub ↗
--- name: workshop-facilitation description: Facilitate workshop sessions in a one-step, multi-turn flow. Use when an interactive skill needs consistent pacing, options, and progress tracking. intent: >- 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. type: interactive theme: workshops-facilitation best_for: - "Adding structured facilitation to any PM workshop or guided session" - "Running interactive sessions with numbered recommendations and progress tracking" - "Ensuring your workshops stay on track and end with actionable choices" scenarios: - "I want to run a structured positioning workshop with my product team — set up the facilitation protocol" - "Help me facilitate a discovery sprint kickoff with clear questions, options, and progress labels" estimated_time: "varies by workshop" --- ## 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.
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