workshop-facilitation skill

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.

Input

Nothing required — this skill defines the facilitation protocol other interactive skills follow. Also useful: If invoked standalone, name the session you want facilitated and any context for it; that context carries into the session as answers already given.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. When another skill references this protocol, that skill's Input section governs what to provide.

Example invocation: Facilitate a 45-minute retro on our failed beta launch using this protocol.

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)

1. Run one question per turn and wait for an answer before continuing.
2. Keep questions plain-language; include a short example response format when helpful.
3. Show progress each turn:

• Context Qx/8 during context collection
• Scoring Qx/5 during assessment/scoring

1. Ask follow-up clarifications only when they materially improve recommendation quality.
2. 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.

1. Provide numbered recommendations only at decision points:

• after context synthesis,
• after maturity/profile synthesis,
• during priority/action-plan selection.

1. Accept numeric or custom choices, synthesize multi-select choices, and continue.
2. If interrupted by a meta question, answer directly, then restate progress and pending question.
3. If the user says stop/pause, halt immediately and wait for explicit resume.
4. 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."

Inline input at invocation: when the user supplies context with the invocation itself, credit it as answers, open at the first unanswered question, and keep progress labels honest (start at Context Q2/6 if Q1 was covered). Full transcript, including the re-asking anti-pattern: examples/inline-input-flow.md.

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.