AI Collaboration Profile Builder

Help AI understand a user's cognitive style, output preferences, and value orientation so it can generate deliverables that better match what they actually w...

Agent Collaboration Profile Builder

Overview

Turn fuzzy self-descriptions into a stable collaboration profile that another agent can reuse. Start from the user's real pain, run a guided questionnaire in rounds, infer stable traits, resolve contradictions, and output practical Markdown files instead of entertainment-style labels.

Workflow

1. Calibrate the request

• Start by restating the real job: build a collaboration profile, not a personality label.
• Ask what feels off in the user's current AI workflow, what tasks they use AI for, and what a "good" answer feels like.
• Match the user's language. Chinese and English are both supported.
• If the user already supplied rich preferences, skip redundant intake questions and move to missing dimensions.

2. Run the guided questionnaire

• Load questionnaire-v1.md.
• Use the intake plus the 5 core dimensions: cognitive style, work style, output preference, value function, and collaboration protocol.
• Ask 8 to 12 questions per round. Do not dump the whole bank unless the user explicitly asks for it.
• Accept option codes, prose, or mixed answers.
• After each round, give a short local synthesis before moving on.

3. Infer stable traits

• Load inference-rules.md.
• Compress answers into a small set of high-value traits. Do not echo raw scores or every option.
• Prefer repeated signals over one-off answers, later answers over earlier answers, and explicit free text over inferred defaults.
• If answers conflict, run one conflict-calibration round before producing the final profile.
• If the user is actually blocked at a higher-level framing problem, say so and reframe it.

4. Generate the profile

• Load output-schema.md and templates.md.
• Default output: AI_USER_PROFILE.md.
• Offer split outputs only when useful: USER.md, WORKSTYLE.md, and COLLAB_PROTOCOL.md.
• Keep the section titles stable so another agent can reuse them reliably.
• Mark unresolved items in Known Unknowns.

5. Handle partial or messy sessions

• If the user stops early, produce a partial profile instead of abandoning the session.
• Mark unanswered or conflicting items explicitly.
• If the user asks for a simple personality label, explain that this skill optimizes for collaboration quality and actionability, then redirect to practical traits.

Output Rules

• Default opening: restate the real problem or goal, then give the conclusion.
• Optimize for decision value, not coverage.
• Keep the writing direct, structured, and high-density.
• Use headings plus paragraphs by default. Use tables only for comparison, classification, or decision support.
• Distinguish fact, inference, recommendation, and unknown when the difference matters.
• Keep the final profile reusable by another agent without needing the full questionnaire transcript.

References

• questionnaire-v1.md: question bank and round structure
• inference-rules.md: trait mapping, conflict handling, and compression rules
• output-schema.md: stable output contract
• templates.md: final Markdown templates
• examples.md: example sessions and output excerpts

Triggers And Examples

Use this skill when the user says things like:

• "Help me build an AI collaboration profile."
• "AI answers are always close, but not quite right."
• "Generate an OpenClaw-readable USER.md for me."
• "I want an agent to understand how I think and how I like to work."
• "Use this skill to turn my AI collaboration preferences into an agent-readable profile."