Persona Calibration#

Use this skill to turn vague "I want a better personality" requests into a structured persona update workflow.

What This Skill Does#

• Analyze common persona dimensions from industry examples before designing questions.
• Run a multi-round calibration interview instead of asking for a single freeform description.
• Separate persona core from expression style and operating rules.
• Convert interview results into concrete edits for SOUL.md, IDENTITY.md, USER.md, and optionally MEMORY.md.
• Prefer proposal → approval → edit for major personality changes.

When To Use#

Use when the user asks to:

• create a new persona / personality for an OpenClaw instance
• refine or update an existing assistant personality
• make the agent more like a partner / advisor / operator / researcher / etc.
• compare persona frameworks or prompt dimensions from GitHub or the web
• design a questionnaire or interview for personality tuning
• help another person update their own OpenClaw instance persona

Core Principle#

Do not jump straight into writing persona prose.

First extract the hidden dimensions behind the requested personality, then validate them through structured questions, then update files.

Workflow#

Step 1: Baseline scan#

If the user mentions existing persona repositories, prompt frameworks, or wants a more evidence-based process:

• Inspect a few representative sources from GitHub or the web.
• Extract recurring dimensions instead of copying wording.
• Summarize the dimensions in a compact model.

Read references/persona-dimensions.md for the default dimension model and example source patterns.

Step 2: Build a dimension model#

Use these buckets as the default model:

• Identity / role
• Objective function / priorities
• Beliefs / philosophy
• Decision style
• Communication style
• Proactiveness boundaries
• Scenario behavior
• Anti-patterns / forbidden behaviors
• Memory policy
• Self-correction / update policy

Adjust if the user clearly needs more specific domains.

Step 3: Run calibration rounds#

Do not dump a huge unstructured questionnaire all at once.

Use progressive rounds. The default order is:

• Round 1 — Core positioning
  • role, priorities, judgment style, proactiveness
• Round 2 — Communication & expression
  • length, structure, tone, disagreement style, forbidden phrasing
• Round 3 — Scenario calibration
  • debugging, research, architecture, brainstorming, rushed tasks
• Round 4 — Boundaries, memory, correction
  • what to remember, what not to remember, when to interrupt, how to update rules
• Round 5 — Example validation
  • present several answer samples and ask for favorite / least favorite

Default size:

• 8-12 questions per round
• Mix direct preference questions with scenario questions and trade-off questions

Step 4: Ask better questions#

Design questions to reveal not just stated preferences, but actual operating preferences.

Mix these question types:

• Baseline questions — explicit preferences
• Scenario questions — same dimension across multiple contexts
• Trade-off questions — force ranking between competing values
• Boundary questions — what feels annoying, wrong, or overbearing
• Example selection questions — choose preferred response samples

Important: do not hard-code a single number of clarification questions into the final persona. If the calibrated preference is to clarify first, phrase it as:

• ask 1-3 high-value clarification questions by default
• continue only if still needed
• stay concise; avoid turning the interaction into an interrogation

Step 5: Summarize after each round#

After each round:

• give a concise interpretation of what the answers imply
• highlight any tension or unresolved ambiguity
• explain what the next round is trying to disambiguate

Do not silently absorb answers without reflecting them back.

Step 6: Produce a persona update proposal#

Before editing files, produce a proposal that translates the interview into:

• role identity
• value hierarchy
• communication rules
• scenario handling rules
• memory rules
• update policy

For major personality edits, get explicit user approval before changing files.

Step 7: Apply updates to files#

Map results to files like this:

• SOUL.md → personality core, communication style, behavioral rules, scenario guidance, absolute don'ts
• IDENTITY.md → concise role / creature / vibe summary
• USER.md → user preferences if the changes are really user-specific rather than agent-specific
• MEMORY.md → compact long-term summary of calibration results

Do not stuff every interview detail into every file. Keep long-term files crisp.

Step 8: Recommend a trial period#

After editing:

• explain what changed
• recommend a short trial period in real conversations
• invite the user to note where the "feel" is still off
• suggest a second-pass refinement only after some real usage

Output Requirements#

When reporting results, prefer this structure:

• Current read of the persona
• Key signals from the latest round
• Tensions / unresolved edges
• Proposed rule changes
• Next round or next action

When delivering the final proposal, include:

• final persona summary
• file mapping ( SOUL.md, IDENTITY.md, USER.md, MEMORY.md)
• what should be edited now vs what should wait for trial feedback

Guardrails#

• Do not optimize for theatrical prose; optimize for durable operating rules.
• Do not confuse personality with roleplay.
• Do not lock the persona into rigid behavior when the user actually wants conditional switching.
• Do not store low-value conversational fragments as long-term memory.
• Do not make major persona edits without a proposal review first, unless the user explicitly asked for direct changes.

References#

• For default persona dimensions and what industry persona repos usually encode, read references/persona-dimensions.md.
• For a reusable round-by-round questionnaire scaffold, read references/questionnaire-template.md.
• For an example of how calibration results should be distributed across OpenClaw files, read references/file-mapping-example.md.