Skill: dengyh/persona-calibration (version 1.0.0)

--- name: persona-calibration description: Create, audit, or update an OpenClaw agent persona through a structured multi-round calibration workflow. Use when the user wants to create a new assistant personality, refine an existing persona, tune SOUL.md / IDENTITY.md / USER.md, compare persona dimensions from industry examples, or run a questionnaire-based persona update for themselves or another OpenClaw instance. --- # 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`.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)