Skills

create-anyone

Installation
SKILL.md

anyone.skill — Distill Anyone

Every person is a unique decision system, an irreplicable voice, a finite set of memories.
anyone-skill distills that uniqueness into a portable, evolvable OpenPersona skill pack.

anyone-skill is a distillation front-end for OpenPersona. It handles data collection, 4-dimension extraction, and evidence grading. The final output is a full OpenPersona persona pack generated via skills/open-persona.

Dependency chain: anyone-skill → skills/open-persona → openpersona create

Trigger phrases

  • /create-anyone
  • "distill X into a skill"
  • "create a persona for X"
  • "make a skill pack for X"
  • "I want to talk to X as an AI"
  • "clone X's personality"

To evolve an existing persona:

  • "I have more data" / "add this to X"
  • "that's not right" / "X wouldn't say that"
  • /update-anyone {slug}

Tools

Task Tool
Read any text / JSON / CSV / PDF / image Read (native — use for most chat exports)
Search public figures / fictional characters WebSearch
Extract SQLite databases (iMessage / WeChat) Bash → python3 ${CLAUDE_SKILL_DIR}/scripts/preprocess.py --input <file.db>
Sample oversized files (>5000 messages) Bash → python3 ${CLAUDE_SKILL_DIR}/scripts/preprocess.py --input <file> --max 3000
Write / update files Write / Edit
Version management Bash → python3 ${CLAUDE_SKILL_DIR}/scripts/version_manager.py
List existing personas Bash → python3 ${CLAUDE_SKILL_DIR}/scripts/skill_writer.py --action list

Reading strategy: use Read directly for all text-based exports — WhatsApp _chat.txt, Telegram result.json, Slack/Discord JSON, email .eml, Twitter/X archive, Feishu/DingTalk export, plain text, CSV. The agent understands any readable format natively; no parser needed.
Use preprocess.py only for: (1) binary SQLite .db files, (2) files too large to fit in context (auto-samples down to --max).

Phase 0: Classify the Subject

Determine which category the subject falls into — different categories use different data strategies and ethical rules:

Who do you want to distill?

  [1] Yourself           — full digital self
  [2] Someone you know   — colleague, friend, family, partner, ex
  [3] Public figure      — entrepreneur, artist, athlete, politician
  [4] Fictional character — game, anime, novel, film, series
  [5] Historical figure  — relies on documents, biographies, speeches
  [6] Archetype          — composite persona, no single real subject

Phase 1: Ethics & Copyright Check

Full rules: references/ethics.md. Key points by category:

Someone you know — confirm personal use only; no harassment, impersonation, or deception; all data stored locally.

Public figure — use only publicly traceable sources; generated skill must include disclaimer on first run: "Based on public information. Not the real person. For reference only."

Fictional character

  • Personal local use → no restrictions, direct roleplay mode
  • Distributing to others → activate Inspired-by mode (reinterpret, don't replicate)
  • The key criterion is distribution intent, not release year

Historical figure — publicly published sources only; mark uncertain claims as inferred (L3/L4).

Archetype — inform user this is a synthetic persona with no real-world counterpart.

Phase 2: Intake (exactly 3 questions)

Ask only these 3 questions, in order. Summarize answers before proceeding.

Q1: Codename (required)

What should we call them? Doesn't need to be their real name.
e.g. Alex · Jobs · Geralt · Grandma Rose

Q2: Basic info (one sentence, skippable)

Age / era, role / identity, where they're from — whatever comes to mind.
e.g. 28, product designer, Berlin · Apple co-founder, 1955–2011, Silicon Valley · The Witcher, monster hunter, medieval fantasy world

Q3: Personality impression (one sentence, skippable)

What's your core impression? MBTI, traits, contradictions, a moment that defined them.
e.g. INTJ, perfectionist, publicly harsh but privately warm · quiet until it matters, never explains their moves

Phase 3: Collect Source Material

Guide the user based on subject type:

Someone you know / Yourself

How would you like to provide source material?
More data = higher fidelity.

  [A] Chat export
      iMessage (macOS) · WhatsApp export · Telegram export
      Signal export · Slack export · Discord export
      WeChat (WeChatMsg / PyWxDump) · Feishu / DingTalk

  [B] Documents / email
      Notes, diaries, letters, essays, .eml / .mbox

  [C] Social media archive
      Twitter/X data export · Instagram archive · LinkedIn export
      Facebook data download

  [D] Paste / describe
      Paste text directly, or describe from memory

For each file provided:

  • Text / JSON / CSV exports → use Read directly. The agent reads and understands any format.
  • SQLite .db files (iMessage chat.db, WeChat PyWxDump) → run preprocess.py --input <file.db>
  • Very large files (>5 MB or clearly >5000 messages) → run preprocess.py --input <file> --max 3000

After processing each source, immediately save a copy to training/raw/ (do not wait for Step 6-D):

Source type           → save as training/raw/…
─────────────────────────────────────────────────────────────
Chat export (any)     → whatsapp.jsonl / imessage.jsonl / …  [{role, content}, …]
Essay / diary / notes → essays.txt                           plain text paragraphs
Interview / Q&A       → interviews.jsonl                     [{role:"user"|"assistant", content}]
Social posts          → social.jsonl                         [{role:"assistant", content}]
  • Keep original wording — do NOT paraphrase in raw/
  • Redact obvious PII before saving (phone numbers, SSNs, addresses)

Report after processing each source:
✅ [N] messages from [source] ([date range if known]) → saved to training/raw/[filename]

Public figure / Historical figure

Will search the following automatically via WebSearch:

  → Interviews and transcripts (video subtitles / text)
  → Books, speeches, open letters, earnings calls
  → Authorized biographies and academic studies
  → Public social media posts (X / LinkedIn / Instagram)
  → Documentary reviews and analytical essays

Report: [N] sources indexed, ~[M] words of coverage

User may also provide: book PDFs / video transcripts / interview screenshots.

Save all collected text to training/raw/ as plain .txt or structured .jsonl (interviews as {role:"user"|"assistant", content}).

Fictional character

Will collect via:

  [A] WebSearch → character wiki (Fandom / IMDB / game databases)
  [B] User-provided: script, lore book, novel text, dialogue list
  [C] User-described: memorable quotes, behavioral patterns

Activate copyright guard: ask "Will this skill be shared with others?"

  • Yes → Inspired-by mode
  • No → direct roleplay mode

Save all collected text to training/raw/ (scripts → script.jsonl, lore/wiki → lore.txt).

Archetype

Skip data collection. Proceed directly to Phase 4 based on Phase 2 impressions.

Phase 4: 4-Dimension Extraction

After all source material is processed, extract along 4 dimensions:

Dimension 1: Procedure — How do they think?

  • Mental models: 3–6 frameworks they habitually use (e.g. first principles, inversion, analogical reasoning)
  • Decision heuristics: their rule-of-thumb judgments ("always X before Y", "never trust Z unless...")
  • Information preference: data-driven or intuitive? big-picture or detail-oriented?
  • Risk posture: where are they bold, where are they cautious?

Dimension 2: Interaction — How do they speak?

  • Vocabulary: high-frequency words, catchphrases, words they never use, signature sentence structures
  • Rhythm and density: fast/slow, high/low information density, use of silence or pauses
  • Emotional temperature: composed vs. expressive; what silence means for them
  • Conflict style: how they express frustration; how they respond to being challenged
  • Humor: self-deprecating / ironic / dry / none

Dimension 3: Memory — What shaped them?

  • Key events: 3–5 specific moments that formed their character (with date/context when possible)
  • Relationship network: the people who influenced them most, and the pattern of those relationships
  • Fixations / avoidances: themes they return to or deliberately avoid
  • Anchors of pride: what they are most proud of

Dimension 4: Personality — What are their hard limits?

  • Core values: 3 non-negotiable principles they won't compromise on
  • Internal contradictions: the biggest tension within their character
  • Immutable traits: qualities that stay constant regardless of context
  • Layer 0 prohibitions: things they would never say or do under any circumstances

Phase 5: Evidence Grading

Tag each extracted piece with a confidence level:

Level Standard Tag
L1 Direct quote Verbatim, traceable source [L1: source]
L2 Reported Cited or paraphrased by others, verifiable [L2]
L3 Inferred Reasonably inferred from multiple signals [L3: inferred]
L4 Inspired Based on impression / fictional canon / archetype [L4: inspired]

Conflict resolution: higher level wins. Equal-level conflicts are listed side by side with source noted.

Phase 6: Generate OpenPersona Skill Pack

Field mapping reference: references/output-format.md

Step 6-A: Build persona.json

Map extraction results to OpenPersona v0.17+ format:

{
  "soul": {
    "identity": {
      "personaName": "Display name",
      "slug": "lowercase-hyphenated-slug",
      "bio": "2–4 sentence background. Key events. L1/L2 evidence preferred.",
      "sourceIdentity": "Real name or 'CharacterName from WorkTitle' (real/fictional subjects only)"
    },
    "aesthetic": {
      "visualDescription": "Appearance / visual style (omit if unknown)"
    },
    "character": {
      "personality": "Core traits, 3–5 descriptive tags. From Personality dimension.",
      "speakingStyle": "Vocabulary, rhythm, emotional temperature, catchphrases. From Interaction dimension.",
      "boundaries": [
        "Layer 0 constraint 1 (L1/L2 evidence)",
        "Layer 0 constraint 2"
      ]
    }
  },
  "body": {
    "runtime": {
      "framework": "openclaw",
      "modalities": ["text"]
    }
  },
  "evolution": {
    "instance": {
      "enabled": true,
      "boundaries": {
        "immutableTraits": ["Immutable trait 1", "Immutable trait 2"]
      }
    }
  }
}

Filling rules:

  • Use L1/L2 evidence for bio, personality, speakingStyle
  • L3/L4 content stays in persona.md only — not in persona.json
  • sourceIdentity: real people → their name; fictional → "CharacterName from WorkTitle"; archetypes → omit
  • Public figures / historical figures: add to boundaries: "Based on public information. Not the real person."

Step 6-B: Generate skill pack via skills/open-persona

Load skills/open-persona/SKILL.md and run with the persona.json from Step 6-A:

npx openpersona create --config persona.json --output ./{slug}-skill

Output is a full OpenPersona persona pack:

{slug}-skill/
├── SKILL.md          ← Soul/Body/Faculty/Skill index
├── persona.json      ← Declaration (derived fields stripped)
├── state.json        ← Initial runtime state
├── soul/
│   ├── injection.md  ← Self-awareness injection
│   └── constitution.md
├── agent-card.json
└── scripts/
    └── state-sync.js ← Body nervous system

Step 6-C: Install (optional)

npx openpersona install ./{slug}-skill
npx openpersona switch {slug}

Step 6-D: Export Training Data (for persona-model-trainer)

Export a training/ directory alongside the skill pack. This feeds persona-model-trainer with both layers of data:

training/
  raw/                      ← original source files (authentic voice, unmodified)
    chat_logs.jsonl         ← chat exports: {role, content} turns
    books.txt               ← long-form text: treated as persona's monologue
    interviews.jsonl        ← Q&A format: {role:"user"|"assistant", content}
    social_posts.jsonl      ← short posts: {role:"assistant", content}
    [... one file per source]
  conversations.jsonl       ← distilled & structured turns (from Phase 4 extraction)
  profile.md                ← concise character profile (system prompt seed)
  metadata.json             ← slug, name, source count, word count, timestamp

How to build each file:

training/raw/ is already populated progressively during Phase 3 as each source is processed. Do not re-write it here.

training/conversations.jsonl — write distilled turns from Phase 4 extraction:

Each line is one turn: {"role": "user"|"assistant", "content": "..."}.
Represent the persona's voice as assistant turns. Synthesize realistic user prompts for user turns.

Minimum 50 turns; aim for 200–500 if source material allows.

training/profile.md — write a concise 300–500 word character sheet:

# {Name} — Character Profile

## Identity
[1–2 sentences: who they are, era, role]

## Voice
[Key vocabulary, catchphrases, sentence rhythm, emotional temperature]

## Core Values
[3 non-negotiable principles]

## Immutable Traits
[3–5 qualities that never change regardless of context]

## Do Not Cross
[Layer 0 prohibitions: what they would never say or do]

training/metadata.json:

{
  "slug": "{slug}",
  "name": "{display name}",
  "subject_type": "personal|public|fictional|historical|archetype",
  "source_count": N,
  "total_words": N,
  "distilled_turns": N,
  "raw_files": ["chat_logs.jsonl", "essays.txt"],
  "created_at": "ISO-8601 timestamp"
}

After export, print:

📦 Training data ready → training/
   raw/          {N} source files  (~{M} words authentic voice)
   conversations.jsonl  {N} distilled turns
   profile.md    {N} words
   → Ready for: persona-model-trainer

persona.md (always keep locally)

Alongside persona.json, maintain a persona.md with the full 4-dimension extraction + all evidence tags. Used for Phase 7 evolution. Not included in the skill pack.

Phase 7: Evolve

Enter evolution mode when the user says — don't restart from scratch:

  • Add material: "I found more chat logs" / "here's another source"
    → Preprocess new source → save to training/raw/ → merge into persona.md → conflict check → update persona.json → re-run Step 6-D (update conversations.jsonl + metadata.json) → re-run Phase 6-B → bump version
  • Correct: "they wouldn't say that" / "that description is wrong"
    → Locate persona.md section → revise → adjust evidence level → sync persona.json → update affected turns in training/conversations.jsonl → bump version
  • Rollback: /rollback {slug} {version}
    → python3 scripts/version_manager.py --action rollback --slug {slug} --version {version}

Print a diff summary after each update:

🔄 v0.1.0 → v0.1.1
  + 3 new L1 evidence items (Interaction dimension)
  ✏️  Revised speakingStyle — removed inaccurate catchphrase
  ↻  Regenerated skill pack → {slug}-skill/

Layer 0 Safety (hard rules — always enforced)

  1. Someone you know: not for harassment, stalking, or deception; does not replace real human connection; if unhealthy obsession is detected, gently suggest professional support
  2. Public figures: disclaimer required on first run; do not fabricate political views or private life details they haven't expressed
  3. Fictional characters (when distributing): Inspired-by mode required; output must differ meaningfully from the original IP
  4. Universal: the generated skill never speaks words the subject would absolutely never say — unless supported by L1/L2 evidence

Subject Strategy Reference

Subject Data strategy Output mode Copyright guard
Yourself Chat · diary · social archive Full persona —
Someone you know Chat · documents Full persona —
Public figure WebSearch · public documents Mental models + voice Disclaimer mode
Fictional (personal use) Wiki · user-provided Direct roleplay —
Fictional (distributing) Wiki · user-provided Inspired-by mode ✅ Active
Historical figure Documents · biographies · WebSearch Mental models + reconstruction Disclaimer mode
Archetype User description only Synthetic persona —

List existing personas

When the user says /list-anyone:

python3 ${CLAUDE_SKILL_DIR}/scripts/skill_writer.py --action list --base-dir ./.claude/skills

Display: codename · version · last updated · subject type.

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