back
loading skill details...
Distill anyone into a runnable OpenPersona skill pack — real or fictional, personal or public, living or historical. Collects chat logs, documents, and publi...
---
name: anyone-skill
description: "Distill anyone into a runnable OpenPersona skill pack — real or fictional, personal or public, living or historical. Collects chat logs, documents, and public content, extracts a 4-dimension persona, and generates a portable OpenPersona pack via skills/open-persona. Use when asked to distill, clone, or create a persona for any person or character."
license: MIT
compatibility: "Designed for Claude Code, Cursor, or OpenClaw. Requires Python 3. Uses WebSearch for public figures and fictional characters."
allowed-tools: Read Write Edit Bash WebSearch
metadata:
version: "1.1.2"
author: acnlabs
---
# 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`
**Extended chain (local model)**: `anyone-skill` → `persona-knowledge` → `persona-model-trainer` → runnable persona model
**Optional integration**: When `persona-knowledge` is installed (`skills/persona-knowledge/`), anyone-skill uses it for persistent storage, semantic search, and Knowledge Graph instead of writing directly to `training/raw/`. Detection:
```bash
# Check at start of Phase 3 — if this directory exists, use persona-knowledge integration
ls skills/persona-knowledge/SKILL.md 2>/dev/null && echo "persona-knowledge detected"
```
When detected, data flow becomes: `source → persona-knowledge ingest → MemPalace + KG + wiki → persona-knowledge export → training/`
## 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](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`
#### Path A: persona-knowledge detected
If `persona-knowledge` is installed, ingest each source via its pipeline:
```bash
python skills/persona-knowledge/scripts/ingest.py \
--slug {slug} --source <path> --persona-name "{Name}"
```
This automatically handles PII scanning, deduplication, MemPalace storage, KG extraction, and `sources/` backup. No manual `training/raw/` writing needed.
Report after each source:
`✅ [N] messages from [source] → persona-knowledge/{slug}`
#### Path B: no persona-knowledge (default)
**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.
#### persona-knowledge integration (when detected)
Before extraction, query MemPalace for an overview and use the wiki as a starting point:
```bash
# Get a ~170-token overview of what's been ingested
mempalace wake-up --wing {slug}
```
Then read existing wiki pages for structured knowledge using the `Read` tool:
- `~/.openpersona/knowledge/{slug}/wiki/identity.md`
- `~/.openpersona/knowledge/{slug}/wiki/voice.md`
- `~/.openpersona/knowledge/{slug}/wiki/values.md`
- `~/.openpersona/knowledge/{slug}/wiki/thinking.md`
Use the wiki content as evidence-grounded starting points for each dimension. Fill gaps with semantic search: `mempalace search "decision making style" --wing {slug}`
After extraction, update the wiki pages with new insights (following Phase 3 of persona-knowledge's SKILL.md).
### 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](references/output-format.md)
### Step 6-A: Build persona.json
Map extraction results to OpenPersona v0.17+ format:
```json
{
"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:
```bash
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)
```bash
npx openpersona install ./{slug}-skill
npx openpersona switch {slug}
```
### Step 6-D: Export Training Data (for persona-model-trainer)
> Full procedure and file templates: [references/training-export.md](references/training-export.md)
Export a `training/` directory with raw source files, distilled conversation turns, a character profile, and metadata. If `persona-knowledge` is available, run `export_training.py`; otherwise build files manually from Phase 3–4 outputs.
**With persona-knowledge** (versioned export — preferred):
```bash
python skills/persona-knowledge/scripts/export_training.py \
--slug {slug} --output training/
# Auto-assigns version (v1, v2, …). Override with --version v2.
# Writes export_version + export_hash to training/metadata.json.
# persona-model-trainer will record these as dataset_version + dataset_export_hash
# in training_summary.json, forming a complete provenance chain.
```
View export history at any time:
```bash
python skills/persona-knowledge/scripts/export_training.py --slug {slug} --list
```
**→ To train a local model**, pass the exported `probes.json` to `persona-model-trainer`:
```bash
bash skills/persona-model-trainer/scripts/pipeline.sh \
--slug {slug} \
--model google/gemma-4-E4B-it \
--source ./training \
--method mlx \ # or: unsloth (NVIDIA) / colab (no GPU)
--preset gemma4 \
--probes ./training/probes.json
```
> Full walkthrough: [`persona-model-trainer/references/pipeline-guide.md`](../persona-model-trainer/references/pipeline-guide.md)
### 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`:
```bash
python3 ${CLAUDE_SKILL_DIR}/scripts/skill_writer.py --action list --base-dir ./.claude/skills
```
Display: codename · version · last updated · subject type.don't have the plugin yet? install it then click "run inline in claude" again.