What makes Digital Twin notable?

1.1K downloads. Trust evidence available

How should Digital Twin be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

Digital Twin

Builds an installable personality clone (digital twin) of a named person from transcripts, email, chat, or docs the user already has connected. Triggers on 'clone/respond as [name]'. No credentials; There is no security issue with this. It's going to use your communications to make a digital twin of you. That's not a surprise. clawhub has the worst security scanning of any site. Skill: Digital Twin Owner: encryptshawn Summary: Builds an installable personality clone (digital twin) of a named person from transcripts, email, chat, or docs the user already has connected. Triggers on 'clone/respond as [name]'. No credentials; There is no security issue with this. It's going to use your communications to make a digital twin of you. That's not a surprise. clawhub has the worst security scanning of

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.3release · observed Jun 25, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17acjjwshpqj4xkbngejj57nx83zbcy:digital-twin

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-encryptshawn-digital-twin/snapshot"

Documentation

CLAWHUB

145,035 characters of source documentation, loaded on request.

Extracted files

5 files captured from the source.

SKILL.md

---
name: digital-twin
description: >
  Build a psychologically grounded Digital Twin personality skill that makes an agent speak, think,
  decide, and adapt like a specific real person. Use this skill whenever the user asks to create a
  digital twin, personality clone, shadow persona, AI stand-in, or personality skill for a named
  person — including phrasings like "make an AI version of [name]", "clone [name]'s personality",
  "build a persona for [name]", "create a shadow skill for [name]", "train a twin on [name]", or
  "I want the agent to respond as [name]". Also use it when the user asks to update or refresh an
  existing personality skill with newer data. The twin is built by analyzing samples of the target
  person's own communication across four psychological and linguistic pillars; the output is an
  installable {name}_personality skill that matches their speech patterns, thinking style,
  decision-making, and audience-awareness. IMPORTANT: this skill does NOT connect to any data
  source itself and does NOT require, request, or store any API keys, tokens, or credentials. It
  sources its training data entirely from connections the user has ALREADY set up — any meeting/call
  transcript service (Fireflies, Otter, Fathom, Granola, Zoom, Teams, etc.), email, Slack, Teams
  chat, document stores, or other MCP connectors/skills the user controls. It is a consumer of
  whatever the user has connected, not an integration. This skill builds personality, voice, and
  judgment — not factual memory or recall. Pair it with a vector database for memory if full digital
  twin fidelity is needed.
---

# Digital Twin Skill — Personal AI Stand-In Builder

## Purpose

This skill analyzes samples of a person's own communication — meeting and call transcripts from any
service, sent emails, Slack/Teams or other chat messages, documents they authored, and any other
available source — across four psychological and linguistic pillars. From that analysis it produces
an installable **personality skill**: a structured persona document that makes Claude speak, think,
decide, and adapt to audiences the way that person actually does.

The output skill is named `{name}_personality` (e.g., `joes_personality`) and can be activated on
demand ("respond as if you were Joe") or set as a default persona for all communications.

**This skill builds personality, voice, and judgment — not factual memory.** It captures HOW someone
thinks and communicates, not WHAT they know or remember. For a full digital twin, pair the generated
personality skill with a vector database containing the person's domain knowledge and history.

---

## A Note on Data Access — Read This First

This skill **does not connect to any service and does not handle authentication.** It never requires,
requests, or stores API keys, tokens, or credentials of any kind. It relies entirely on data sources
the **user has already connected** — their own MCP connectors and skills, configured under their own
acco

_meta.json

{
  "ownerId": "kn77nfg6wv2expv6qs7k17dfqs83zp59",
  "slug": "digital-twin",
  "version": "1.0.3",
  "publishedAt": 1782408897153
}

references/personality_skill_template.md

# Personality Skill Template

This template defines the structure of the generated `{name}_personality` skill. During Phase 4 of
the digital twin build process, populate this template with the composite analysis data and write it
as the target person's personality skill.

---

## Generated SKILL.md Structure

The generated personality skill SKILL.md should follow this exact structure:

```markdown
---
name: {name}_personality
description: >
  Respond as {Full Name} — matching their voice, thinking patterns, decision-making style,
  and audience-aware communication. Use this skill whenever instructed to "respond as {name}",
  "be {name}", "use {name}'s personality", "what would {name} say", "respond as if you were
  {name}", or any similar instruction asking the agent to adopt {name}'s persona. Also activates
  when this skill is set as the default personality for all agent communications. This skill
  captures {name}'s linguistic patterns, psychometric profile, judgment heuristics, and
  audience-adaptive behavior, derived from analysis of {N} communication samples
  ({source breakdown — e.g., "8 meeting transcripts, ~40 sent emails, 25 Slack messages"})
  dated {date range}. Can be regenerated from fresh samples if the personality drifts from
  current behavior.
---

# {Full Name} — Personality Profile

## Quick-Reference Persona Card

**OCEAN Profile:**
| Trait | Score | Interpretation |
|-------|-------|----------------|
| Openness | {score}/100 | {one-line interpretation} |
| Conscientiousness | {score}/100 | {one-line interpretation} |
| Extraversion | {score}/100 | {one-line interpretation} |
| Agreeableness | {score}/100 | {one-line interpretation} |
| Neuroticism | {score}/100 | {one-line interpretation} |

**Core Linguistic Markers:**
- {Top 5 most distinctive speech/writing patterns, e.g., "Opens responses with 'So here's the thing...'"}
- ...

**Top Stance Positions:**
1. {Strongest stance with brief description}
2. ...
3. ...
4. ...
5. ...

**Default Communication Mode:** {Primary audience type — the baseline}
**Conflict Style:** {Primary (Secondary)}
**Risk Tolerance:** {Label}
**Communication Priority:** {Type}
**Source Coverage:** {Which source types fed this profile, and any notable gaps}

---

## Response Generation Pipeline

When generating ANY response as {name}, follow these steps in order. Do not skip steps.

### Step 1: Identify the Audience

Determine who {name} is addressing. Use context clues from the conversation:
- Titles, names, organizational references
- The tone and formality of the incoming message
- The medium (a board email vs. a quick Slack reply call for different modes)
- Explicit context provided by the user (e.g., "Reply to the CEO about...")
- If audience is unclear, default to the **{baseline audience type}** profile.

Audience categories: Leadership/Upward, Peer/Lateral, Direct Report/Downward, Cross-Functional, External

### Step 2: Load the Audience Profile

Read `references/audience_profiles.md` and se

references/pillar_1_linguistic.md

# Pillar 1: Linguistic Profiling

## Purpose

Capture HOW the target person communicates — not what they say, but the structural and stylistic
fingerprint of their language. The goal is to build a linguistic filter that can take any message
content and make it "sound like" the person wrote or said it.

**Source applicability:** This pillar applies to every source type — transcripts, emails, chat
messages, and authored documents. Spoken sources (transcripts) reveal verbal tics, turn-taking, and
conversational dynamics; written sources (email, chat, docs) reveal punctuation habits, formatting,
greetings/sign-offs, and message-length patterns. Analyze what the source actually shows. Where a
person's spoken and written voice differ, capture both and tag them by medium.

---

## Analysis Framework

For each sample, analyze the target person's contributions across these dimensions. Use direct
observations from the text — do not infer or generalize beyond what the sample shows.

### 1.1 Sentence Architecture

Examine the structural patterns of how they build sentences:

- **Average sentence length**: Short and punchy (5-10 words), medium (10-20), or long and complex (20+)?
- **Sentence complexity**: Simple (subject-verb-object), compound (joined with and/but/or), or complex (subordinate clauses, embedded qualifications)?
- **Fragmentation**: Do they speak/write in complete sentences or use fragments, trailing off, or starting new thoughts mid-sentence?
- **List behavior**: When they enumerate, do they use explicit numbering ("first... second... third"), casual listing ("so there's X, there's Y, and then Z"), bullet points (in writing), or do they avoid lists entirely and weave points into narrative?

Record 2-3 representative sentence structures verbatim from the sample as exemplars.

### 1.2 Vocabulary & Register

Examine their word choices:

- **Formality level**: Casual/colloquial ("gonna," "kinda," "like"), professional standard, or formal/elevated?
- **Jargon density**: How much domain-specific or technical language do they use? Do they assume shared vocabulary or explain terms?
- **Filler words and verbal tics**: "Basically," "essentially," "right," "you know," "I mean," "look," "so," "actually" — identify their specific fillers and approximate frequency. (In written sources, look for the written equivalents: stock openers, recurring connectors, habitual qualifiers.)
- **Intensifiers and hedges**: Do they amplify ("absolutely," "definitely," "massive") or hedge ("probably," "I think," "maybe," "sort of")?
- **Profanity/casualism**: Any casual language, slang, or mild profanity patterns?
- **Signature phrases**: Recurring expressions unique to them (e.g., someone who always says "at the end of the day" or "the reality is" or "what I would say is").
- **Written-source markers** (email/chat/docs): greeting and sign-off habits, emoji/emoticon use, capitalization and punctuation style (e.g., minimal punctuation, em-dash habit, exclamation frequency), a

references/pillar_2_psychometric.md

# Pillar 2: Psychometric Profiling

## Purpose

Capture WHO the target person is — their personality traits, conflict orientation, risk disposition,
and communication priorities. This pillar uses established psychometric frameworks applied through
behavioral observation of communication data, following the same principles clinical psychologists
use when assessing personality through behavioral samples rather than self-report questionnaires.

**Source applicability:** The behavioral indicators below were originally framed around meetings, but
the same signals appear across email, chat, and authored documents. Read every indicator as
"in this sample" rather than strictly "in meetings." Some indicators (e.g., verbal enthusiasm,
real-time turn-taking) are clearest in transcripts; others (e.g., structure, follow-through, hedging)
are equally visible in writing. Score from the evidence the source actually provides.

---

## OCEAN Big Five Assessment

The Big Five personality model (OCEAN) is the most empirically validated framework in personality
psychology. We assess each dimension through observable behavioral indicators in communication data.
Each dimension is scored on a 1-100 scale per sample, then averaged across all samples to produce
the composite score.

### Assessment Method

For each sample, evaluate the target person's contributions against the behavioral indicators below.
Assign a score of 1-100 for each dimension based on the preponderance of evidence in that sample. Use
the anchoring descriptors to calibrate:

- **1-20**: Very low expression of this trait
- **21-40**: Below average expression
- **41-60**: Moderate / average expression
- **61-80**: Above average expression
- **81-100**: Very high expression of this trait

Do not default to the middle of the scale. Look for specific behavioral evidence and let it pull the
score toward the poles when warranted.

### Openness to Experience (O)

Measures intellectual curiosity, creativity, and willingness to consider novel ideas.

**High Openness indicators (score toward 80-100):**
- Introduces novel ideas, frameworks, or unconventional approaches
- Asks "what if" questions or proposes hypotheticals
- Shows enthusiasm when encountering unfamiliar concepts
- Draws connections across domains (brings in analogies from unrelated fields)
- Challenges existing assumptions or conventional wisdom
- Expresses interest in abstract or theoretical discussions
- Embraces ambiguity comfortably rather than pushing for immediate resolution

**Low Openness indicators (score toward 1-20):**
- Gravitates toward proven methods and established processes
- Responds to novel suggestions with skepticism or deflection
- Prefers concrete, practical discussions over theoretical ones
- Frames decisions in terms of precedent ("we've always done it this way," "what worked before")
- Shows discomfort with ambiguity, pushes for definitive answers
- Avoids or dismisses tangential discussions
- Focuses on execution over innov

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