silicon-doppelganger_skill

This skill helps you create accurate personal proxy agents by extracting personality and decision patterns into portable schemas for PAIRL integration.
  • Python

20

GitHub Stars

4

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

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Installation

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npx veilstrat add skill leegonzales/aiskills --skill silicon-doppelganger

  • CHANGELOG.md2.0 KB
  • LICENSE1.0 KB
  • README.md8.4 KB
  • SKILL.md8.7 KB

Overview

This skill builds psychometrically accurate personal proxy agents ("Digital Twins") for the PAIRL Conductor system. It extracts personality, decision heuristics, and values into a portable persona schema that lets agents negotiate, filter, and act on a principal's behalf. The output is a Conductor-ready persona profile that preserves fidelity across sessions and systems.

How this skill works

Run a structured workflow: a guided interview collects psychometrics, linguistic samples, narrative identity, and decision heuristics. The data is encoded into a compact XML persona schema that defines core drivers, decision logic, agent rules, and stress behaviors. The proxy is validated via behavioral testing against the principal and then integrated as a spoke in PAIRL Conductor with explicit must_reject, must_protect, and should_prefer rules.

When to use it

  • Create a personal proxy agent to accept or reject tasks automatically
  • Build a Digital Twin for PAIRL Conductor integration and calendar negotiation
  • Extract robust personality and decision patterns for agent representation
  • Validate an AI proxy against real-world principal choices
  • Model team dynamics by simulating multiple proxies

Best practices

  • Conduct a 45–60 minute structured interview and collect concrete examples and quotes
  • Capture psychometrics (CliftonStrengths, VIA) and communication samples for a linguistic fingerprint
  • Encode explicit agent rules (must_reject, must_protect, should_prefer) with clear thresholds
  • Run a question battery and target ≥80% lenient-match accuracy before deployment
  • Document contradictions, shadow behavior, and blind spots rather than smoothing them

Example use cases

  • Proxy negotiates calendar slots and declines requests that violate energy or craft boundaries
  • Team leads simulate partnership friction by loading multiple persona schemas
  • Individuals surface blind spots and produce a ‘how I work’ artifact for collaborators
  • WritingPartner uses the linguistic fingerprint to calibrate tone and phrasing
  • Deploy a proxy to filter incoming email and surface only aligned requests

FAQ

Aim for 80%+ lenient-match accuracy on a behavioral question battery; refine mismatches iteratively.

What artifacts are produced?

A Conductor-ready XML persona schema, an origin-story document, extraction checkpoints, and validation question sets.

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silicon-doppelganger skill by leegonzales/aiskills | VeilStrat