secondme-skill

secondme-skill is a complete pipeline for building your AI Second Me — distill your identity from owned data, grow a private knowledge base, train a local model that speaks like you, and govern what gets shared. Local-first, privacy-first, fully yours.

This is an orchestration skill package. It does not replace lower-level capabilities.

Source of truth

• Persona source declaration:

  persona.json

• Pipeline runtime state:

  state/pipeline-state.json

• Product and governance spec:

  references/product-report.md

• Generated runtime pack:

  generated/persona-secondme-skill/

• Regeneration script:

  scripts/regenerate-pack.sh

Dependency chain

• Foundation:

  openpersona

  -> persona pack creation and lifecycle baseline
• Orchestration:

  secondme-skill

  -> workflow gates, state, and report contracts
• Capability chain:

  1. anyone-skill

     -> identity extraction and evidence grading

  2. persona-knowledge

     -> data ingestion, deduplication, wiki/KG, versioned export

  3. persona-model-trainer

     -> local training, evaluation, export, integration

Required execution policy

• Use non-interactive generation and scripts where possible.
• Keep local-first and least-privilege defaults.
• Keep stage outputs auditable with version/hash references.
• Treat

  persona-secondme-skill/

  as generated output (read-only baseline).
• When

  persona.json

  changes, regenerate the runtime pack before release.
• Before publishing outside this repository, run

  scripts/publish-check.sh

  .
• Preferred release check path:

  scripts/run-gates.sh

  (regenerate + sync + model gate + publish gate).

Stage contract

init

• Validate toolchain and directories.
• Initialize or load

  state/pipeline-state.json

  .

ingest

• Ingest user-owned data with PII scanning.
• Require explicit source authorization from user.

distill

• Build structured persona extraction artifacts.
• Ensure minimum persona input for OpenPersona is complete.

train

• Route by hardware tier:
  • Apple Silicon:

    mlx

  • NVIDIA:

    unsloth

  • No local GPU:

    colab

eval

• Check thresholds:

  • voice_score >= 3.5

  • probe_score >= 0.8

  • perplexity

    degradation <= 20% vs last viable version

integrate

• Integrate model artifacts only when eval gate passes.
• Require runtime pack persona model integration before marking stage pass.

report

• Emit three reports under

  reports/data

  ,

  reports/model

  ,

  reports/deploy

  .
• Keep

  report

  and deployment recommendation in blocked state if persona model gate fails.

Failure routing

• Data gate fail -> return to

  ingest

  and request source expansion.
• Train fail -> change backend or reduce model size.
• Eval fail -> augment data or retune hyperparameters, then retrain.

Always update

error_code

,

last_error

, and

retry_count

in pipeline state before retry.

Human approval gates

Require explicit human approval for:

• financial/legal commitments
• account-changing write actions
• external publishing/sharing of identity artifacts

Persona model gate

secondme

requires trained persona model integration, not only host default model fallback.

Pass criteria:

• generated/persona-secondme-skill/persona.json

  contains

  body.runtime.models

  .

• body.runtime.models

  has at least one model entry.

• scripts/check-model-integration.sh

  returns success.

If this gate fails,

report

must not be marked pass and deployment recommendation remains blocked.

Sync discipline

1. Edit root

   persona.json

   and orchestration docs first.
2. Run

   scripts/regenerate-pack.sh

   .
3. Run

   scripts/check-sync.sh

   to validate root and generated pack alignment.
4. Verify runtime pack path

   generated/persona-secondme-skill/

   exists and updated.
5. Only then produce release reports under

   reports/

   .