persona-evaluator — Persona Quality Auditor

Score any OpenPersona persona pack against the 4+5 framework standard: 4 Layers (Soul · Body · Faculty · Skill) × 5 Systemic Concepts (Evolution · Economy · Vitality · Social · Rhythm) + Constitution compliance gate.

persona-evaluator reads persona.json, generated artifacts, and soul files to produce a structured 9-dimension report — calibrated to the OpenPersona quality standard, with role-aware severity and three modes for self / peer / black-box review.

Quick Start

# Evaluate an installed persona (static / structural)
npx openpersona evaluate <slug>

# JSON output (for scripting or CI)
npx openpersona evaluate <slug> --json

# Save report to file (always JSON; --json not needed alongside --output)
npx openpersona evaluate <slug> --output report.json

# Embed evaluable persona content (Soul/character/behavior-guide) so an
# LLM evaluator (this skill, acting through an agent) can also judge
# quality semantically — not just structurally
npx openpersona evaluate <slug> --pack-content

Choosing a mode

persona-evaluator runs in three complementary modes. Pick the mode based on what the user asks before reading the rest of this file.

Structural is the default. Switch to semantic only when the user explicitly asks for narrative quality review (e.g. "evaluate me semantically", "self-review my pack", "qualitative audit"). Switch to black-box only when you cannot read the subject's persona.json on disk.

Sections below cover each mode in depth: structural (What Gets Scored), semantic white-box (Semantic Evaluation), and semantic black-box (Black-box Semantic Evaluation).

What Gets Scored

The structural CLI scores 9 dimensions + the Constitution gate. Severity (strict / normal / lenient) is set per dimension by the persona's declared role.

Each dimension produces a 0–10 score, a list of issues (✗), and suggestions (→). The overall score is a severity-weighted average — see Role-aware scoring.

Role-aware scoring

The structural evaluator already reads soul/identity.role and assigns each dimension a severity. The semantic reviewer must respect those severities (see references/RUBRICS.md for the rubric anchors).

Built-in role profiles

If soul/identity.role is missing or unrecognised, the evaluator falls back to assistant.

Reading the Report

Each dimension shows:

✓  identity                        9/10  (strict)
✗  character.boundaries            4/10  (strict)
   ✗ no hard limits declared in `boundaries`
   → add at least one enforceable rule (cite §3 Safety)

• ✓ / ✗ — pass / fail at this dimension's severity threshold.
• (strict | normal | lenient) — severity from the role profile.
• ✗ ... — required issue that must be fixed to pass.
• → ... — optional suggestion (does not block scoring).

The summary footer prints overall score, Constitution status, and a sorted list of dimensions by severity.

Score bands

A Constitution §3 violation caps the overall score at 3 regardless of other dimensions.

Semantic Evaluation (LLM-driven)

Structural mode is deterministic. Semantic mode is the LLM agent's qualitative review — narrative quality of background, personality, speakingStyle, voice fidelity in behavior-guide.md, etc. Two scenarios share the same procedure:

• Self-evaluation: the host persona reviews its own pack.
• Peer-evaluation: an installed evaluator reviews a different persona pack the user supplies.

When to invoke semantic mode

Trigger semantic mode only when the user explicitly asks for it — phrases like "evaluate me semantically", "self-review my pack", "peer-review this persona", "qualitative audit". Otherwise, default to structural mode.

Procedure

1. Run the structural CLI with --pack-content:

 npx openpersona evaluate <slug> --pack-content

2. Stop and report immediately if constitution.passed === false. Do not produce semantic scores when §3 has failed; the structural blockers must be fixed first.
3. Read report.packContent from the JSON. It includes (where defined): character.{background,personality,speakingStyle,boundaries}, immutableTraits, aesthetic.{emoji,creature,vibe}, and a whitelisted soulDocs map keyed by filename (behavior-guide.md, self-narrative.md, identity.md — only those that exist).
4. Score each present field 0–10 using the rubrics in references/RUBRICS.md. Use the per-dimension severity already attached to each dimension by the structural evaluator (strict / normal / lenient) to gate which checks count.
5. Emit the report in the white-box format defined in references/REPORT-FORMAT.md (## White-box format). Keep it under ~500 words.

Mode: self-evaluation

You are evaluating your own pack. The user has invited you (the host persona) to review yourself.

• What this is for: Catch own blind spots and surface concrete polish targets.
• Your bias: Self-flattery and minimisation. You will instinctively justify why your background is "deep enough" or your boundaries are "implied".
• Counter-bias instruction: For every per-field score, before deciding the number, write one sentence answering: "If I weren't me, what specifically would I down-score about this field?" Then score.
• Acceptable output tone: First person ("My speakingStyle…"), candid about gaps. Avoid "I think this is great." Avoid generic praise.

Mode: peer-evaluation

You are evaluating a different persona. The user has invited you (Reviewer-X) to look at Subject-Y.

• What this is for: Bring an outside perspective. Self-eval can't see what's missing; peer-eval can.
• Your bias: Standards-projection. If you are a strict-Skill assistant, you will instinctively want Subject-Y to also be Skill-rigorous, even if Subject-Y is a companion.
• Counter-bias instruction: Score Subject-Y against its declared role, not yours. Re-read the role and weights block before each rubric. Lower expectations for lenient dimensions even if you personally find them important.
• Acceptable output tone: Third person ("Subject's background…"). State your own role at the top so the reader can adjust for any leak-through.
• Disclose disagreements with the role itself: If you genuinely think the declared role is wrong (e.g. labelled companion but reads like assistant), say so as a separate cross-cutting observation — don't silently re-score against your preferred role.

Black-box Semantic Evaluation

Everything above assumes you can read the subject's persona.json and soul/*.md. That's false in the most common peer-audit scenario: you're asked to evaluate another agent whose pack you cannot read. In that case the rubrics are the same; what changes is the data source and the confidence cap.

Three data-source tiers, in descending fidelity:

Tier 1 produces the regular white-box report (header line: Data source: A2A pack-content handshake from <subject-slug>). Tier 2 and Tier 3 produce a separate black-box report. Never escalate tiers silently.

Full mechanics — handshake schema, probe table, identity-coherence dimension, confidence-cap justification, and hard rules — live in references/BLACK-BOX.md.

The black-box report format is in references/REPORT-FORMAT.md (## Black-box format).

Acting on Findings

Fix §3 violations first

Constitution violations are hard blocks — they cap the score at 3 regardless of everything else. Open soul/behavior-guide.md and remove any capability declarations that violate §3 Safety.

Fix issues before suggestions

Issues (✗) indicate missing required elements or broken configurations. Suggestions (→) are optional enhancements. Prioritize issues in low-scoring dimensions.

Apply fixes via refine

For Soul-layer fixes (background depth, speaking style, boundaries):

npx openpersona refine <slug> --emit    # request refinement via Signal Protocol
# (host LLM generates improvements)
npx openpersona refine <slug> --apply   # apply approved refinement

For structural fixes (missing faculty, missing minTrustLevel): Edit persona.json directly and regenerate:

npx openpersona update <slug>           # regenerate from updated persona.json

After applying any fix, re-run npx openpersona evaluate <slug> (see Quick Start) to verify the score improved and Constitution gate passes.

CI Integration

# .github/workflows/persona-quality.yml
- name: Evaluate persona quality
  run: |
    npx openpersona evaluate ${{ env.PERSONA_SLUG }} --output report.json
    SCORE=$(jq '.overallScore' report.json)
    if [ "$SCORE" -lt 6 ]; then
      echo "Persona quality score $SCORE < 6 — review required"
      exit 1
    fi

Relationship to Other Skills

Install

persona-evaluator ships bundled with the OpenPersona framework and is available immediately after installing it:

npm install -g openpersona
# persona-evaluator is included — no separate install needed
npx openpersona evaluate <slug>

A standalone distributable is also available at acnlabs/persona-evaluator on GitHub and listed on openpersona.co/skill/persona-evaluator.

Versioning

Current version: 0.3.4 (also in frontmatter metadata.version).

See CHANGELOG.md for full version history, rationale, test surface, and re-validation evidence. The deeper rubric review trail lives in docs/SKILL-RUBRIC.md and docs/SKILL-RUBRIC-SESSION-2.md in the main OpenPersona repo.