SKILL: Lofn Evaluator — Panel of Experts Selection & Ranking

SkillMedia

Evaluate, score, rank, and select Lofn outputs using panels, facets, eligibility scoring, and platform fit. Use after 24 outputs exist or when choosing finalists. Do NOT use to generate songs/images or perform QA formatting cleanup.

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the SKILL: Lofn Evaluator — Panel of Experts Selection & Ranking skill

What this skill tells your AI

The instructions your AI receives, as published by localsymmetry/lofn in skills/evaluation/SKILL.md and read by ahel’s review.

PREREQUISITES: 0. Load resources/panel-of-experts.md to understand the panel of experts prompting you will use.

  1. Load skills/lofn-core/SKILL.md for personality and Panel system.
  2. Load skills/lofn-core/PIPELINE.md for the MANDATORY execution pipeline.
  3. Load skills/evaluation/TASK_TEMPLATE.md for exact evaluation requirements.

⚠️ This skill handles the critical panel selection and ranking phases of the pipeline. The evaluator makes or breaks the creative direction.


🎯 PURPOSE

The evaluator has two core functions:

  1. Panel Generation — Select and configure the 3 panels (concept, medium, context) with appropriate experts and transformations
  2. Selection & Ranking — Score and rank the 24 outputs from any modality, selecting the best N for delivery

📊 SELECTION & RANKING

Use Select_Best_Pairs.md for ranking outputs.

Requirements

After the pipeline generates 24 outputs:

  1. Define scoring facets (from Step 06)
  2. Score each output against the facets (1-10 scale)
  3. Score each output on the 7 Eligibility Properties (see QA SKILL.md §0A) — classify as ACCESSIBLE or AMBITIOUS
  4. Apply weights based on platform/goal
  5. Rank all 24 by weighted score
  6. Select top N based on request (default: best 4-6)
  7. Ensure pattern alignment — selections must align with the orchestrator's chosen pattern (BARBELL, ALL-ACCESSIBLE, ALL-AMBITIOUS, GRADIENT, CONTRAST-PAIRS). When BARBELL is selected, at least 40% of selections should lean toward the run's intended barbell classification.

Scoring Dimensions

DimensionWeight (Competition)Weight (Social)
Originality25%15%
Technical Execution20%15%
Emotional Impact20%25%
Bold Choice Quality15%10%
Platform Fit10%25%
Viral Potential10%10%

Eligibility Scoring (EMBEDDED 2026-05-02 — 14-panel meta-analysis)

In addition to creative scoring, every output is scored on 7 eligibility properties (full rubric: QA SKILL.md §0A):

PropertyWeight
Body in the song15%
Adoptable hook20%
Vast emotional TAM15%
Specificity paradox15%
Cognitive ease10%
Vocal co-discovery15%
Sonic threshold10%

Accessible runs: eligibility average ≥3.5 is a HARD GATE. Below threshold → flagged for revision. Ambitious runs: eligibility scoring is informational only (artistic identity, not mass reach).

Output Format

## Ranking Results

### Top Selections

| Rank | Title | Pair | Variation | Score | Key Strengths |
|------|-------|------|-----------|-------|---------------|
| 1 | ... | A | 3 | 8.7 | ... |
| 2 | ... | C | 1 | 8.4 | ... |
| 3 | ... | B | 4 | 8.2 | ... |
| 4 | ... | D | 2 | 8.0 | ... |

### Scoring Breakdown (Top 4)

#### Rank 1: [Title]
- Originality: 9/10 — [rationale]
- Technical: 8/10 — [rationale]
- Emotional: 9/10 — [rationale]
- Bold Choice: 8/10 — [rationale]
- Platform Fit: 8/10 — [rationale]
- Viral Potential: 9/10 — [rationale]
- **Weighted Total: 8.7**

[repeat for top 4]

### Panel Notes on Selection
[Key insights from the panel debate about why these won]

Release / Falsification Logging (ADDED 2026-05-02)

Before any candidate is published or treated as a formula test:

  1. Read references/release_falsification_logging.md.
  2. Copy assets/release_record.template.json into the run/release directory as release_record.json.
  3. Fill route, archetype, seed source, AI vocabulary level, pre-release eligibility scores, and hypothesis BEFORE metrics arrive.
  4. Validate with scripts/validate_release_record.py.
  5. Update day 7 / 14 / 30 metrics later without rewriting the pre-registered hypothesis.

⚡ ACTIVATION

For Panel Generation:

  1. Research context — Current trends, cultural moment, platform requirements
  2. Select baseline experts — 6 per panel following composition rules
  3. Run panel debate — Full dissent, backtracking, synthesis
  4. Apply transformations — Group + Skeptic choices
  5. Output panel specification

For Selection & Ranking:

  1. Receive 24 outputs from pipeline
  2. Define/confirm scoring facets
  3. Score all 24 against facets
  4. Calculate weighted totals
  5. Select top N with rationale

The evaluator's judgment shapes the final output. Choose wisely. Score rigorously.

Signals

GitHub stars
22
Forks
1
Last commit
Aug 2026
Advanced
Catalog kind
skill
Gateway key
lofn-evaluation
Source
github.com/localsymmetry/lofn