SKILL: Lofn Evaluator — Panel of Experts Selection & Ranking
SkillMediaEvaluate, 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.
No other account needed.
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.
- Load
skills/lofn-core/SKILL.mdfor personality and Panel system. - Load
skills/lofn-core/PIPELINE.mdfor the MANDATORY execution pipeline. - Load
skills/evaluation/TASK_TEMPLATE.mdfor 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:
- Panel Generation — Select and configure the 3 panels (concept, medium, context) with appropriate experts and transformations
- 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:
- Define scoring facets (from Step 06)
- Score each output against the facets (1-10 scale)
- Score each output on the 7 Eligibility Properties (see QA SKILL.md §0A) — classify as ACCESSIBLE or AMBITIOUS
- Apply weights based on platform/goal
- Rank all 24 by weighted score
- Select top N based on request (default: best 4-6)
- 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
| Dimension | Weight (Competition) | Weight (Social) |
|---|---|---|
| Originality | 25% | 15% |
| Technical Execution | 20% | 15% |
| Emotional Impact | 20% | 25% |
| Bold Choice Quality | 15% | 10% |
| Platform Fit | 10% | 25% |
| Viral Potential | 10% | 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):
| Property | Weight |
|---|---|
| Body in the song | 15% |
| Adoptable hook | 20% |
| Vast emotional TAM | 15% |
| Specificity paradox | 15% |
| Cognitive ease | 10% |
| Vocal co-discovery | 15% |
| Sonic threshold | 10% |
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:
- Read
references/release_falsification_logging.md. - Copy
assets/release_record.template.jsoninto the run/release directory asrelease_record.json. - Fill route, archetype, seed source, AI vocabulary level, pre-release eligibility scores, and hypothesis BEFORE metrics arrive.
- Validate with
scripts/validate_release_record.py. - Update day 7 / 14 / 30 metrics later without rewriting the pre-registered hypothesis.
⚡ ACTIVATION
For Panel Generation:
- Research context — Current trends, cultural moment, platform requirements
- Select baseline experts — 6 per panel following composition rules
- Run panel debate — Full dissent, backtracking, synthesis
- Apply transformations — Group + Skeptic choices
- Output panel specification
For Selection & Ranking:
- Receive 24 outputs from pipeline
- Define/confirm scoring facets
- Score all 24 against facets
- Calculate weighted totals
- 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