extract-evaluation-protocol
SkillDev toolsLets your agent pull evaluation details like metrics and baselines from a source and flag what's missing or unclear.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the extract-evaluation-protocol skill
About this skill
Extract evaluation-protocol elements from a source and mark unstated or ambiguous parameters.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/extract-evaluation-protocol/SKILL.md and read by ahel’s review.
Purpose
Extract evaluation-protocol elements from a source and mark unstated or ambiguous parameters.
Input contract
required: [source_record, protocol_schema]
optional: [supplementary_materials, domain_defaults]
constraints: [extracted values must distinguish stated, inferred, and missing]
Procedure
- Parse task, data, split, metric, baseline, budget, evaluator, and reporting elements.
- Link each value to a source passage or record field.
- Mark ambiguity, omission, and any inference made from context.
- Emit a normalized protocol record with comparability notes.
If multiple protocols can now be represented on the same comparison schema, consider compare-evaluation-protocols as the next tactic.
Output contract
produces: [protocol_record, source_links, ambiguity_log, comparability_notes]
delta_fields: [evidence_updates, uncertainties, open_questions]
Quality gates
- Missing fields are explicit.
- Inferred defaults are never represented as stated facts.
Failure and counterexamples
Do not fill omitted protocol values from common practice without an inference label.
Provenance map
resolved: protocol-element-extraction
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Key
extract-evaluation-protocol- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine