design-data-extraction
SkillMediaLets your agent design claude skill forms that structure how research data is extracted for evidence synthesis.
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Details
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About this skill
Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/design-data-extraction/SKILL.md and read by Ahel’s review.
Purpose
Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.
Input contract
required: [evidence_question, record_schema]
optional: [coding_guidance, unit_rules, quality_fields]
constraints: [field definitions and missing-data states must be explicit]
Procedure
- Derive fields from the evidence question and synthesis outputs.
- Define types, units, allowed values, coding rules, and provenance fields.
- Specify unknown, not reported, not applicable, and ambiguous states.
- Pilot the form on boundary records and revise only documented ambiguities.
If the extraction schema is fixed and studies are ready for methodological appraisal, consider audit-study-validity as the next tactic.
Output contract
produces: [extraction_schema, coding_rules, missing_data_policy, pilot_issues]
delta_fields: [decisions, assumption_updates, uncertainties, open_questions]
Quality gates
- Every output field has an extraction source and semantic definition.
- Missingness is not conflated with a negative finding.
Failure and counterexamples
Do not add fields that cannot affect the synthesis, and do not collapse incomparable units.
Provenance map
resolved: data-extraction-form
Signals
- GitHub stars
- 503
- Forks
- 42
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
design-data-extraction- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine
github.com/yogsoth-ai/de-anthropocentric-research-engine
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