Skip to content
Automation
Skill

/design-data-extraction

Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.

From plugin
de-anthropocentric-research-engine
499200 skills
Install
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill design-data-extraction --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/design-data-extraction

Context preview

The summary Claude sees to decide when to auto-load this skill.

Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis.

SKILL.md

design-data-extraction.SKILL.md
name: design-data-extraction
description: "Define a structured extraction form, field semantics, coding rules, and missing-data handling for evidence synthesis."

design-data-extraction

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

1. Derive fields from the evidence question and synthesis outputs. 2. Define types, units, allowed values, coding rules, and provenance fields. 3. Specify unknown, not reported, not applicable, and ambiguous states. 4. 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`
Read more
Ships withde-anthropocentric-research-engine

The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.

Get the whole plugin