abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Extract a schema-declared evidence record from a source, including methods, datasets, metrics/results, conditions, limitations, provenance, and explicitly missing/ambiguous fields.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill extract-evidence-record -a windsurf -y
name: extract-evidence-record description: "Extract a schema-declared evidence record from a source, including methods, datasets, metrics/results, conditions, limitations, provenance, and explicitly missing/ambiguous fields."
Extract a schema-declared evidence record containing methods, data, metrics, results, conditions, limitations, provenance, and explicit missing fields.
required: [source, extraction_schema] optional: [protocol_record, quality_rubric, condition_schema] constraints: [each extracted value is source-linked and missing or ambiguous fields remain explicit]
1. Identify source identity, study design, population, intervention/exposure, comparator, and outcome. 2. Extract methods, data, evaluation conditions, metrics, estimates, and limitations into the schema. 3. Record units, uncertainty, provenance links, and missing/ambiguous fields. 4. Run schema and consistency checks before releasing the record.
If the evidence record contains enough design and method detail for appraisal, consider `audit-study-validity` as the next tactic.
produces: [evidence_record, condition_record, source_links, missing_field_log, extraction_notes] delta_fields: [evidence_updates, uncertainties, open_questions]
Do not infer unreported baselines or merge records from different study versions without lineage evidence.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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