Extracts and assesses research methodology, claims, and evidence from research papers in HASS disciplines. Evaluates transparency, replicability, and credibility through systematic extraction of research designs, methods, protocols, claims, and evidence using a six-pass iterative workflow.
Systematic extraction and assessment framework for research methodology, claims, and evidence in HASS disciplines (archaeology, biology, ethnography, ecology, literary studies, philology, etc.).
This skill enables comprehensive extraction of research methodology and argumentation from academic papers through a structured multi-pass workflow:
The extracted data enables assessment of research transparency, replicability, and credibility.
Use when users request:
The complete extraction follows this sequence:
Blank JSON Template
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Claims/Evidence Pass 1 (liberal extraction)
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Claims/Evidence Pass 2 (rationalisation)
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RDMAP Pass 3 - Explicit (liberal extraction from Methods)
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RDMAP Pass 4 - Implicit (scan for undocumented items)
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RDMAP Pass 5 (rationalisation)
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Validation Pass 6 (integrity checks)
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Assessment-Ready Extraction
Key principle: Single JSON document flows through all passes. Each pass populates or refines specific arrays, leaving others untouched.
This skill provides:
The user provides:
Why this separation? Extraction prompts evolve frequently through testing and refinement. This architecture allows prompt tuning without modifying the skill package, minimizing versioning conflicts.
Users will typically request extraction at a specific pass. Listen for:
The user will provide the extraction prompt for the specific pass they want. These prompts are:
Claims/Evidence Extraction:
RDMAP Extraction:
Validation:
The prompts contain detailed instructions, examples, and decision frameworks for that specific extraction pass. Follow the prompt provided.
If you encounter uncertainty during extraction, consult:
Core Extraction Principles:
references/extraction-fundamentals.md - Universal sourcing requirements, explicit vs implicit extraction, systematic implicit RDMAP patterns, systematic implicit arguments patterns with 6 recognition patterns (ALWAYS read first for all extraction passes)references/verbatim-quote-requirements.md - Strict verbatim quote requirements (prevents 40-50% validation failures)references/verification-procedures.md - Source verification for Pass 6 validationSchema & Structure:
references/schema/schema-guide.md - Complete object definitions with inline examplesDecision Frameworks:
references/checklists/tier-assignment-guide.md - Design vs Method vs Protocol decisionsreferences/research-design-operational-guide.md - Operational patterns for finding all Research Designs (4-6 expected)references/checklists/consolidation-patterns.md - When to lump vs split items, cross-reference repair procedure (CRITICAL for Pass 2 & Pass 5)references/checklists/expected-information.md - Domain-specific completeness checklistsExamples:
references/examples/sobotkova-example.md - Complete worked exampleFollow the workflow guidance to:
["M003", "M007"]Evidence = Raw observations requiring minimal interpretation
Claims = Assertions that interpret or generalize
Test: "Does this require expertise to assess or just checking sources?"
Research Designs (WHY), Methods (WHAT), Protocols (HOW).
For complete tier assignment guidance: See references/checklists/tier-assignment-guide.md
Evidence items with identical claim support patterns that are never cited independently should be consolidated.
For complete algorithm, examples, and cross-reference repair:
→ See references/checklists/consolidation-patterns.md
For testing/debugging:
Expected outcomes:
Token efficiency:
Common user patterns:
Working with prompts:
Always:
The user will provide the detailed extraction prompt for each pass. Use this skill's reference materials to support decision-making during extraction.