Claude Cursor Skill

evidence-review

Build evidence-controlled literature reviews and gap maps with source-status labels, claim registers, citation-role plans, traceability tables, and overclaim audits. Use when drafting or auditing review papers, thesis literature reviews, scoping reviews, or evidence syntheses whe

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Download yha9806-academic-writing-toolkit-archive_skills_evidence-review-184e482.zip · 6 KB
Part of yha9806/academic-writing-toolkit — 21 skills

Install

skills CLI npx skills add https://github.com/yha9806/academic-writing-toolkit/tree/main/archive/skills/evidence-review
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install yha9806-academic-writing-toolkit@llmmart
Git git clone https://github.com/yha9806/academic-writing-toolkit.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole yha9806/academic-writing-toolkit collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

/evidence-review - Evidence-Controlled Review Skill

Purpose

Create literature-review and review-paper workflows that are traceable from evidence to claim. This skill extends the toolkit's read-note-map-integrate pipeline with stricter evidence-status control, gap mapping, citation-role planning, paragraph-level claim traceability, and overclaim auditing.

Trigger Words

This skill activates on: evidence review, gap review, gap map, claim traceability, overclaim audit, review paper, scoping review, systematic narrative review, /evidence-review.

Core Rules

  1. Keep the central review scope explicit.
  2. Separate direct evidence, background evidence, methodological support, workflow/governance support, candidate-only records, unpublished work, and in-progress work.
  3. Do not fabricate references, DOI, PMID, sample size, venue, abstracts, results, or claims.
  4. Do not treat candidate-only, metadata-only, abstract-only, generated-stub, or manual-review records as full-text evidence.
  5. Do not treat adjacent-domain evidence as direct validation of the target domain.
  6. Preserve uncertainty and boundary language.
  7. Use the project's citation style or placeholder convention. If the project uses review placeholders, preserve [CITE: CitationKey].
  8. Draft section by section. Do not assemble a final chapter or paper until section-level controls are clean.

Workflow

1. Scope And Boundary Setup

Create a short scope statement with:

  • central review question
  • direct evidence domain
  • adjacent background domains
  • methodological support domains
  • excluded topics
  • key overclaim risks

2. Evidence Inventory

Inspect existing project files before drafting. Common locations include:

  • literature/reading_notes/
  • summaries/
  • evidence/
  • outputs/references_master.csv
  • outputs/search_log.md

Classify each source using references/evidence_status_schema.md.

3. Gap Map

Create or update:

  • evidence/evidence_matrix.csv
  • evidence/claim_register.csv
  • evidence/citation_plan.csv
  • evidence/remaining_gap_notes.csv
  • evidence/overclaim_risk_register.csv

Map each source to what it can legitimately support. If direct evidence is sparse, state that as a gap instead of filling the gap with background literature.

4. Section Drafting

For each section:

  1. Create an input-check report.
  2. Draft first-pass prose.
  3. Create paragraph-level claim traceability.
  4. Create citation usage audit.
  5. Create overclaim audit.
  6. Create revision notes.
  7. Revise into second-pass prose only after controls are clean.

5. Assembly Readiness

Before merging sections:

  • check every section has compatible draft maturity
  • check traceability and audit files exist
  • check CSV files import cleanly
  • audit cross-section flow
  • audit repetition and boundary-language duplication
  • audit integrated citation consistency
  • audit integrated overclaim risks

6. Export Or Sharing

Use /export only after the review has passed assembly and overclaim checks. Export is packaging, not revision.

Reading Notes Bridge

When the review starts from source reading, use the template in references/reading_notes_template.md. Every source note should record:

  • citation key or provisional source ID
  • source provenance
  • evidence status
  • relevance
  • key arguments
  • detailed notes
  • review connections
  • claim candidates
  • limitations and boundary cautions
  • follow-up questions

Output Templates

Use references/review_workflow_templates.md for recommended CSV columns and report structures.

Minimum section controls:

  • reports/<section>_input_check.md
  • docs/<section>_first_pass_draft.md or chapters/<section>_first_pass_draft.md
  • evidence/<section>_claim_traceability.csv
  • evidence/<section>_citation_usage_audit.csv
  • reports/<section>_overclaim_audit.md
  • reports/<section>_revision_notes.md

Optional Package Check

If Python is available, run:

python {skill_dir}/scripts/check_review_package.py <project_root>

This checks common directories, common evidence-control files, and CSV readability. It does not judge scientific validity.

Stop Conditions

Stop and report a blocker if:

  • the user prohibits searching but the requested claim requires new evidence
  • only candidate or abstract evidence exists for a central claim
  • a clinical, causal, deployment, or outcome claim lacks direct evidence
  • unpublished or in-progress work cannot be separated from published literature
  • citation provenance is missing for a source being used as evidence
Files (academic-writing-toolkit)
  • references
    • evidence_status_schema.md 2.2 KB
      # Evidence Status Schema
      
      | Status | Meaning | Allowed use |
      | --- | --- | --- |
      | `verified_full_text_supported` | Full text or structured full-text notes have been reviewed locally. | Can support claims within verified scope. |
      | `published_anchor_supported` | Published direct anchor for the central review domain. | Can support central claims with stated cautions. |
      | `direct_domain_full_text_supported` | Full-text evidence directly studies the target domain. | Domain-specific claims. |
      | `background_supported` | Full-text evidence from an adjacent background domain. | Context only. |
      | `methodological_support_only` | Evidence supports method rationale. | Method framing, not target-domain validation. |
      | `workflow_support_only` | Evidence supports workflow or decision-support framing. | Design logic, not direct validation. |
      | `governance_support_only` | Evidence supports reporting, audit, traceability, or lifecycle governance. | Governance design, not clinical or empirical correctness. |
      | `boundary_supported` | Scope or limitation boundary derived from evidence-control logic. | Prevents overclaiming. |
      | `metadata_only` | Bibliographic metadata only. | Candidate tracking only. |
      | `abstract_only` | Abstract but no verified full text. | Background scouting only unless explicitly allowed. |
      | `candidate_placeholder_only` | Candidate record without verified evidence. | Do not use as evidence. |
      | `unpublished_work_context_only` | Unpublished thesis, pre-submission, or under-review work. | Context only, not published literature. |
      | `in_progress_work_context_only` | In-progress system or experiment. | Context only, not validated evidence. |
      | `insufficient_evidence` | Claim lacks adequate support. | Revise, remove, or mark as gap. |
      
      ## Rules
      
      1. Direct evidence must match the target domain, task, data type, and claim level.
      2. Adjacent-domain evidence cannot validate target-domain performance.
      3. Method evidence supports rationale, not completed validation.
      4. Outcome claims require outcome evidence.
      5. A visible feature is not automatically a diagnosis.
      6. Auditability, provenance, or structured reporting is not clinical validation.
      7. If sample size or metadata conflicts, preserve the caution.
      
      
    • reading_notes_template.md 1.4 KB
      # Reading Notes Template
      
      ```markdown
      # Reading Notes: {Author} - {Title} ({Year})
      
      **Citation key**: {CitationKey or provisional ID}
      **Source**: {single-line citation or metadata record}
      **Source URL or provenance**: {URL, database, DOI, PMID, local path, or user-provided source}
      **Date read**: {YYYY-MM-DD}
      **Evidence status**: {verified_full_text_supported / metadata_only / abstract_only / candidate_placeholder_only / other status}
      **Relevance**: {review section, chapter, or gap area}
      **Manual check needed**: yes/no
      
      ---
      
      ## Key Arguments
      
      - {main argument}
      
      ## Detailed Notes
      
      ### p.{N}-{M} or section marker: {Topic}
      
      {Paraphrased note. Keep quotes short and page-specific if used.}
      
      ## Key Terms
      
      | Term | Meaning in context | Review relevance |
      | --- | --- | --- |
      | {term} | {meaning} | {connection} |
      
      ## Review Connections
      
      | Note point | Section | Connection type | Evidence role |
      | --- | --- | --- | --- |
      | {point} | {section_id} | supports / challenges / extends / boundary | direct / background / method / workflow / governance |
      
      ## Claim Candidates
      
      | Candidate claim | Citation key | Evidence status | Boundary language needed | Use or defer |
      | --- | --- | --- | --- | --- |
      | {claim} | {key} | {status} | {yes/no and wording} | use / defer / gap |
      
      ## Limitations And Boundary Cautions
      
      - {limitation}
      - {claim not supported by this source}
      
      ## Questions And Follow-ups
      
      - {question}
      
      ---
      
      *Last updated: {YYYY-MM-DD}*
      ```
      
      
    • review_workflow_templates.md 1.6 KB
      # Review Workflow Templates
      
      ## Evidence Matrix
      
      `evidence/evidence_matrix.csv`
      
      ```text
      citation_key,title,year,authors,source_type,evidence_status,scope_role,domain,task,key_findings,limits,source_path,provenance,manual_check_needed
      ```
      
      ## Claim Register
      
      `evidence/claim_register.csv`
      
      ```text
      section_id,claim_id,claim_text,evidence_status,citation_keys,source_paths,evidence_strength,overclaim_risk,boundary_language,manual_check_needed
      ```
      
      ## Citation Plan
      
      `evidence/citation_plan.csv`
      
      ```text
      section_id,citation_key,citation_role,evidence_type,source_path,use_case,priority,caution_note
      ```
      
      Citation roles:
      
      - direct_domain_anchor
      - clinical_or_domain_problem_framing
      - background_context
      - methodological_support
      - workflow_support
      - governance_support
      - boundary_support
      - unpublished_context
      - in_progress_context
      - candidate_only_do_not_use
      
      ## Remaining Gap Notes
      
      `evidence/remaining_gap_notes.csv`
      
      ```text
      gap_id,section_id,gap_description,evidence_needed,current_evidence_status,why_gap_matters,next_action
      ```
      
      ## Claim Traceability
      
      `evidence/<section>_claim_traceability.csv`
      
      ```text
      section_id,subsection_id,paragraph_id,main_claim,citation_keys_used,evidence_status,source_summary_paths,overclaim_risk,boundary_language_used,manual_check_needed
      ```
      
      ## Citation Usage Audit
      
      `evidence/<section>_citation_usage_audit.csv`
      
      ```text
      citation_key,subsections_used,citation_role,evidence_type,used_correctly,overused,underused,caution_note
      ```
      
      ## Overclaim Audit
      
      For each risk:
      
      ```text
      - risk:
      - status: absent / present / needs_revision
      - affected subsection:
      - recommended safer wording:
      ```
      
      
  • scripts
    • check_review_package.py 2.4 KB
      #!/usr/bin/env python3
      """Lightweight checks for an evidence-controlled review project."""
      
      from __future__ import annotations
      
      import argparse
      import csv
      from pathlib import Path
      
      
      COMMON_FILES = [
          "evidence/evidence_matrix.csv",
          "evidence/claim_register.csv",
          "evidence/citation_plan.csv",
          "evidence/overclaim_risk_register.csv",
      ]
      
      COMMON_DIRS = ["docs", "evidence", "reports", "summaries"]
      OPTIONAL_DIRS = ["literature", "literature/reading_notes", "chapters", "outputs", "final_output"]
      
      
      def read_csv(path: Path) -> tuple[bool, str]:
          try:
              with path.open("r", encoding="utf-8-sig", newline="") as handle:
                  reader = csv.DictReader(handle)
                  rows = list(reader)
              if reader.fieldnames is None:
                  return False, "no header row"
              return True, f"{len(rows)} rows"
          except Exception as exc:
              return False, str(exc)
      
      
      def main() -> int:
          parser = argparse.ArgumentParser()
          parser.add_argument("root", nargs="?", default=".", help="review project root")
          parser.add_argument("--strict", action="store_true", help="exit 1 if expected files are missing")
          args = parser.parse_args()
      
          root = Path(args.root).resolve()
          missing: list[str] = []
      
          print(f"Review project root: {root}")
          print("\nDirectory check:")
          for rel in COMMON_DIRS:
              path = root / rel
              status = "ok" if path.is_dir() else "missing"
              print(f"- {rel}: {status}")
              if status == "missing":
                  missing.append(rel)
      
          print("\nOptional academic-writing workflow directories:")
          for rel in OPTIONAL_DIRS:
              status = "ok" if (root / rel).is_dir() else "not present"
              print(f"- {rel}: {status}")
      
          print("\nCommon file check:")
          for rel in COMMON_FILES:
              path = root / rel
              if not path.exists():
                  print(f"- {rel}: missing")
                  missing.append(rel)
                  continue
              ok, detail = read_csv(path)
              status = "ok" if ok else "csv_error"
              print(f"- {rel}: {status} ({detail})")
              if not ok:
                  missing.append(rel)
      
          print("\nSummary:")
          if missing:
              print(f"- warnings: {len(missing)} issue(s)")
              for item in missing:
                  print(f"  - {item}")
              return 1 if args.strict else 0
      
          print("- no common package issues detected")
          return 0
      
      
      if __name__ == "__main__":
          raise SystemExit(main())
      
      
  • SKILL.md 4.9 KB
    ---
    name: evidence-review
    description: Build evidence-controlled literature reviews and gap maps with source-status labels, claim registers, citation-role plans, traceability tables, and overclaim audits. Use when drafting or auditing review papers, thesis literature reviews, scoping reviews, or evidence syntheses where adjacent-domain evidence, candidate records, unpublished work, and unsupported claims must be kept separate.
    allowed-tools: Read, Glob, Grep, Edit, Write, Bash
    ---
    
    # /evidence-review - Evidence-Controlled Review Skill
    
    ## Purpose
    
    Create literature-review and review-paper workflows that are traceable from evidence to claim. This skill extends the toolkit's read-note-map-integrate pipeline with stricter evidence-status control, gap mapping, citation-role planning, paragraph-level claim traceability, and overclaim auditing.
    
    ## Trigger Words
    
    This skill activates on: `evidence review`, `gap review`, `gap map`, `claim traceability`, `overclaim audit`, `review paper`, `scoping review`, `systematic narrative review`, `/evidence-review`.
    
    ## Core Rules
    
    1. Keep the central review scope explicit.
    2. Separate direct evidence, background evidence, methodological support, workflow/governance support, candidate-only records, unpublished work, and in-progress work.
    3. Do not fabricate references, DOI, PMID, sample size, venue, abstracts, results, or claims.
    4. Do not treat candidate-only, metadata-only, abstract-only, generated-stub, or manual-review records as full-text evidence.
    5. Do not treat adjacent-domain evidence as direct validation of the target domain.
    6. Preserve uncertainty and boundary language.
    7. Use the project's citation style or placeholder convention. If the project uses review placeholders, preserve `[CITE: CitationKey]`.
    8. Draft section by section. Do not assemble a final chapter or paper until section-level controls are clean.
    
    ## Workflow
    
    ### 1. Scope And Boundary Setup
    
    Create a short scope statement with:
    
    - central review question
    - direct evidence domain
    - adjacent background domains
    - methodological support domains
    - excluded topics
    - key overclaim risks
    
    ### 2. Evidence Inventory
    
    Inspect existing project files before drafting. Common locations include:
    
    - `literature/reading_notes/`
    - `summaries/`
    - `evidence/`
    - `outputs/references_master.csv`
    - `outputs/search_log.md`
    
    Classify each source using `references/evidence_status_schema.md`.
    
    ### 3. Gap Map
    
    Create or update:
    
    - `evidence/evidence_matrix.csv`
    - `evidence/claim_register.csv`
    - `evidence/citation_plan.csv`
    - `evidence/remaining_gap_notes.csv`
    - `evidence/overclaim_risk_register.csv`
    
    Map each source to what it can legitimately support. If direct evidence is sparse, state that as a gap instead of filling the gap with background literature.
    
    ### 4. Section Drafting
    
    For each section:
    
    1. Create an input-check report.
    2. Draft first-pass prose.
    3. Create paragraph-level claim traceability.
    4. Create citation usage audit.
    5. Create overclaim audit.
    6. Create revision notes.
    7. Revise into second-pass prose only after controls are clean.
    
    ### 5. Assembly Readiness
    
    Before merging sections:
    
    - check every section has compatible draft maturity
    - check traceability and audit files exist
    - check CSV files import cleanly
    - audit cross-section flow
    - audit repetition and boundary-language duplication
    - audit integrated citation consistency
    - audit integrated overclaim risks
    
    ### 6. Export Or Sharing
    
    Use `/export` only after the review has passed assembly and overclaim checks. Export is packaging, not revision.
    
    ## Reading Notes Bridge
    
    When the review starts from source reading, use the template in `references/reading_notes_template.md`. Every source note should record:
    
    - citation key or provisional source ID
    - source provenance
    - evidence status
    - relevance
    - key arguments
    - detailed notes
    - review connections
    - claim candidates
    - limitations and boundary cautions
    - follow-up questions
    
    ## Output Templates
    
    Use `references/review_workflow_templates.md` for recommended CSV columns and report structures.
    
    Minimum section controls:
    
    - `reports/<section>_input_check.md`
    - `docs/<section>_first_pass_draft.md` or `chapters/<section>_first_pass_draft.md`
    - `evidence/<section>_claim_traceability.csv`
    - `evidence/<section>_citation_usage_audit.csv`
    - `reports/<section>_overclaim_audit.md`
    - `reports/<section>_revision_notes.md`
    
    ## Optional Package Check
    
    If Python is available, run:
    
    ```bash
    python {skill_dir}/scripts/check_review_package.py <project_root>
    ```
    
    This checks common directories, common evidence-control files, and CSV readability. It does not judge scientific validity.
    
    ## Stop Conditions
    
    Stop and report a blocker if:
    
    - the user prohibits searching but the requested claim requires new evidence
    - only candidate or abstract evidence exists for a central claim
    - a clinical, causal, deployment, or outcome claim lacks direct evidence
    - unpublished or in-progress work cannot be separated from published literature
    - citation provenance is missing for a source being used as evidence
    
    

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