research-systematic-literature-review

name

research-systematic-literature-review

description

Full systematic literature review (PRISMA 2020 core) with discovery, screening, evidence extraction, synthesis, confidence grading, and adversarial stress-testing across papers. Prefer peer-reviewed published versions over preprints and use this skill either directly or as the coordinated literature stage inside a larger research workflow. Use when asked to run or audit a literature review, evidence synthesis, state-of-the-art survey with explicit methods, citation integrity checks, or confidence-rated conclusions from research publications.

Research Systematic Literature Review

Quick start

1. Require domain before substantive review work. Stop and ask if missing.
2. Collect optional inputs including whether the user wants deep technical/mathematical exposition. If technical_exposition is not provided, explicitly ask the user to choose between standard vs detailed_math. Apply defaults for missing non-domain inputs and log assumptions.
3. Check tool access. If web browsing/search is unavailable and no corpus or Zotero access is provided, stop and ask for browsing access, research-zotero, or a user corpus.
4. Initialize the review artifact pack with scripts/init_review_pack.py.
5. If Zotero is relevant, invoke research-zotero first or consume existing ./zotero/ artifacts.
6. Run discovery, deduplication (including preprint→published version resolution), screening, extraction, synthesis, and adversarial checks.
7. Generate PRISMA flow accounting with scripts/prisma_flow_md.py and insert it into <topic>.review.md.
8. Validate the full pack with scripts/validate_review_pack.py before returning output.

Input contract

Required input

• domain: mandatory. Do not continue without this value.

Optional inputs with defaults

• research_question: default to "What does current evidence show about in ?"
• inclusion_criteria: default to peer-reviewed primary studies (published) relevant to topic and domain; use high-quality preprints only when no published version exists or when recency is essential, and label them.
• exclusion_criteria: default to off-topic studies, non-substantive summaries, and sources without sufficient methodological detail.
• date_range: default to the last 10 years through today.
• study_types: default to experimental, observational, benchmarking, and systematic-review studies when relevant.
• language: default to English.
• population/context: default to the broad population/context implied by topic and domain.
• outcomes: default to efficacy/performance, robustness, safety, and transferability outcomes where applicable.
• quality_threshold: default to retain studies with at least moderate methodological quality and transparent reporting.
• technical_exposition: default to standard. If set to detailed_math, the <topic>.review.md must include formal definitions/notation, key equations/objectives, and (when relevant) theorem statements or proof sketches in the Synthesis section.
• zotero_library_type: optional. user or group when Zotero should be used as a source.
• zotero_library_id: optional. Zotero user ID or group ID.
• zotero_collection_key: optional. Restrict Zotero sync to a collection.
• zotero_query: optional. Zotero free-text query for item discovery.
• zotero_tags: optional. Restrict Zotero items by tag.
• zotero_access_mode: optional. api-key, oauth-key, or mcp.

Hard-stop and failover rules

• Stop immediately if domain is missing.
• Stop immediately if web browsing/search is unavailable and there is no user-provided corpus and no research-zotero artifact or Zotero API/MCP path.
• Continue with soft defaults only for non-domain fields and explicitly log all defaults under "Assumptions applied".

Default output contract

Primary file: <topic>.review.md

Supporting files: <topic>.protocol.md <topic>.search-log.md <topic>.screening-log.md <topic>.evidence-table.md

Optional support files when Zotero is used: <topic>.zotero-items.json <topic>.zotero-sync.md

Required sections in <topic>.review.md: Protocol Search Strategy Screening Decisions Evidence Table Synthesis Adversarial Stress Test Limitations Confidence Assessment PRISMA flow accounting

Artifact naming rules

• Normalize <topic> to lowercase hyphen-case for file names.
• Keep all outputs in one review directory.
• Refuse to overwrite existing artifacts unless explicit overwrite is requested.

Workflow (PRISMA 2020 core + adversarial pass)

1) Define protocol

• Use references/protocol-template.md.
• Record topic, domain, question, inclusion/exclusion criteria, date range, outcomes, and quality threshold.
• Log every default assumption.

2) Execute discovery and search logging

• Use references/search-strategy-template.md.
• Search multiple relevant sources and log exact query strings, filters, and retrieval dates.
• If Zotero access is available, use research-zotero or consume its artifacts and decide whether Zotero is:
  • a curated seed library
  • a citation cross-check source
  • a discovery source for saved collections/tags
• If ./zotero/zotero-items.json already exists, prefer consuming it over re-syncing.
• If Zotero MCP is available in the runtime, research-zotero should prefer it for interactive library inspection.
• If Zotero MCP is unavailable but API access is available, research-zotero should export items and log the sync before this skill consumes them.
• For major ML conferences hosted on OpenReview (e.g., ICLR/NeurIPS, and others when applicable), include OpenReview as a first-class discovery source (especially for 2025+ venue years when recency matters). Use the OpenReview API v2 (api2.openreview.net) for reproducible queries (e.g., notes/search + notes?id=...), and log venue group(s), query terms/fields (title/abstract), and whether you filtered to accepted papers.
• Prefer published/peer-reviewed indexing and publisher sources over preprint aggregators when both exist (e.g., venue/publisher pages, PubMed, ACL Anthology, ACM DL, IEEE Xplore, SpringerLink, etc.).
• Use preprint servers (arXiv/bioRxiv/medRxiv/SSRN) primarily for discovery and open-access full text.
• For every preprint candidate, attempt to resolve the peer-reviewed published version (check preprint DOI/journal-ref fields; search title+authors+venue; use Crossref/OpenAlex/Semantic Scholar as needed).
• If an accepted full conference/journal version exists (publisher page, proceedings, or an accepted OpenReview venue record), treat the preprint as a duplicate publication: keep the accepted/published version as the canonical record/citation; optionally retain the preprint URL only as a full-text access link in notes.
• arXiv ↔ OpenReview canonicalization rule (ML conferences): when both an arXiv preprint and an OpenReview forum record exist for the same paper, prefer the OpenReview record only if it corresponds to an accepted full venue paper (use venue / venueid cues; avoid treating Rejected_Submission / Withdrawn_Submission / “Submitted to …” as canonical). Keep arXiv as an access copy when helpful.
• Track deduplication decisions explicitly.
• Treat Zotero as a curated discovery aid, not as proof that a paper meets the final inclusion criteria. Every included paper still needs screening and evidence extraction.

3) Screen records and account for flow

• Use references/screening-template.md.
• Record title/abstract and full-text decisions with reasons.
• Deduplicate across versions (preprint vs conference/journal) and record as duplicate-publication with a note like "preprint superseded by published version".
• Maintain PRISMA count keys exactly as defined in the template.

4) Extract structured evidence

• Use references/evidence-table-template.md.
• Capture design, population/context, outcomes, key results, and risk of bias per study.
• Capture DOI, venue, and publication status (published vs preprint) for each included study. Cite the published version when available.

5) Synthesize findings and grade confidence

• Use references/report-template.md.
• Separate high-confidence findings, mixed evidence, and unresolved questions.
• State confidence rationale from consistency, quality, directness, and risk of bias.
• If technical_exposition=detailed_math, make the Synthesis section math-forward: define the core objects precisely, write the primary learning objectives/constraints, and summarize theoretical results using correct formal statements (without over-quoting).

6) Run adversarial stress-test

• Use references/adversarial-literature-checklist.md.
• Red-team causal claims, endpoint definitions, subgroup claims, publication bias, and citation integrity.
• Flag unsupported, misleading, or overgeneralized conclusions with concrete fixes.

7) Apply domain adapter

• Use references/domain-adapters.md.
• Start from the generic bias rubric and apply the domain-specific adapter before final conclusions.

8) Validate and finalize

• Generate PRISMA flow markdown with scripts/prisma_flow_md.py.
• Validate structural integrity and count consistency with scripts/validate_review_pack.py.
• Return the full review pack with explicit assumptions and known limitations.

Scripts

• scripts/init_review_pack.py: create deterministic Markdown scaffolds for protocol/search/screening/evidence/report.
• scripts/prisma_flow_md.py: parse standardized screening counts and emit PRISMA flow accounting Markdown.
• scripts/validate_review_pack.py: validate required sections, mandatory fields, and PRISMA count consistency.

References

• references/protocol-template.md
• references/search-strategy-template.md
• references/screening-template.md
• references/evidence-table-template.md
• references/prisma-core-checklist.md
• references/adversarial-literature-checklist.md
• references/domain-adapters.md
• references/report-template.md
• ../research-zotero/references/zotero-artifact-contract.md