Skill: c-narcissus/research-review-skill-factory (version 1.0.1)

--- name: research-review-skill-factory description: Build custom peer-review skills for specific research areas, problem families, and method combinations using OpenReview evidence. Use when Codex needs a compact meta-review skill factory that takes a research field or topic cluster, retrieves and synthesizes recent ICLR/OpenReview reviewer concerns and accepted-paper author response patterns, then generates a ClawHub-ready reviewer skill tailored to that field/problem rather than to one specific manuscript. license: MIT-0 metadata: openclaw: emoji: "review-factory" requires: anyBins: - python3 - python --- # Research Review Skill Factory Use this meta-skill to build a custom review skill for a specific research area, problem family, or method combination. It is broader than a manuscript-specific builder: the generated child skill should help review future papers in the selected area. ## Core Idea Create a field/problem-specific reviewer skill: ```text research area + problem set -> area profile -> OpenReview queries -> reviewer concern patterns -> custom area reviewer skill ``` Examples: - [REDACTED_SECRET] - [REDACTED_SECRET] - [REDACTED_SECRET] - [REDACTED_SECRET] ## Workflow 1. **Define the research area and problem set** - Ask for or infer the area scope: narrow field, parent fields, problem family, method families, theory objects, experiment settings, and target venues. - Use `references/research_area_profile_schema.md`. - Preserve narrow terms before broad terms. 2. **Generate OpenReview query plan** - Create 8-20 queries covering the exact area phrase, subproblems, method families, theory or benchmark keywords, closest baseline families, and broader fallback fields. - Check the current date and select the current ICLR year plus two previous public ICLR years unless the user specifies years. 3. **Retrieve public OpenReview evidence** - Use: ```bash python scripts/fetch_openreview_field_evidence.py --field "<query>" --years <Y1> <Y2> <Y3> --output "<evidence-dir>/<query-slug>" ``` - Collect reviewer concerns from accepted, rejected, withdrawn, and desk-rejected public submissions when available. - Use author responses only from accepted papers by default. 4. **Synthesize an area review-response bank** - Cluster reviewer concerns by category. - For each pattern, record trigger terms, reviewer concern, accepted-paper response pattern, what future papers in this area must show, and representative evidence. - Keep direct quotes short; paraphrase patterns and cite forum URLs. 5. **Generate the child area reviewer skill** - Use `scripts/init_research_area_review_skill.py` with a filled area profile JSON. - The generated child skill must include `SKILL.md`, `agents/openai.yaml`, `references/research_area_profile.md`, `references/openreview_review_response_bank.md`, `references/review_output_contract.md`, `references/subtle_logic_flaws.md`, `LICENSE.txt`, and `_meta.json`. 6. **Validate and package** - Run `quick_validate.py` on the child skill. - Run syntax checks on scripts. - Package the child skill only after confirming there are no raw evidence caches, PDFs, manuscripts, pycache, or private data. ## Generated Child Skill Requirements The child skill must instruct future reviewers to: - classify a submitted paper inside the target research area; - retrieve the local area review-response bank before writing review comments; - generate area-specific reviewer concerns and rebuttal/revision guidance; - cite OpenReview precedent with year, status, title, forum URL, and note type; - audit novelty, soundness, baselines, reproducibility, A+B incrementality, and subtle logic flaws; - provide light, moderate, and major revision paths. ## Evidence Rules - Never fabricate OpenReview titles, forum IDs, decisions, scores, or author responses. - Treat OpenReview evidence as precedent, not as law. - Do not include raw review dumps in the generated child skill. - If evidence is sparse, label the bank as `limited evidence` and include a broader fallback area. ## References - `references/research_area_profile_schema.md`: area/problem profile schema. - `references/openreview_area_evidence_workflow.md`: retrieval and synthesis protocol. - `references/generated_area_review_skill_contract.md`: generated child skill contract. - `references/subtle_logic_flaws.md`: reusable hidden-weakness checklist.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)