scispark

name

scispark

description

Generate evidence-tracked research ideas through an arXiv-based, skill-native Scispark workflow. Use when Codex needs to turn a research keyword, question, paper set, Zotero/library material, or arXiv results into structured facts, testable hypotheses, an initial research idea, technical optimization, mechanism-of-action analysis, human-AI collaboration review, or optional academic slides. Also use when the user mentions Scispark, 科研想法生成, 研究假设生成, 机制优化, MoA, literature-backed idea generation, or 从关键词到研究方案.

Scispark

Overview

Use this skill to turn a research keyword or early topic into a staged, evidence-tracked research idea. This is a skill-native adaptation of the Tashan Scispark workflow: the current Codex model does the reasoning, and arXiv is the paper-search route.

The normal workflow does not require an external model API key or separate search product account.

Resources

• scripts/init_scispark_workspace.py: create the standard output folders and starter files.
• scripts/search_arxiv.py: query arXiv and output normalized Scispark literature records.
• references/arxiv-integration.md: arXiv search, evidence status, and threshold rules.
• references/stage-contracts.md: required inputs and outputs for each stage.
• assets/final_idea_template.md: final research idea report structure.

Workflow

1. Parse the user request into a keyword, domain, constraints, and target stage.
2. Create or identify the output directory. Default:

03-AI笔记/scispark/{keyword}/

3. Read references/arxiv-integration.md, then run scripts/search_arxiv.py for literature search. Keep the actual query terms, source route, and status.
4. Read references/stage-contracts.md before writing stage files.
5. Execute stages in order unless the user asks for a target stage:
   • Stage 1: fact extraction
   • Stage 2: hypothesis generation
   • Stage 3: initial research idea
   • Stage 4: technical optimization + review
   • Stage 5: MoA optimization + review
   • Stage 6: human-AI collaboration integration + academic norm check
   • Stage 7: optional slide outline or Quarto/reveal.js source
6. Maintain literature.csv throughout. Every cited or candidate paper should have a row with title, source, stage, usage, and verification status.
7. Produce {keyword}_final_idea.md using assets/final_idea_template.md.

Literature Thresholds

Use these as evidence-quality gates, not as rigid blockers:

Level	Evidence	Action
Ideal	50+ relevant papers or records	Deep analysis
Standard	30+ relevant papers or records	Normal workflow
Minimum	15+ relevant papers or records	Proceed with limitation note
Below minimum	<15 records	Ask to broaden terms or stop before final claims

When the user supplies a curated paper set, use it even if it is smaller, but label the scope.

Review Rules

• Do not turn a keyword directly into a polished proposal without stage evidence.
• Separate facts, hypotheses, methods, mechanisms, and final synthesis.
• Assign stable hypothesis IDs H1 to H5.
• Assign review problem IDs such as S4-P1, S5-P1, and S6-P1.
• For every strong claim in the final idea, point to a paper row, user-provided evidence, or an explicit limitation.
• Do not invent DOI, journal rank, impact factor, or full-text findings.
• Use arXiv records as 已核验 only when title, authors/year, arXiv ID, URL, and abstract are present and relevant. Otherwise mark 待核验.
• If arXiv returns too few or weakly related records, broaden/refine the query before final claims and label the limitation.

Output Contract

Report:

• output directory
• stages completed
• literature search route and status
• evidence count and threshold level
• top hypotheses
• final idea path
• limitations and next search/refinement step

For quick requests, stop at Stage 3 and say which later stages were skipped.