- name
- jason-academic-writing
- description
- Complete academic paper writing pipeline with integrity checks and multi-agent review system. Optimized prompts for Methods/Results/Discussion sections. Features self-counterargument framework, bias matrix, and overclaim self-audit. Use when writing research papers, need citation verification, anti-hallucination checks, multi-perspective review, or auditable process records.
- version
- 1.0.1
- requires
- env
Academic Writing Pipeline
End-to-end academic paper production with built-in quality gates and multi-agent review.
Pipeline Overview
Research → Write → Integrity Check → Review → Revise → SummaryEach stage has defined inputs/outputs and quality gates. The pipeline is non-linear: stages may loop (Review → Revise → Re-Review) until quality threshold met.
Stage Details
Stage 1: Research
Goal: Gather and organize evidence.
Actions:
- Literature search via Semantic Scholar API
- Filter by relevance score ≥ 0.5
- Grade evidence level (A: meta-analysis, B: RCT, C: observational, D: opinion)
- Output:
research/evidence.json
Script: scripts/research.py
Stage 2: Write
Goal: Generate structured manuscript.
Actions:
- Build argument chain from evidence
- Generate sections: Abstract, Introduction, Methods, Results, Discussion
- Track citation markers for each claim
- Output:
draft/manuscript.md
Script: scripts/write.py
Stage 3: Integrity Check (CRITICAL)
Goal: Anti-hallucination verification.
Check types:
- Citation verification: DOI exists? Authors match? Year correct?
- Data verification: Numbers match tables/figures?
- Claim verification: Evidence supports assertion?
Threshold: Must pass 100% of checks to proceed.
Script: scripts/integrity_check.py
APIs used:
- Semantic Scholar (
https://api.semanticscholar.org) - CrossRef DOI (
https://api.crossref.org/works/)
Stage 4: Review (5-Person Panel)
Agents:
| Role | Focus | Score Weight |
|---|---|---|
| Editor-in-Chief | Contribution, journal fit | 30% |
| Methodology | Methods, stats, reproducibility | 25% |
| Domain Expert | Related work, theory | 20% |
| Devil's Advocate | Strongest counter-arguments | 15% |
| Synthesizer | Merge opinions, roadmap | 10% |
Decision mapping:
- ≥80: Accept
- 65-79: Minor Revision
- 50-64: Major Revision
- <50: Reject
Script: scripts/review.py
Stage 5: Revise
Goal: Address reviewer feedback.
Actions:
- Parse Synthesizer roadmap
- Generate revision plan with priorities
- Rewrite affected sections
- Re-run Integrity Check
Script: scripts/revise.py
Stage 6: Process Summary
Goal: Auditable record.
Output: summary.json containing:
- Timeline of each stage
- Decision points and scores
- Integrity check results
- Reviewer scores and comments
- Revision history
Script: scripts/summary.py
Configuration
Edit config.yaml for:
- Model selection (default:
qwen3.5-plus) - Temperature (default: 0.3 for stability)
- Review thresholds
- API keys
Usage
# Full pipeline
python scripts/main.py --topic "your research topic"
# Single stage
python scripts/main.py --stage integrity-check --input draft/manuscript.md
# With custom config
python scripts/main.py --config custom_config.yamlQuality Gates
| Gate | Requirement | Action on Fail |
|---|---|---|
| Evidence | ≥5 Grade A/B sources | Return to Research |
| Integrity | 100% verification | Return to Write |
| Review | ≥65 score | Loop Revise |
| Final Integrity | 100% verification | Block submission |
Key Principles
- Integrity First: Citation verification is non-negotiable
- Quantified Review: Scores enable objective decisions
- Loopable Pipeline: Revision cycles until threshold met
- Auditable Output: Process Summary for journal submission
Reference Files
references/review_rubric.md- Detailed scoring criteriareferences/evidence_levels.md- Evidence grading standardsreferences/citation_styles.md- Journal formatting guides