Why this Skill suite surged to 11,600 stars in one week
In May 2026, academic-research-skills entered the GitHub Trending Top 10 with 20,268 total stars and 11,600 stars added in seven days. The project reflects the community's deep understanding of Claude Code's "Skill pattern": AI is your copilot, not your pilot—it handles mechanical work so you can focus on parts that actually require training.
The repository packages three skills plus a 10-stage pipeline. Reported cost: $4-6 in API spend and 2-4 hours of collaboration to produce a 15,000-word paper.
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Deep Research (v2.9.4) — 13 research agents
| Mode | Use case | Depth |
|------|----------|-------|
| full | Full research run | Balanced |
| quick | 30-minute high-fidelity brief | High-fidelity |
| systematic-review | PRISMA-compliant systematic review | High-fidelity |
| socratic | Guided inquiry dialogue | Originality |
| fact-check | Fact verification | High-fidelity |
| lit-review | Literature review | High-fidelity |
| review | Paper assessment | Balanced |
Core capabilities
- Socratic mode — clarifies vague research ideas
- PRISMA systematic review and meta-analysis support
- Intent detection — identifies what you really want to study
- Conversation-health monitoring — prevents AI drift
- Semantic Scholar API validation — eliminates hallucinated citations
- Optional cross-model adversarial verification
- Style Calibration — feed in 3+ of your past papers so the system learns your writing voice
- Writing Quality Check — flags 25 common AI tells, dash overuse, throat-clearing openings, structural patterns, burstiness
- LaTeX Hardening — APA 7.0
apa7class, IEEE, Chicago - VLM Figure Verification — verifies figures visually
- Anti-Leakage Protocol — prevents information disclosure across sessions
- Read-Only Constraint — reviewer agents cannot modify the manuscript, only suggest changes (mirroring real peer-review boundaries)
- R&R Traceability Matrix — tracks whether each reviewer comment was addressed
- Three cognitive framework files:
- Toulmin argumentation model + Bradford Hill causal reasoning
- Three-lens review (internal validity / external validity / contribution)
- Research gap definition (real gaps vs. rhetorical gaps)
- Architecture: three composable Skills (Deep Research, Academic Paper, Academic Paper Reviewer) connected by a 10-stage pipeline.
- Scale: 13 + 12 + 7 specialized agents totaling 32 distinct AI roles.
- Cost and time: roughly $4-6 in API spend and 2-4 hours of collaboration per 15,000-word paper.
- Quality safeguards: Semantic Scholar citation validation, VLM figure verification, AI-tell writing checks, anti-leakage protocol.
- Editorial fidelity: read-only reviewer constraint, R&R traceability matrix, and a 0-100 rubric with four decision bands mirror real peer review.
- Positioning: AI is framed as a copilot handling mechanical tasks so researchers can focus on parts that require domain training.
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Academic Paper (v3.1.2) — 12 writing agents
| Mode | Use case |
|------|----------|
| full | Full paper |
| plan | Guided drafting |
| outline-only | Outline only |
| revision | Revise per reviewer comments |
| revision-coach | Parse reviewer comments |
| abstract-only | Abstract only |
| lit-review | Literature review paper |
| format-convert | LaTeX / citation format conversion |
| citation-check | Citation audit |
| disclosure | AI disclosure statement (NeurIPS-style) |
Core capabilities
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Academic Paper Reviewer (v1.9.1) — 7 multi-perspective agents
| Role | Responsibility | |------|----------------| | EIC (Editor-in-Chief) | Overall quality gate | | R1 / R2 / R3 | Three dynamic reviewers with distinct emphases | | Devil's Advocate | 8-dimension challenge of the paper | | Quality Rubric | 0-100 scoring: ≥80 Accept, 65-79 Minor, 50-64 Major, <50 Reject |
Core design points
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