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Academic Research Skills (ARS)

external · status: active · focus: end-to-end · discipline: general · started: 2026

Project page: https://github.com/Imbad0202/academic-research-skills

Source: projects/landscape/academic-research-skills.yml

Positioning

A comprehensive Claude Code plugin suite (v3.9.0 at scoring date) for the academic research pipeline: literature → write → review → revise → finalize. Three sub-suites — Deep Research (13 agents), Academic Paper (12 agents), Academic Paper Reviewer (7 agents) — with Style Calibration, anti-leakage protocols, Semantic Scholar API verification, VLM figure verification, and explicit human-in- the-loop gates throughout.

Distinctive contribution

Most-adopted Claude Code-native research plugin (9k+ stars) with an explicit "AI as copilot, not pilot" design stance. v3.8 introduces a claim-faithfulness audit pass (ARS_CLAIM_AUDIT=1) that fetches each cited source against three-layer citation anchors and gate-blocks output on five hallucination classes (claim-not-supported, negative-constraint-violation, fabricated-reference, anchorless, constraint-violation-uncited). This is the most operationally serious anti-hallucination apparatus in the catalog.

Evaluation scores

Dimension Score (0–3) Note
Lifecycle coverage 2 Five stages spanning literature through review; less coverage on empirical analysis / modeling than full-pipeline economics tools.
Autonomy level 1 Explicit copilot stance: 'AI is your copilot, not the pilot.' Human approval at every stage gate.
Architectural transparency 3 Detailed architecture docs (docs/ARCHITECTURE.md), public failure-mode reference, calibration thresholds (FNR<0.15, FPR<0.10) published in spec.
Inputs supported 3 Topic / draft / reviewer-comment inputs; Pandoc + tectonic for DOCX/PDF; multiple installation paths (plugin, project skills, global skills, claude.ai Project).
Outputs / reproducibility 3 Three-layer citation anchors enable claim-level audits; replication packages; versioned releases.
Internal evaluation 3 Calibration mode with gold-set FNR/FPR measurement; cross-model verification (ARS_CROSS_MODEL); engages published failure-mode literature directly.
Openness 2 CC BY-NC 4.0 (non-commercial); sponsor-supported; Codex CLI sibling distribution available.
Maturity / traction 3 9.3k+ stars; v3.9.0 with multi-version release cadence; English + Traditional Chinese docs; multi-IDE support.
Cross-family policy 1 ARS_CROSS_MODEL flag enables cross-family verification; not the default.
Runtime assurance 3 Claim-faithfulness audit pass (ARS_CLAIM_AUDIT) with 5 HIGH-WARN classes + 3-stage citation anchors + cross-model verification = heaviest claim-audit harness in the catalog alongside ARIS.
Cross-platform portability 3 Claude Code + VS Code + JetBrains + Codex CLI sibling distribution + 5 install methods.

Scored on 2026-05-18. See the evaluation rubric.

Tags

Pipeline stages: literature-discovery literature-synthesis paper-drafting revision-editing referee-simulation

Architectural features: multi-agent debate-consensus human-in-loop tool-use rag-knowledge-base artifact-versioning

Inputs: research-topic paper-draft reviewer-comments

Outputs: paper-pdf paper-docx literature-review reviewer-report revision-plan

Data sources: user-provided

Knowledge sources: semantic-scholar arxiv

Limitations

  • Non-commercial license restricts deployment options.
  • Heavy on the writing/review block; lighter on empirical data analysis or formal modeling.
  • Quality of claim-audit gating depends on user-supplied calibration set.
  • Author's substantive scholarly background is not disclosed in repo metadata; project is engineering-led rather than discipline-led.

Also compared in

  • ARIS Table 4 (footnote) (yang2026aris) — Discussed as a parallel claim-audit project.