Academic Research Skills for Claude Code: A Human-in-the-Loop Pipeline for Paper Writing

Academic Research Skills (ARS) for Claude Code is a plugin that supports the full research-to-publication pipeline: research → write → review → revise → finalize. It is designed as a human-in-the-loop system, explicitly rejecting full automation. The tool handles grunt work — reference hunting, citation formatting, data verification, logical consistency checks — while the researcher retains control over question definition, method selection, interpretation, and the core argument.
Quick Install (Claude Code v3.7.0+)
/plugin marketplace add Imbad0202/academic-research-skills
/plugin install academic-research-skillsVerify with /ars-plan to start a Socratic dialogue mapping out chapter structure, or /ars-lit-review "your topic" for a single-shot literature review.
Why Human-in-the-Loop?
ARS cites Lu et al. (2026, Nature 651:914-919) who built The AI Scientist — the first fully autonomous AI research system to publish a paper through blind peer review at a top ML venue (ICLR 2025 workshop, score 6.33/10 vs workshop average 4.87). Their Limitations section lists failure modes: implementation bugs, hallucinated results, shortcut reliance, bug-as-insight reframing, methodology fabrication, frame-lock, and citation hallucinations. ARS is built on the premise that a human researcher augmented by AI avoids these failure modes better than either alone.
Integrity Gates & Calibration
Stage 2.5 and Stage 4.5 run a 7-mode blocking checklist (see academic-pipeline/references/ai_research_failure_modes.md). The reviewer offers an opt-in calibration mode that measures its own false negative rate / false positive rate against a user-supplied gold set.
Features
- Style Calibration learns your voice from past work.
- Writing Quality Check catches patterns that make prose feel machine-generated.
- Semantic Scholar API verification (inspired by PaperOrchestra, Song, Song, Pfister & Yoon, 2026, Google).
- Anti-leakage protocol, VLM figure verification, and score trajectory tracking.
Architecture
Full pipeline documentation is in docs/ARCHITECTURE.md, including flow diagram, stage-by-stage matrix, data-access flow, skill dependency graph, quality gates, and mode list. For DOCX output, pandoc is required; for APA 7.0 PDF, tectonic + Source Han Serif TC font (Markdown output works without either).
📖 Read the full source: HN AI Agents
👀 See Also

Claude for Creative Work: MCP Connectors for Blender, Adobe, Ableton, and More
Anthropic released a set of MCP connectors allowing Claude to interface with creative tools including Blender, Autodesk Fusion, Adobe Creative Cloud, Ableton Live, and Splice, enabling natural-language control, scripting, and pipeline automation.

Skir: A Modern Alternative to Protocol Buffers for Type-Safe Data Exchange
Skir is a declarative language for defining data types, constants, and APIs that generates idiomatic, type-safe code in TypeScript, Python, Java, C++, Kotlin, and Dart from a single .skir file. It includes built-in schema evolution safety, RPC support similar to gRPC, and serialization to JSON or binary formats.

Synapse: Real-Time Dashboard for Visualizing Claude Code Agent Sessions
Synapse is a real-time dashboard that visualizes Claude Code agent sessions as interactive node graphs, showing agent spawns, tool calls, and subagents. It requires Node.js and Claude, installs via npm, and offers multiple analysis views and remote approval features.

Open Source Curated Collection of OpenClaw Resources Unveiled
Discover a new open-source collection of OpenClaw resources, curated by the community to enhance AI development and collaboration.