Academic Research Skills

Academic Research Skills for Claude Code

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A comprehensive suite of Claude Code skills for academic research, covering the full pipeline from research to publication.

Install in 30 seconds (Claude Code CLI / VS Code / JetBrains, v3.7.0+):

/plugin marketplace add Imbad0202/academic-research-skills
/plugin install academic-research-skills

Then try /ars-plan to walk through your paper structure via Socratic dialogue, or jump to Quick install for prerequisites and the traditional symlink flow.

On claude.ai or Claude Desktop (paid plans), add the same repository under Customize → Plugins → Add → Add marketplace → Add from a repository, then install academic-research-skills. Hooks and plugin agents do not run in plain chat there; see SETUP Method 0.

AI is your copilot, not the pilot. It can draft text, including a whole paper in full mode, but the decisions stay yours, and the pipeline stops for your confirmation at every stage. It handles the grunt work (hunting down references, formatting citations, verifying data, checking logical consistency) so you can focus on the parts that actually require your brain: defining the question, choosing the method, interpreting what the data means, and deciding what comes after "I argue that." You remain the author, and you answer for every claim you submit.

Unlike a humanizer, this tool doesn't help you hide the fact that you used AI. It helps you write better. Style Calibration learns your voice from past work. Writing Quality Check catches the patterns that make prose feel machine-generated. The goal is quality, not cheating.

Why human-in-the-loop, not full automation?

Lu et al. (2026, Nature 651:914-919) built The AI Scientist — the first fully autonomous AI research system to publish a paper through blind peer review at a top-tier ML venue (ICLR 2025 workshop, score 6.33/10 vs workshop average 4.87). Their Limitations section enumerates the failure modes that any fully-autonomous AI research pipeline inherits: implementation bugs, hallucinated results, shortcut reliance, bug-as-insight reframing, methodology fabrication, frame-lock, citation hallucinations.

ARS is built on the premise that a human researcher augmented by AI avoids these failure modes better than either alone. Stage 2.5 and Stage 4.5 integrity gates 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 FNR/FPR against a user-supplied gold set.

Zhao et al. (2026-05) audited 111M references across 2.5M papers on arXiv, bioRxiv, SSRN, and PMC. Their conservative estimate is 146,932 hallucinated citations for 2025 alone, with an observed mid-2024 inflection; for the bioRxiv-to-PMC pairing they report 85.3% preprint-to-published persistence. The paper describes "real citations deployed to support claims the cited references do not actually make" as an open challenge. ARS v3.7.1 added trust-chain frontmatter for source provenance; v3.7.3 added locator infrastructure (three-layer citation anchors) for future claim-level audits and surfaces advisory risk signals at cite time (ARS labels the claim-faithfulness gap internally as "L3"; this is ARS terminology, not the paper's). v3.7.x is motivated by Zhao et al.'s corpus-scale findings; corpus-scale evaluation of ARS itself remains future work.

v3.8 closes the second half of the L3 gap. v3.7.3 made every citation carry a locator anchor; v3.8 adds an opt-in audit pass (ARS_CLAIM_AUDIT=1) that fetches the cited source against each anchor and judges whether the claim is actually supported. Five new HIGH-WARN classes (claim-not-supported, negative-constraint-violation, fabricated-reference, anchorless, constraint-violation-uncited) gate-refuse output through the formatter terminal hard gate. A calibration runner ships with a 25-tuple synthetic gold set and FNR<0.15 + FPR<0.10 acceptance thresholds. Its shipped test drives the runner with a stub judge that returns the gold labels, so it checks the tooling, not a live judge; no live-judge calibration result is recorded yet, and the ramp-on plan waits for one (v3.8 spec §5).

Ren et al. (2026, Self-Improvements in Modern Agentic Systems: A Survey) supplies a third, survey-level anchor. Its scientific-discovery synthesis (§7.4) concludes that discovery agents cannot easily verify novelty, correctness, or reproducibility on their own and may exploit weak proxies instead, must manage evidence across heterogeneous tools and literature, and raise governance issues — "scientific writing can also amplify misinformation when the evidence is weak." Its generation-loop chapters (§5.1–§5.2) list human auditing and retained human anchors among the practical safeguards for self-generated evaluation loops, and its historical chapter (§2.2) records the oldest form of the same lesson: the practical success of Lenat's EURISKO depended heavily on the user serving as the external evaluation signal, pruning unproductive heuristic drift — a limitation the survey notes persists in modern agentic systems. ARS cites the survey as design rationale for its human-in-the-loop stance, not as empirical proof that human-in-the-loop pipelines outperform autonomous ones; the survey's actionable deltas for ARS are tracked in #539–#541 and #547–#550.

Gartenberg et al. (2026, Organization Science 37(3):795-812, "More versus better") supplies a fourth anchor, and the first from the journal side. The Organization Science AI Task Force scored every first submission (6,957) and every text-format review (10,389) the journal received between January 2021 and February 2026 with a commercial AI-writing classifier and standard readability indices. Manuscripts scored as heavily AI-written read worse on those indices and were desk-rejected more often; reviews scored as more AI-written leaned toward theory and away from data; and the editors conclude that current AI tools, amplified by publish-or-perish incentives, "appear to be pushing the system toward an equilibrium of more rather than better research." Their §5 contrasts "cognitive surrender" (Shaw & Nave, 2026, as cited there) with human-first use and asks authors to disclose how a manuscript was produced. The evidence is observational, aggregate, and from one journal, and the classifier is a proprietary instrument. ARS cites the editorial as design rationale for recording volume as a non-goal (see POSITIONING.md) and for the Collaboration Depth Observer and the claim-strength ladder, not as evidence about ARS output; the actionable deltas are tracked in #829–#833.

Wang, Li et al. (2026-09, The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing, a survey of 230 sources) supplies a fifth anchor, and the first that treats research production and peer review as one coupled system. Its evaluative-authority ladder (§4.1) runs from author-facing feedback through reviewer assistance and official AI reviews to scoring and decision support, with the survey's point that capability at one rung does not justify use at the next; ARS's simulated panel sits on the lowest rung by design (see POSITIONING.md). Two of its findings shape the reviewer roadmap. First, as the survey summarizes Dycke & Gurevych (2026, §4.5), 391 edits that break a paper's scientific support relations produced no statistically significant difference in the tested automated reviewers' aspects, sentiment, or scores compared with 540 soundness-neutral controls, while presentation-only rewrites with the science held fixed moved AI-review scores (§5.2); the survey's §9.2 conclusion is that a static evaluation can overstate an AI reviewer's reliability once authors can observe and adapt to it. ARS tracks the corresponding Round-1 paired controls in #871 and the author-identity-cue controls (§7.2) in #872; both are measurements, not new mechanisms. Second, its §9.1 cites Brodeur et al. (2026, PNAS 123(22):e2524747123), a randomized study in which 288 researchers in 103 teams reproduced published quantitative social-science results under three conditions: human-only, AI-assisted (ChatGPT as a collaborative tool), and AI-led (ChatGPT with minimal human oversight). Human-only and AI-assisted teams reproduced 94% and 91%, AI-led teams 37%, and the AI-assisted teams detected fewer major coding errors than the human-only teams. In that study, then, AI assistance did not verify better than humans alone and AI-led verification did much worse; ARS reads this as a reason to keep verification human-led at every checkpoint, not as evidence that its own checkpoints or integrity gates are effective. The survey is a synthesis rather than an experiment, its structured search stops at 2026-07-01 and the later targeted update did not rerun every query (§10), its deployment evidence is concentrated in a small number of AI/CS conferences, OpenReview-based settings, and selected journals, and its "arms race" framing is a lens, not a finding; ARS cites it as design rationale, not as evidence about ARS output.

King & Worthington (2026-10, AI Article Reviews Dominate Human Peer Reviews at Their Best: Evidence and Policy Choices) supplies a sixth anchor, and the first that compares AI and human reviews of the same manuscripts head to head. On 124 manuscripts and 134 paid expert reviews from a pre-submission review service whose authors rated those reviews highly, Claude Opus 4.8 wrote one review per manuscript in each of two conditions, at maximum reasoning effort. A GPT-5.5 judge preferred the AI review in every decided pair, each pair judged in both presentation orders, and three former journal editors preferred it in 26 of 30 blinded judgements. In a replication that used both model families as reviewers and judges, the AI review was preferred in 404 of 416 comparisons (97.1%) by the Opus judge and in 392 of 416 (94.2%) by the GPT judge. Two results bear on ARS's reviewer. In the additional-sources area (alternative data, theory, or literature), the AI review won 62% of decided pairs without web retrieval (n=85) and 100% with it (n=99), and ARS's domain reviewer recommends literature without retrieving or verifying it (#1001). The AI reviews were also not error-free: in the no-tools condition, 5.5% of AI claim assessments were judged contradicted by the manuscript, against 15.9% for the human reviews, and 37.1% of AI reviews had at least one such claim; ARS does not check its panel's claims against the manuscript (#1002). The paper's policy section describes authors iterating with a published AI review prompt and answering it in a letter, close to the use ARS is designed for, and warns that authors who revise for a published prompt may end up optimizing for it, leaving biases unrelated to the quality of the science. Its limits frame how ARS reads it: one review service, one judge model in the main analysis, editors who judged from the reviews alone, no measure of whether revised papers improved, and results that, in the authors' words, "do not speak to" how editors should make publication decisions. That last boundary matches where POSITIONING.md places ARS's panel. This paper and the Dycke & Gurevych result above address different questions: review preference and factual grounding here, sensitivity to changes in a manuscript's soundness there, so neither result answers the other. ARS cites the paper as design rationale, not as evidence about ARS output.

v3.3 was inspired by PaperOrchestra (Song, Song, Pfister & Yoon, 2026, Google): Semantic Scholar API verification, anti-leakage protocol, VLM figure verification, and revision-trajectory tracking. ARS now implements that last idea through categorical, evidence-anchored criterion trajectories rather than score deltas.

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Architecture & pipeline

👉 docs/ARCHITECTURE.md — the full pipeline view: flow diagram, stage-by-stage matrix, data-access flow, skill dependency graph, quality gates, and mode list.

The architecture doc supersedes the sprawling pipeline description that used to live here. Everything about what runs in which stage now lives in one place.

Quick install

Prerequisites

• Claude Code (latest; plugin packaging requires recent versions)
• ANTHROPIC_API_KEY exported, or set on first claude run
• Optional: Pandoc for DOCX, tectonic + Source Han Serif TC for APA 7.0 PDF (Markdown output works without either)
• Optional (real Python): needed only for the write-scope guard and a few opt-in commands; the core skills are prompt-driven. Details, including the Windows notes on Git Bash and the Microsoft Store Python stub, are in docs/SETUP.md § Python (optional).

Which controls are active in your install channel? Availability varies by install channel. See the per-channel map: docs/CONTROL_AVAILABILITY.md.

Plugin install (v3.7.0+, recommended):

/plugin marketplace add Imbad0202/academic-research-skills
/plugin install academic-research-skills

Verify it works: run /ars-plan and describe a paper you're working on — ARS will start a Socratic dialogue to map out chapter structure. For a single-shot test instead, try /ars-lit-review "your topic".

👉 docs/SETUP.md — full guide: install Claude Code, set up API keys, optional Pandoc/tectonic for DOCX/PDF, cross-model verification (ARS_CROSS_MODEL), and six installation methods (Plugin for Claude Code / claude.ai / Claude Desktop, project skills, global skills, claude.ai Project, repo-cloned, Claude Science import).

👉 docs/DATA_FLOWS.md — what leaves your machine (bibliographic resolvers, optional consent-gated cross-model calls, the plugin update check), what is cached locally, for how long, and how to turn each path off.

👉 docs/RISK_REGISTER.md — the standing risks the suite knows about, which existing controls address each one, the evidence status behind those controls, and what remains open.

Using Claude Science? The five skills import directly: Skills → Import from GitHub, paste https://github.com/Imbad0202/academic-research-skills, Preview, then Import (requires v3.14.0+ of this repo — the importer reads the explicit skill paths in the marketplace manifest). Imports are point-in-time snapshots: re-import after ARS updates. Imported skills carry the ARS methodology (research / writing / review protocols); Claude Code-specific machinery — slash commands, hooks, subagent orchestration — does not transfer. See docs/SETUP.md Method 5 for details.

Using Pi? Install the in-tree, community-maintained wrapper with pi install git:github.com/Imbad0202/academic-research-skills. It keeps the original ARS content authoritative and documents Pi-specific orchestration and hook limitations. See pi/README.md.

Using Codex CLI? Install the sibling distribution instead: Imbad0202/academic-research-skills-codex — same workflow content, Codex-native packaging as a single $academic-research-suite skill with ars-* aliases.

Third-party platforms and integrations that wrap or host ARS are listed in THIRD_PARTY.md — community-submitted and not reviewed or endorsed by the maintainer.

Governance: who decides, what cross-model review does and does not provide, and the project's end-of-life posture are stated in GOVERNANCE.md; security reporting and triage in SECURITY.md.

Performance & cost

👉 docs/PERFORMANCE.md — per-mode token budgets, full-pipeline estimate (about US$3–7 for a 15k-word paper at 2026-09 list prices, before cache discounts), and recommended Claude Code settings (Auto mode; Agent Team optional).

Guides & articles

• Academic Writing Shouldn't Be a Solo Act — full pipeline walkthrough (English)
• 學術寫作不該是一個人的事：一套開源 AI 協作工具如何改變研究者的工作流 — 完整使用指南（繁體中文）

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Features at a glance

• Deep Research — 13-agent research team with Socratic guided mode, PRISMA systematic review, intent detection, dialogue health monitoring, optional cross-model DA, Semantic Scholar API verification.
• Academic Paper — 12-agent paper writing with Style Calibration, Writing Quality Check, LaTeX hardening, visualization, revision coaching, citation conversion, anti-leakage protocol, and VLM figure verification.
• Academic Paper Reviewer — 7-agent multi-perspective peer review with criterion-bound, evidence-anchored narrative judgements (Journal-Fit Reviewer + 3 dynamic reviewers + Devil's Advocate), concession threshold protocol, attack intensity preservation, optional cross-model DA critique / calibration, R&R traceability matrix, read-only constraint. Current live reviews remain NOT_CALIBRATED; full calibration produces a bounded candidate profile, while live-profile application is not yet wired.
• Academic Pipeline — 10-stage pipeline orchestrator with adaptive checkpoints, claim verification, Material Passport, optional repro_lock, optional cross-model integrity verification, mid-conversation reinforcement, and narrative criterion-by-criterion regression checks (the typed trajectory carrier is deferred).
• SR-Screener — protocol-driven study screening for systematic, scoping and rapid reviews: two blinded AI reviewers plus a third-reviewer adjudicator, ordered exclusion codes, no silent defaults, resumable batch runs, QC (seed studies, near-miss rechecks, kappa and PABAK), PRISMA 2020 counts, EndNote/Zotero RIS groups, and a literature_corpus[] handoff to academic-paper. AI decisions are decision support; the review team verifies them.
• Data Access Level Metadata (v3.3.2+) — every skill declares data_access_level (raw / redacted / verified_only); enforced by scripts/check_data_access_level.py. Pattern adapted from Anthropic's automated-w2s-researcher (2026). See shared/ground_truth_isolation_pattern.md.
• Task Type Annotation (v3.3.2+) — every skill declares task_type (open-ended or outcome-gradable). All current ARS skills are open-ended.
• Benchmark Report Schema (v3.3.5+) — JSON Schema + lint for honest benchmark comparisons. See shared/benchmark_report_pattern.md.
• Artifact Reproducibility Lockfile (v3.3.5+) — optional repro_lock sub-block on Material Passport. Configuration documentation, not replay guarantee — LLM outputs are not byte-reproducible. See shared/artifact_reproducibility_pattern.md.
• Model Tiering (#517, v3.16+) — optional ARS_MODEL_TIERING switch with two directions: economy (execution-type agents dispatch one tier below the session model, floor Opus-class) and quality-boost (judgment-type agents at integrity gates and final review step up to the frontier tier). Default unset = byte-equivalent to pre-#517 behavior. See shared/model_tiering.md.
• Canonical Cross-Model Handoff Envelope (#527, v3.17+) — the owner→dispatcher→owner blind-checkpoint transport path (#523) now has a machine-stable [CROSS-MODEL-HANDOFF v1] envelope with a normative Python grammar (scripts/cross_model_handoff.py) instead of prose-only enforcement, pinning agreement/divergence/malformed-result routing across all three checkpoint owners. See shared/cross_model_verification.md §"Cross-model handoff envelope".
• Experiment Provenance Intake (#260) — optional experiment_provenance[] on the Material Passport records experiments the scholar ran externally (ARS never runs experiments), and manuscript claims join to them via claim_intent_manifest.planned_experiment_ids[]. The integrity gate (Stage 2.5/4.5) audits each experiment-backed claim against declared provenance — ALIGNED / OVERSTATED / NOT_SUPPORTED_BY_PROVENANCE / PROVENANCE_INSUFFICIENT — without judging whether the experiment itself was correct. A fail-closed experiment_intake_declaration makes "did you run experiments?" an explicit decision the scholar answers after Stage 1, or on entry when a run starts later (even literature-only runs declare no_experiments_declared). See shared/handoff_schemas.md §"Experiment Provenance Intake (#260)".

Integrity and verification boundary: ARS checks the manuscript and the reported process—including citation existence, claim–source alignment, reported methodology, declared experiment–result alignment, figure/table fidelity, and reporting/process/package conformance. Some checks are sampled or LLM-mediated. ARS does not establish that procedures were actually performed, raw data are authentic, or results reproduce; a consistently reported fabrication can pass these checks. See POSITIONING.md § Integrity checks and the empirical-work boundary.

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Showcase: real pipeline output

See the complete artifacts from a real pipeline run, including peer review reports, integrity verification reports, and the final paper:

A March 2026 record, not current performance. This run (2026-03-07 to 03-08) used academic-pipeline v2.3, before ARS v3.3 added the Semantic Scholar check and v3.11 added the deterministic four-index citation gate. Its numbers describe that version; the current gates have not been measured on this paper. The byline names Claude (Anthropic) as author because the researcher asked for that during the experiment. The paper is not an Anthropic publication, and ARS's positioning is that the tool does not replace the researcher and does not claim authorship (POSITIONING.md).

Browse all pipeline artifacts →

Artifact	Description
Final Paper (EN)	APA 7.0 formatted, LaTeX-compiled
Final Paper (ZH)	Chinese version, APA 7.0
Integrity Report — Pre-Review	Stage 2.5: flagged 15 problem references (8 with bibliographic errors, 6–8 likely fabricated) + 3 statistical errors
Integrity Report — Final	Stage 4.5: zero regressions confirmed
Peer Review Round 1	Journal-Fit Reviewer + 3 Reviewers + Devil's Advocate
Re-Review	Verification after revisions
Peer Review Round 2	Follow-up review
Response to Reviewers	Point-by-point author response
Post-Publication Audit Report	Full-reference audit run separately with Claude Code + WebSearch: 21 of the 68 final references still had problems after 3 rounds of integrity checks

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Companion: Experiment Agent

If your research involves running experiments (code or human studies) before writing, the Experiment Agent skill fills the gap between ARS Stage 1 (RESEARCH) and Stage 2 (WRITE).

ARS Stage 1 RESEARCH  →  RQ Brief + Methodology Blueprint
        ↓
  experiment-agent     →  run/manage experiments → validate results
        ↓
ARS Stage 2 WRITE     →  write paper with verified experiment results

What it does: executes code experiments (Python, R, etc.) with real-time monitoring, manages human study protocols with IRB ethics checklist, interprets statistics with 11-type fallacy detection, and verifies reproducibility.

How to use together: pause the ARS pipeline after Stage 1, run experiments in a separate experiment-agent session, then bring the results (with Material Passport) back to ARS Stage 2. ARS requires zero modification. See the experiment-agent README for setup instructions.

Stage 1 intake declaration (#260, #925): at the checkpoint after Stage 1, or on entry when a run starts after Stage 1, ARS asks you whether the paper reports experiments or data analyses you ran yourself, and records your answer, in your words, in a fail-closed experiment_intake_declaration on the Material Passport. ARS does not infer the answer from the manuscript. If you ran experiments externally, the scholar enters one experiment_provenance[] entry per experiment (experiment_id, nested repro_lock, planned_vs_executed[], negative_results[], known_limitations[]) and the declaration is set to experiments_declared; if not, it is set to no_experiments_declared. The declaration is required on every post-#260 passport — a run that touches no experiments still declares no_experiments_declared, so the integrity gate can never be silently bypassed by a forgotten provenance block. The experiment_ids are frozen at this intake point, which closes before the first Stage 2 writer is dispatched (or before the first integrity gate when no Stage 2 lies ahead), so you can pause after Stage 1, run the experiments, and record them when you return; the writers later reference them via planned_experiment_ids[].

Teaching-side companion: Teaching Skills applies the ARS architecture (skill ensembles, shared contracts, staged gates, a Course Passport) to the teaching side of academic life — course design → lessons → assessment → delivery → reflection; its sotl mode hands classroom-inquiry projects off to ARS deep-research / academic-paper for the publication phase.

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Usage

Quick Start

# Start a full research pipeline
You: "I want to write a research paper on AI's impact on higher education QA"

# Start with Socratic guidance
You: "Guide my research on AI in educational evaluation"

# Write a paper with guided planning
You: "Guide me through writing a paper on demographic decline"

# Review an existing paper
You: "Review this paper" (then provide the paper)

# Check pipeline status
You: "status"

Individual Skills

Deep Research (8 modes)

"Research the impact of AI on higher education"       → full mode
"Give me a quick brief on X"                          → quick mode
"Do a systematic review on X with PRISMA"             → systematic-review mode
"Guide my research on X"                              → socratic mode (guided)
"Fact-check these claims"                             → fact-check mode
"Do a literature review on X"                         → lit-review mode
"Compare these papers in WHY/HOW/WHAT format"         → three-way-scan mode
"Review this paper's research quality"                → review mode

Academic Paper (11 modes)

"Write a paper on X"                                  → full mode
"Guide me through writing a paper"                    → plan mode (guided)
"Build a paper outline"                               → outline-only mode
"I have a draft, here are reviewer comments"          → revision mode
"Parse these reviewer comments into a roadmap"        → revision-coach mode
"Write an abstract for this paper"                    → abstract-only mode
"Turn this into a literature review paper"            → lit-review mode
"Convert to LaTeX" / "Convert citations to IEEE"      → format-convert mode
"Check citations"                                     → citation-check mode
"Generate an AI disclosure statement for NeurIPS"     → disclosure mode
"Audit my rebuttal draft against the reviews"         → rebuttal-audit mode

Academic Paper Reviewer (6 modes)

"Review this paper"                                   → full mode (Journal-Fit Reviewer + R1/R2/R3 + Devil's Advocate)
"Quick assessment of this paper"                      → quick mode
"Guide me to improve this paper"                      → guided mode
"Check the methodology"                               → methodology-focus mode
"Verify the revisions"                                → re-review mode
"Calibrate this reviewer against my gold set"         → calibration mode

Academic Pipeline (Orchestrator)

"I want to write a complete research paper"           → full pipeline from Stage 1
"I already have a paper, review it"                   → mid-entry at Stage 2.5 (integrity first)
"I received reviewer comments"                        → mid-entry at Stage 4

Pipeline ends with Stage 6: Process Summary — auto-generates a paper creation process record with 6-dimension Collaboration Quality Evaluation (1–100 scoring).

SR-Screener (8 modes)

"Turn my proposal into a screening protocol"          → protocol mode
"Is this abstract eligible for my review?"            → quick mode (single-reviewer triage)
"Pilot the screening with my seed studies"            → pilot mode
"Screen these database exports"                       → ta-screen mode
"Screen the full texts of the advanced records"       → ft-screen mode
"Adjudicate the conflicts in my Rayyan export"        → adjudicate mode
"Double-check my exclusions"                          → audit mode
"Give me the PRISMA numbers for the screening"        → report mode

Supported Languages

• Traditional Chinese (繁體中文) — default when user writes in Chinese
• English — default when user writes in English
• Bilingual abstracts (Chinese + English) for academic papers

Using a different language? Socratic mode (deep-research) and Plan mode (academic-paper) use intent-based activation — they detect the meaning of your request, not specific keywords. This means they work in any language without modification.

However, the general Trigger Keywords section (which determines whether the skill is activated at all) still lists English and Traditional Chinese keywords. If you find the skill isn't activating reliably in your language, you can add your language's keywords to the ### Trigger Keywords section in each SKILL.md file to improve matching confidence.

Supported Citation Formats

• APA 7.0 (default, including Chinese citation rules)
• Chicago (Notes & Author-Date)
• MLA
• IEEE
• Vancouver

Supported Paper Structures

• IMRaD (empirical research)
• Thematic Literature Review
• Theoretical Analysis
• Case Study
• Policy Brief
• Conference Paper

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Skill Details

Per-agent responsibilities and per-stage artifacts now live in docs/ARCHITECTURE.md. Version numbers are anchored here so release metadata stays in one place.

Deep Research (v2.12.1)

13-agent research team. Modes: full, quick, review, lit-review, three-way-scan, fact-check, socratic, systematic-review. Full agent roster and artifacts: see ARCHITECTURE.md §3.

Academic Paper (v3.3.1)

12-agent paper writing pipeline. Modes: full, plan, outline-only, revision, revision-coach, abstract-only, lit-review, format-convert, citation-check, disclosure, rebuttal-audit. Output: MD + DOCX (via Pandoc when available) + LaTeX (APA 7.0 apa7 class / IEEE / Chicago) → PDF via tectonic. Full agent roster and per-phase responsibilities: see ARCHITECTURE.md §3.

Academic Paper Reviewer (v1.11.1)

7-agent multi-perspective review with criterion-bound narrative judgements. Modes: full, re-review, quick, methodology-focus, guided, calibration. Current live reviews and Schema 6 packages remain NOT_CALIBRATED; full calibration can produce a bounded candidate profile, but application to a live review is not wired. No numerical total is mapped to Accept, Minor Revision, Major Revision, or Reject. First-round review panel vs. contract-governed re-review dispatch boundary: see ARCHITECTURE.md §3 Stage 3 / Stage 3'.

Academic Pipeline (v3.24.2)

…