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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.

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.

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.

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.

👉 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.

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, 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.

👉 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).

• 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）

• 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.

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 →

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.

# 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"

"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

"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

"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

"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).

"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

• 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.

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

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

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.

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.

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.

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'.

10-stage orchestrator with integrity verification, two-stage review, Socratic coaching, and collaboration evaluation. Pipeline rules (protocol the agents follow, not runtime guarantees): every stage requires user confirmation checkpoint; integrity verification (Stage 2.5 + 4.5) is MANDATORY with no unrecorded bypass (every override requires user reasoning recorded for Stage 6); R&R Traceability Matrix (Schema 11) maps each reviewer concern to the author's revision claim and records whether the re-review verified it. v3.4 added the Compliance Agent (PRISMA-trAIce + RAISE) at Stage 2.5 / 4.5. v3.5 adds the Collaboration Depth Observer (collaboration_depth_agent, advisory only — never blocks) at every FULL/SLIM checkpoint and at pipeline completion. MANDATORY integrity gates (2.5 / 4.5) explicitly skip the observer so compliance checks are not diluted. Based on Wang & Zhang (2026), IJETHE 23:11. Stage-by-stage matrix with agents, artifacts, and gates: see ARCHITECTURE.md §3.

4-agent study screening between deep-research (question, protocol, search) and academic-paper (writing the review). Modes: protocol, quick, pilot, ta-screen, ft-screen, adjudicate, audit, report. Two blinded reviewer subagents (Read and Grep only) screen every record against the user-confirmed protocol, a third reviewer adjudicates advance-vs-exclude conflicts, and standard-library Python scripts parse RIS / PubMed .nbib / Web of Science / CSV exports, de-duplicate, batch, merge, and build the screening log, RIS groups, PRISMA 2020 counts, a methods draft with [TO COMPLETE] slots, and a literature_corpus[] file. Rules the agents follow (not runtime guarantees): no record is screened before the user confirms the protocol, no failed call becomes a default "exclude", and the review team verifies the decisions before the numbers are reported. See sr-screener/SKILL.md.

While using ARS to write a reflection article about AI in higher education, I ran into three structural problems that no amount of prompt engineering could fix:

1. Frame-lock: I asked the AI to run a devil's advocate debate against its own thesis. It did — four rounds, each more refined than the last. But every round stayed inside the frame I'd set. The DA attacked arguments, never premises. It never asked "are we even discussing the right question?" This is the same pattern that caused the 31% citation error rate in v2.7's stress test: the verifying AI and the generating AI share the same cognitive frame.

2. Sycophancy under pushback: Every time I challenged the DA's attacks, it conceded too quickly. It retracted findings faster than it launched them. The model's training rewards conversational harmony — so "the user pushed back" was treated as evidence that the attack was wrong, when often it just meant the user was persistent.

3. Intent misdetection: The Socratic Mentor kept trying to converge and produce deliverables ("Want me to write this up?") when I was still exploring. It couldn't distinguish "the user wants a deep philosophical discussion" from "the user wants an RQ brief." Both look like engagement, but they need opposite AI behaviors.

Devil's Advocate — Concession Threshold Protocol (deep-research + academic-paper-reviewer)

• DA must now score every rebuttal on a 1-5 scale before responding
• Concession only allowed at score ≥4 (rebuttal directly addresses core attack with evidence)
• Score ≤3: hold position and restate the original attack
• Anti-sycophancy rules: no consecutive concessions, concession rate tracking, frame-lock detection after each checkpoint

Socratic Mentor — Intent Detection Layer (deep-research)

• Classifies user intent as exploratory vs. goal-oriented at dialogue start and every 3 turns
• Exploratory mode: disables auto-convergence, raises max rounds to 60, prohibits "want me to summarize?" prompts
• Goal-oriented mode: standard convergence behavior
• Anti-premature-closure rules: in exploratory mode, the user decides when to stop

Socratic Mentor — Dialogue Health Indicator (deep-research)

• Silent self-assessment every 5 turns on three dimensions: persistent agreement, conflict avoidance, premature convergence
• Auto-injects challenging questions when agreement pattern detected
• Invisible to user (to prevent gaming), but log available for post-session review

These optimizations don't solve AI's structural limits — they make the limits visible and manageable. The DA will still eventually concede if pushed hard enough. The Socratic Mentor will still have some convergence bias. But now there are explicit checkpoints that slow down the sycophancy, force the DA to justify concessions, and prevent the Mentor from wrapping up before the user is ready.

The deeper lesson: AI literacy isn't about learning to use AI as a tool, following ethics rules, or fearing AI risks. It's about engaging AI deeply enough to discover its structural limits yourself — and your own thinking limits in the process.

This work is licensed under CC-BY-NC 4.0.

You are free to:

• Share — copy and redistribute the material
• Adapt — remix, transform, and build upon the material

Under the following terms:

• Attribution — You must give appropriate credit
• NonCommercial — You may not use the material for commercial purposes

Attribution format:

Based on Academic Research Skills by Cheng-I Wu
https://github.com/Imbad0202/academic-research-skills

Cheng-I Wu (吳政宜) — Author and maintainer

aspi6246 — Contributor. The v3.1 optimization was inspired by patterns from Claude-Code-Skills-for-Academics: read-only constraint pattern, anti-pattern codification as first-class design, cognitive framework approach (teaching "how to think" not just procedures), and lean skill size philosophy.

mchesbro1 — Contributor. Originally proposed and drafted the IS Basket of 8 journals for academic-paper-reviewer/references/top_journals_by_field.md (Issue #5).

cloudenochcsis — Contributor. Extended the IS section from the Basket of 8 to the full Senior Scholars' Basket of 11 — adding Decision Support Systems, Information & Management, and Information and Organization (Issue #7, PR #8). Sourced from the AIS Senior Scholars' List of Premier Journals.

eltociear (Ikko Eltociear Ashimine) — Contributor. Translated the Japanese README (README.ja-JP.md) (PR #161).

xpfo-go (xpfo) — Contributor. Translated the Simplified Chinese README (README.zh-CN.md) (PR #181).

devCharlotte — Contributor. Translated the Korean README (README.ko-KR.md) (PR #469).

Yaobin29 — Contributor. Proposed reviewer-response tooling in PR #433; the deep-research three-way-scan mode and the academic-paper rebuttal-audit mode (rescued from the PR's audit concept) were integrated from that contribution in v3.12.1.

ktao732084-arch — Contributor. Expanded the academic-paper disclosure system with nine medical-publishing policy targets, target-specific required-fact intake, and fail-closed standalone rendering (Issue #596, PR #599); expanded the EQUATOR clinical-reporting reference with condensed CARE, STARD and TRIPOD+AI guidance plus a fail-closed study-design routing sequence (Issue #594, PR #601); and designed and contributed the standalone Chinese-literature resolver, API protocol, and synthetic transport-fixture suite (Issue #595, PR #600).

didacrios — Contributor. Translated the Spanish README (README.es-ES.md).

Only the three most recent releases are summarized here. The full release history, including the one-paragraph summaries that used to live on this page, is in CHANGELOG.md.

One new skill and behaviour repairs found by reading a full pipeline run end to end; the prompt-level changes are unmeasured, and sr-screener makes no screening-accuracy claim: v3.23.0 adds sr-screener (#919, contributed by @erfanz97), a fifth skill that turns a review protocol into eligibility rules the user confirms and screens titles/abstracts and full texts with two blinded reviewer subagents and an adjudicator; its tests use synthetic records, and its spreadsheet exports neutralise formula text (#951). A walk-through of a default pipeline run on paper (#925-#929) changed what such a run does: the v3.6.7 Audit Artifact Gate is opt-in, the scholar is asked once whether the paper reports their own experiments, constraints set for a run are kept in the user's words and quoted to each later dispatch they apply to (at review stages they act at the checkpoints), the integrity gates follow one rule set and list paywalled sources as notes, Stages 5 and 6 produce only the files asked for, and opt-in switches surface Stage 5 refusals at Stage 4.5. The Stage 2.5 and 4.5 checkpoints replay evidence rows from a folder the orchestrator names (#933, #947, #948), a source the gate finds fabricated stays out of later revisions (#936), and the ledger readers report parse failures without quoting the file (#898, #945). Smaller changes: per-source method weaknesses in reading outputs (#916), a review-form note at a fixed point (#921), Schema 1 aligned with its producer (#938), and /ars-citation-check inheriting the session model (#912).

Two deterministic checks pinned by synthetic tests; the prompt-level changes are unmeasured: v3.22.2 adds a run ledger (#887). When a pipeline run has a passport file, the orchestrator appends the user's initial instructions, each checkpoint's question and the user's answer in their exact words, step receipts, and file hashes to a local ledger beside the passport. After compaction, resume, or a subagent return, scripts/run_ledger.py report compares the ledger with what the summary or report claims and lists the differences; it now prints that handoff check itself, in English or Traditional Chinese, and hashes the files an entry names when the entry is written (#898). The ledger holds the user's exact words, and docs/DATA_FLOWS.md lists it and how to delete it. The release also adds scripts/check_acronyms.py (#849, proposed by @reiropke), which reports acronyms that are undefined, defined after first use, or defined twice, without calling a model; the prompts have the calling session run it on saved drafts and abstracts, and a review appends its report to the Editorial Decision Letter as an advisory attachment that the decision, the revision roadmap, and re-review criteria do not draw on. Synthetic tests pin both scripts; whether runs write the ledger entries or call the check is unmeasured. The instruction/data boundary now reaches third-party text in dispatches and passport imports (#890), text a receiver reads through its own tool calls, and each skill's main session (#894); a lint pins every copy, and the effect is unmeasured (the opt-in claim-audit judge prompt changes with it, so cached verdicts from the old prompt are not reused). Repairs: the routing core reaches plugin and skills-copy installs (#892); an explicit request stays explicit when the mode's usual input is missing (#889); /ars-lit-review no longer points a run under way to another workflow (#897); the revision coach keeps peer review out of the committee-correspondence variant (#854); the orchestrator scopes "authoritative" skill output to deliverable ownership (#888); and the front page and showcase now match their sources (#908). The routing results come from one session per fixture, a smoke test rather than a rate. One new schema describes the ledger; no existing schema, command model, or effort setting changes.

Model currency and repairs; the new prompt-level guard is unmeasured: v3.22.1 names Claude Opus 5.5 beside Claude Fable 5.1 as a supported session model, after a two-reader audit of the Opus 5.5 system card that retires no guardrail (#883). The docs add effort guidance (Claude Code starts Opus 5.5 at medium; heavy runs should use high or above), one list-price re-derivation for both models, and tiering guidance that reads the ladder as the vendor's lineup order, not a capability order. Because the card reports that Opus 5.5 acts more often than earlier models on instructions inside pasted text, the revision coach now treats pasted reviewer and committee text as data, pinned by a lint; the effect of that prompt-level guard is unmeasured. The release also repairs mode loading and citation checks: the 13 plugin mode commands invoke their namespaced core skill and resolve bundled references from the plugin root, which restores citation-check loading (#857); Chinese APA 7 checks catch a missing in-text author abbreviation, keep ambiguity exceptions and complete reference-list author fields, and propose a reorder only with evidence of a stroke-order inversion (#882); citation checks now distinguish visible syntax errors from unverified resolution or source claims (#882); and new English, Traditional Chinese, and Korean trigger phrases route citation-check requests, with CI bounding each skill description at 1,024 code points (#858, #864). The Pi wrapper accepts string-array system prompts (#880). No schema, command model, or effort setting changes.