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.

AI is your copilot, not the pilot. This tool won't write your paper for you. 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 writing the sentence after "I argue that."

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. Calibration is shipped as a 20-tuple gold set with FNR<0.15 + FPR<0.10 acceptance thresholds; ramp-on plan is deferred to post-calibration evidence per 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.

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.

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): The core skills (research / write / review) need no Python — they are prompt-driven. A real Python interpreter is needed only for: the PreToolUse write-scope guard (optional subagent hardening — if no real Python is found it cleanly no-ops and the guard is simply inactive; core skills are unaffected), plus a few opt-in features that shell out to Python (revision-patch mode, the submission-package verifier, and the /ars-cache-invalidate / /ars-mark-read / /ars-unmark-read commands). On Windows, note that python3 is often a non-functional Microsoft Store placeholder rather than real Python; install Python from python.org (or via winget) so the launcher can find a real interpreter. The guard launcher is a POSIX shell script and hooks.json invokes it through bash, so on Windows it needs Git Bash (bundled with Git for Windows). With Git Bash present, a missing real Python degrades cleanly (the guard no-ops, silently). Without Git Bash, Claude Code falls back to PowerShell, which cannot run the .sh launcher at all: the guard is inactive and the PreToolUse hook will log an error per call rather than no-op quietly (accepted degradation — the guard is optional and never blocks your writes, but the hook noise is the trade-off until Git Bash is installed).

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 four skills import directly: Skills → Import from GitHub, paste https://github.com/Imbad0202/academic-research-skills, Preview, then Import 4 skills (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 (~$4–6 for a 15k-word paper), 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）

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).
• 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 Stage 1 decision (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.

Showcase: real pipeline output

See the complete artifacts from a real 10-stage pipeline run — peer review reports, integrity verification reports, and the final paper:

Browse all pipeline artifacts →

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): at Stage 1, ARS detects whether the run will carry experiment-backed claims and sets a fail-closed experiment_intake_declaration on the Material Passport. 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; 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.

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).

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

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 (v3.21.0)

10-stage orchestrator with integrity verification, two-stage review, Socratic coaching, and collaboration evaluation. Pipeline 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) independently verifies author revision claims. 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.

v3.0 Optimizations: What We Discovered About AI's Structural Limits

What happened

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.

What we changed (v3.0)

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

Why this matters

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.

License

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

Contributors

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).

Changelog

v3.21.0 (2026-08-18) — ISO/IEC 42001-spirit transparency, verifiability, and feasibility track

Transparency you can check: v3.21.0 completes the ISO/IEC 42001-spirit audit track (#753–#760). Outward claims are aligned with the evidence record, and four standing artifacts now answer the questions users actually have: which controls operate in your install channel (docs/CONTROL_AVAILABILITY.md), what leaves your machine and what is stored (docs/DATA_FLOWS.md), how strongly each CI workflow actually enforces (docs/ARCHITECTURE.md §7.1), and which risk each mechanism addresses, with what evidence and what residual gap (docs/RISK_REGISTER.md) — each defended by its own CI lint. GOVERNANCE.md states decision authority, what cross-model review does and does not provide (an error-detection control, not organizational independence), and the end-of-life posture; SECURITY.md gains a solo-runnable severity-tiered triage procedure. These are distilled operating principles with informative anchors to ISO/IEC 42001, not a certification claim, and no new effectiveness numbers are claimed. Suite/pipeline → v3.21.0; deep-research → v2.12.1; academic-paper → v3.3.1; academic-paper-reviewer → v1.11.1.

v3.20.1 (2026-08-15) — Contract-honesty hardening and bounded evaluation substrates

Hardened and bounded: v3.20.1 makes review and integrity claims match the evidence the suite can actually replay. Claim coverage is population-bounded with semantic completeness left unknown; revision claim-strength changes require explicit byte-bound author dispositions; new read attestations require scope and fail visibly; live reviewer packages remain NOT_CALIBRATED; and six-axis provenance replaces binary independence language. It also ships the offline/unmeasured claim-standing probe substrate and a closed first-round assignment gate for the ideation-diversity bundle, plus an opt-in roadmap for future inquiry branches and alternatives. These contracts do not establish improved scientific outcomes, reviewer correctness, complete semantic detection, authenticated human identity, independent error processes, or live-provider effectiveness. Suite/pipeline → v3.20.1; deep-research → v2.12.1; academic-paper → v3.3.1; academic-paper-reviewer → v1.11.1.

v3.20.0 (2026-08-14) — Evidence-bound review and revision, contained transports, hermetic evaluation substrates

Added and hardened: v3.20.0 strengthens evidence and authority boundaries across review, revision, citation, human-subjects, and submission workflows. It adds source-bound evidence rows, author-controlled non-ranking revision roadmaps, replay-bound consistency and content-coverage advisories, unified review criteria, human-subjects authority and pathway traces, deterministic submission and correspondence artifacts, optional post-run adjudication observability, a contained ChatGPT-subscription citation transport, an offline claim-standing candidate ledger, and an opt-in process-isolated PDF text/OCR advisory. Reviewer and re-review contracts gain role-scoped scoring, evidence-before-persuasion gates, and tighter provenance and transport handling; clinical reporting, Chinese-literature resolution, plugin aliases, Pi, and platform guidance are also extended. New evaluation assets include frozen hermetic fixtures and no-call envelopes for revision drift, role topology, ideation diversity, indirect prompt injection, and tortured-phrase screening. Unless a retained measured cohort is explicitly cited in CHANGELOG.md, these are protocol or synthetic/offline conformance artifacts, not evidence of safety, efficacy, accuracy, or behavioral improvement. Suite/pipeline → v3.20.0; deep-research → v2.12.0; academic-paper → v3.3.0; academic-paper-reviewer → v1.11.0.

v3.19.0 (2026-07-22) — Revision-round claim-drift guards, PDF read-integrity preflight, read-scope attestation

Added: three advisory-or-opt-in integrity layers plus a launcher fix. Revision-round claim-drift guards (#569/#570): a claim-strength ladder ("no silent move along is associated with < predicts < causes without an authorizing roadmap item") wired into revision drafting and a new advisory Phase E6, plus a deterministic numeric/citation token-conservation checker — together they stand watch over the epistemic and token halves of the #390 honest-claim residual (a touched block's interior had no fidelity check; whether the guards reduce the measured dr