经 AI Skill Hub 精选评估,学术研究技能框架 获评「强烈推荐」。已获得 7.4k 颗 GitHub Star,这款Prompt模板在功能完整性、社区活跃度和易用性方面表现出色,AI 评分 8.2 分,适合有一定技术背景的用户使用。
为Claude设计的开源学术研究工作流Prompt模板,涵盖研究、写作、审阅、修订全流程。帮助学生、研究者和学术写作者提升论文质量和研究效率,支持AI辅助的学术规范写作。
学术研究技能框架 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力,让 AI 生成更准确、更有价值的输出结果。无需任何安装,直接复制模板内容到 AI 对话框即可使用。
为Claude设计的开源学术研究工作流Prompt模板,涵盖研究、写作、审阅、修订全流程。帮助学生、研究者和学术写作者提升论文质量和研究效率,支持AI辅助的学术规范写作。
学术研究技能框架 是经过精心设计和反复验证的专业 Prompt 模板集合。这些 Prompt 框架能够有效激活 Claude、ChatGPT 等大型语言模型的深层能力,让 AI 生成更准确、更有价值的输出结果。无需任何安装,直接复制模板内容到 AI 对话框即可使用。
# Prompt 无需安装,直接复制使用 # 支持:Claude / ChatGPT / Gemini / 通义千问 等主流模型 # 使用步骤 # 1. 复制 Prompt 模板内容 # 2. 粘贴到 AI 对话框 # 3. 替换 [占位符] 为实际内容 # 4. 发送后获取结构化输出 # 获取原始文件 git clone https://github.com/Imbad0202/academic-research-skills
# 粘贴到 Claude/ChatGPT 使用 # 示例 Prompt 结构: 你是一位 [角色],擅长 [领域]。 请根据以下要求完成任务: 任务背景:[描述背景] 具体要求:[详细说明] 输出格式:[期望格式] # 将 [] 内内容替换为实际需求
# academic-research-skills 配置文件示例(config.yml) app: name: "academic-research-skills" debug: false log_level: "INFO" # 运行时指定配置文件 academic-research-skills --config config.yml # 或通过环境变量配置 export ACADEMIC_RESEARCH_SKILLS_API_KEY="your-key" export ACADEMIC_RESEARCH_SKILLS_OUTPUT_DIR="./output"
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.
NOT_CALIBRATED; full calibration produces a bounded candidate profile, while live-profile application is not yet wired.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 (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 (open-ended or outcome-gradable). All current ARS skills are open-ended.shared/benchmark_report_pattern.md.repro_lock sub-block on Material Passport. Configuration documentation, not replay guarantee — LLM outputs are not byte-reproducible. See shared/artifact_reproducibility_pattern.md.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.[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[] 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.
---
A minor release shipping the Kong et al. (2026, arXiv:2605.18661) auto-research feature track plus the partial-evidence-trap decomposition work, each reviewed and merged independently. New features: Experiment Provenance Intake + claim→experiment alignment — a schema-first evidence-ledger layer for experiment-backed claims, intake-and-alignment only (the scholar runs experiments externally; ARS never executes them) (#260); a Figure/Table Fidelity Gate that checks whether a caption's interpretation follows from the data and whether the manuscript cites the artifact for a claim it supports (#261); a structured Cross-Paper Contradiction inventory making assessed paper-pairs enumerable for scholar confirmation (#262); and sub-claim decomposition before judgment in both the citation judge (#213) and the editorial synthesizer (#214), closing the §F.3.2 partial-evidence trap on both layers. Guidance + interpretive layer: concise-output + pressure-stable boundary reinforcement across the report-producing reviewers (#274); a same-family / rubric-aware calibration epistemic note (#273); the retrieved-content instruction/data boundary stated as a standing principle (#367). Negative scope: the Kong META (#255) closed with a "Rejected mechanisms" section inPOSITIONING.mdenumerating the five autonomous mechanisms ARS does not do, plus two Tier D design-lesson docs. Release-discipline lint: version-consistency invariants 5–7 (#357) and ARCHITECTURE component-version policing (#345). Plus correctness fixes across the cross-model grounding guards (#346 / #349 / #351), the citation-gate cache key and rationale bounding (#359 / #360 / #361), the eval gold set (#250), and ACL/EMNLP disclosure regrounding (#242). The new schemas, manifest field, and all invariants are additive and backward-compatible.academic-pipelinetracks the suite at v3.12.0; the other three skill versions are unchanged. SeeCHANGELOG.mdfor the per-issue detail.
Prerequisites
ANTHROPIC_API_KEY exported, or set on first claude runPreToolUse 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.
---
```
You: "Guide me through writing a paper on demographic decline"
Fixed: two under-specified pipeline boundaries closed (#528) — Stage 5's "before finalization: always MANDATORY" now names exactly one checkpoint (the entry gate between Stage 4.5 PASS and Stage 5 dispatch), and Stage 6 gains a defined terminal-acknowledgement vocabulary (finish/end/done/confirm) plus an explicit decline path; all five pipeline surfaces now carry whole-file sha256 content locks (#529) so any further prompt-surface drift fails CI until the hash is updated in the same commit. Blind-checkpoint transport moved to the dispatching layer (#523) — the Bucket A checkpoint owners were being told to execute cross-model transport themselves, which is unexecutable under the runtime Bash deny; the dispatching layer now owns the transport call. Added: a canonical[CROSS-MODEL-HANDOFF v1]envelope + normative Python grammar (#527) replaces prose-only enforcement of the owner→dispatcher→owner transport path, pinning agreement/divergence/malformed-result routing across all three checkpoint owners. A defrift lock for the #514 tools allowlist (#524, 74 mutation tests) closes the drift path where a symmetric edit to an agent + its mirror could silently re-add Bash. An executable sprint-contract panel checker (#510) recomputes both v3.6.2 decision layers from the primary artifacts and catches a transcription error in the majority-vote formula. A machine-readable degradation registry (#511 Part A) indexes every graceful-degradation mechanism in the suite, plus a hermetic transport-fixture integration test for the citation-verification gate (#511 Part B) exercising all four resolver clients end-to-end against checked-in synthetic API bodies.academic-pipelinetracks the suite at v3.17.0; the other three skill versions are unchanged.
Minor release bundling: the opt-in contamination-triangulation terminal policy layer (#127 — default citation behavior byte-equivalent to v3.9.0); Kong et al. 2026 survey adoptions — the Rebuttal Commitment Ledger (#256/#266/#268/#269) and discipline-relative domain evidence profiles (#259); the v3.10 measurement infrastructure — a generalized eval gold set + ranking-lift CI gate (#184); the scoped-write guard MVP (#134) — a deterministicPreToolUsehook that fences the 23 single-phase agents to their own phase directory and denies them Bash (they use the Grep/Glob and structured editing tools instead); the/ars-mark-readplugin commands (#190) plus a broken-on-arrival fix (#195); a Simplified-Chinese README (#185); and CI hardening (#156/#155).academic-paper→ v3.2.0 andacademic-paper-reviewer→ v1.10.0 for the Commitment-Ledger and domain-profile additions;academic-pipelinetracks the suite at v3.10.0. Default skill behavior is unchanged unless a strict policy mode is opted into; the one default-on change is the #134 guard, which constrains the fenced subagents, not user-facing outputs.
👉 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.
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 →
| Artifact | Description |
|---|---|
| [Final Paper (EN)](examples/showcase/full_paper_apa7.pdf) | APA 7.0 formatted, LaTeX-compiled |
| [Final Paper (ZH)](examples/showcase/full_paper_zh_apa7.pdf) | Chinese version, APA 7.0 |
| [Integrity Report — Pre-Review](examples/showcase/integrity_report_stage2.5.pdf) | Stage 2.5: caught 15 fabricated refs + 3 statistical errors |
| [Integrity Report — Final](examples/showcase/integrity_report_stage4.5.pdf) | Stage 4.5: zero regressions confirmed |
| [Peer Review Round 1](examples/showcase/stage3_review_report.pdf) | Journal-Fit Reviewer + 3 Reviewers + Devil's Advocate |
| [Re-Review](examples/showcase/stage3prime_rereview_report.pdf) | Verification after revisions |
| [Peer Review Round 2](examples/showcase/stage3_review_report_r2.pdf) | Follow-up review |
| [Response to Reviewers](examples/showcase/response_to_reviewers_r2.pdf) | Point-by-point author response |
| [Post-Publication Audit Report](examples/showcase/post_publication_audit_2026-03-09.pdf) | Independent full-reference audit: found 21/68 issues missed by 3 rounds of integrity checks |
---
You: "I want to write a research paper on AI's impact on higher education QA"
You: "status" ```
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.
---
Measured where stated; otherwise bounded: v3.21.1 repairs the contained ChatGPT-subscription citation transport for codex-cli 0.147.0 and records the first Promotion Bakeoff:gpt-5.6-solis validated only for that subscription transport, while it remains provisional on the first-party API route. Future bakeoffs now require sealed preregistration. The release also adds a default-off research-workflow profile substrate (offline deterministic conformance only; no pipeline hook or family-specific shipped profile), an opt-in inquiry-ledger alpha (ARS_INQUIRY_LEDGER=1), and a design-only alternative register that is not implemented. Their behavioral evidence remainsNOT_RUN; no usability, recovery, novelty, correctness, or research-outcome benefit is claimed. The review-criteria registry gains one source-backed illustrative MSR 2027 exact-profile proving set—not venue/discipline coverage, a real-author attestation, or constructive-review evidence—and its required independent-human evaluation remains open. Additional changes aligndata_access_level, consolidate markdown lint grammar, register guard-launcher degradations, and list OrcaRouter as a community integration without endorsement. Suite/pipeline → v3.21.1; deep-research → v2.12.1; academic-paper → v3.3.1; academic-paper-reviewer → v1.11.1.
Added: eight quality mechanisms motivated by Ren et al. (2026, arXiv:2607.13104, Self-Improvements in Modern Agentic Systems: A Survey): per-sub-question scope bindings + a Phase E scope-conformance advisory (#547) and search-bounded novelty claims + an E5 novelty classification (#548) — both advisory-only, displayed per-row at the MANDATORY integrity checkpoints; risk-stratified Stage 2.5 claim verification (100% of HIGH-IMPACT claims + a random sentinel, extending the #518 reference tiers to claim level, #549); cache-through wired into the citation-verification gate with an age-based staleness advisory + opt-in live re-validation (#541, closing the v3.11 Delta-2 forward-decl); a consent-gated cross-model reviewer track — one seat of the fixed five-seat panel on the second model family (#540) — and re-review judge independence with a transparent Judge Record (#539); a metamorphic routing/gate robustness eval seed set (#550), which also shipped the reviewer skill's missing zh-TW trigger aliases; and the survey itself as a third human-in-the-loop literature anchor (#542). Independently of the survey track, plugin installs also gain the #544 SessionStart update-available reminder (/plugin updateannounce when behind;ARS_UPDATE_CHECK=0kill switch).academic-pipelinetracks the suite at v3.18.0; the other three skill versions are unchanged.
A patch release folding the genuinely-novel parts of an external contribution into existing skills as modes, per ARS's mode-based architecture. New modes:deep-researchthree-way-scan— a lightweight WHY/HOW/WHAT paper-comparison triage betweenquickandlit-review, with per-paper shortlists + a cross-paper synthesis (deep-research2.9.4 → 2.10.0);academic-paperrebuttal-audit— standalone advisory QA of an author's existing rebuttal/response draft against the reviewer comments (per-comment coverage table + gap list + tone/evidence/misread risk flags), which generates nothing and explicitly suppresses Schema 11 / Material Passport writes /ready_to_submitwhen run standalone (enforced by acheck_rebuttal_audit_guard()lint with mutation coverage); plus arevision-coachscope extension to pushback/disagreement posture and non-journal scopes, and/ars-3w+/ars-rebuttal-auditslash commands. Routed by input shape: reviewer comments AND a draft →rebuttal-audit; comments only →revision-coach. Integrated from @Yaobin29's PR #433. Suite mode count 25 → 27 (still 4 skills). SeeCHANGELOG.mdfor the per-issue detail.
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| 原始名称 | academic-research-skills |
| 原始描述 | 开源Prompt模板:Academic Research Skills for Claude Code: research → write → review → revise → f。⭐7.4k · Python |
| Topics | 学术写作研究工作流Prompt模板Claude论文辅助 |
| GitHub | https://github.com/Imbad0202/academic-research-skills |
| License | NOASSERTION |
| 语言 | Python |
收录时间:2026-05-15 · 更新时间:2026-05-19 · License:NOASSERTION · AI Skill Hub 不对第三方内容的准确性作法律背书。
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