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.
repro_lock, optional cross-model integrity verification, mid-conversation reinforcement, and score trajectory tracking.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.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)".---
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).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 five installation methods (Plugin, project skills, global skills, claude.ai Project, repo-cloned).
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.
---
```
You: "Guide me through writing a paper on demographic decline"
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) | EIC + 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) cannot be skipped; 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.
---
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.
Plugin packaging upgrade: ARS now installs in one line on Claude Code CLI / VS Code / JetBrains via/plugin marketplace add Imbad0202/academic-research-skills+/plugin install academic-research-skills. The traditionalgit clone + symlink to ~/.claude/skills/flow continues to work — both tracks are first-class.
.claude-plugin/plugin.json declares the suite (4 skills auto-discovered from skills/ directory via relative symlinks). .claude-plugin/marketplace.json registers the plugin so a single GitHub-hosted endpoint serves both the marketplace listing and the plugin source. README + README.zh-TW.md + docs/SETUP.md carry dual-track install instructions.commands/ars-*.md (Phase 2.1, PR #69) mapping MODE_REGISTRY.md entries to /ars-<mode> triggers. Model routing is pinned in each command's frontmatter — opus for full and revision-coach (architectural / review-interpretation depth), sonnet for the other 8. No Haiku per project policy.agents/*_agent.md (Phase 2.1, PR #69) as relative symlinks to the v3.6.7-hardened downstream agents in deep-research/agents/: synthesis_agent, research_architect_agent, report_compiler_agent. Underscore filenames preserved to keep scripts/check_v3_6_7_pattern_protection.py hard-pinned paths and INV-3 manifest-confined Clause 1 invariant intact. Symlinks (not copies) preserve a single source of truth and prevent the Pattern C3 attack surface that v3.6.7 §6 inversion sweep + INV-1/2/3 lint closes. (Materialized to real byte-identical copies in #413 — relative symlinks break Windows checkouts without core.symlinks and zip-download installs; the single-source guarantee moved to the scripts/check_agents_mirror_sync.py byte-equality CI lint.)model: inherit added to those three source agent frontmatters. Inherit chosen over pinning sonnet so an opus session running ARS full pipeline keeps opus agents (instead of being capped). The user's ~/.claude/hooks/warn-agent-no-model.sh PreToolUse hook gates Haiku at the dispatching boundary, so inherit resolves through an already-Haiku-free model.hooks/hooks.json + scripts/announce-ars-loaded.sh (Phase 2.2, PR #70). When the plugin loads, the hook injects an additionalContext listing the 10 slash commands, the 3 plugin agents, and a token-budget pointer into the LLM's first turn. startup and clear source values get the full announce; resume and compact get a one-line ack to avoid burning context. Bash 3.2 compatible — runs on macOS stock /bin/bash with no brew install bash requirement.SubagentStop → run_codex_audit.sh codex audit hook was scoped out for v3.7.0 due to a contract gap (the SubagentStop payload carries no stage/deliverable info, so the wrapper would have to half-infer required arguments) and an invoker-class boundary (run_codex_audit.sh lines 4–7 forbid same-session in-LLM invocation; PostToolUse fires inside the producing session). Real audit-hook integration deferred to a future release when ARS gains a stage/deliverable propagation contract. See docs/design/2026-04-30-ars-v3.7.0-plugin-packaging-roadmap.md Update note 2026-05-05 (Phase 2.2 scope reduction).docs/PERFORMANCE.md + .zh-TW.md gain a "v3.7.0 Plugin agents and model routing" subsection explaining the inherit semantics and current 3-agent scope boundary.${CLAUDE_PLUGIN_ROOT} breaking install paths with spaces) that the inline rounds missed — confirms the value of separating implementation review (inline) from contract review (fresh).commands/, agents/, hooks/, .claude-plugin/, skills/ symlink dir, three plugin-agent model: inherit frontmatter additions). Existing 4.3k clone-install users see no breaking change.deep-research/agents/bibliography_agent.md and academic-paper/agents/literature_strategist_agent.md. Both follow the same five-step corpus-first, search-fills-gap flow when the passport carries a non-empty literature_corpus[] and the same four Iron Rules (Same criteria / No silent skip / No corpus mutation / Graceful fallback on parse failure).obtained_via / obtained_at. final_included = pre_screened_included[] ∪ external_included[] stays neutral — no provenance tags on bibliography entries or literature matrix rows.academic-pipeline/references/literature_corpus_consumers.md with the canonical PRE-SCREENED template, BAD/GOOD examples, four Iron Rules, and per-consumer reading instructions.scripts/check_corpus_consumer_protocol.py enforcing nine protocol invariants with manifest-driven consumer list (scripts/corpus_consumer_manifest.json).shared/handoff_schemas.md retired the v3.6.4 "Consumer-side integration deferred to v3.6.5+" caveat; replaced with backpointer to the consumer protocol.[CORPUS PARSE FAILURE] surface. citation_compliance_agent corpus integration deferred (target version TBD post-v3.8).质量优秀的学术工具,工作流设计科学完整,7.4k星体现高度认可。适合学术写作场景,维护活跃,实用性强。
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AI Skill Hub 点评:学术研究技能框架 的核心功能完整,质量优秀。对于内容创作者和自媒体人来说,这是一个值得纳入个人工具库的选择。建议先在非生产环境试用,再逐步推广。
| 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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