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学术研究技能框架
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Prompt模板

学术研究技能框架

基于 Python · 专业级提示词模板,解锁 AI 的真实潜力
英文名:academic-research-skills
⭐ 7.4k Stars 🍴 853 Forks 💻 Python 📄 NOASSERTION 🏷 AI 8.2分
8.2AI 综合评分
学术写作研究工作流Prompt模板Claude论文辅助
✦ AI Skill Hub 推荐

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。

📚 深度解析

学术研究技能框架 是经过精心设计和实践验证的专业 Prompt 模板。Prompt 工程(Prompt Engineering)是充分发挥 Claude、ChatGPT 等大型语言模型潜力的关键技能,而一套经过优化的 Prompt 模板可以将 AI 输出质量提升数倍。

优质 Prompt 模板的核心价值在于其结构化设计:明确的角色设定、精确的任务描述、具体的输出格式要求和必要的边界条件,这些要素共同构成了一个能够持续产出高质量结果的 Prompt 框架。学术研究技能框架 提供的模板经过反复迭代和用户验证,能够有效减少 AI 的"幻觉"(Hallucination)和输出不稳定问题。

无论你使用 Claude 3.5 Sonnet、GPT-4、Gemini 还是国内的文心一言、智谱 AI,优质的 Prompt 设计都能跨模型复用。AI Skill Hub 建议将本模板保存为个人 Prompt 库的标准组件,根据具体场景调整参数后反复使用,形成自己的 AI 提效工作流。

📋 工具概览

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。

GitHub Stars
⭐ 7.4k
开发语言
Python
支持平台
Windows / macOS / Linux
维护状态
持续维护,定期更新
开源协议
NOASSERTION
AI 综合评分
8.2 分
工具类型
Prompt模板
Forks
853

📖 中文文档

以下内容由 AI Skill Hub 根据项目信息自动整理,如需查看完整原始文档请访问底部「原始来源」。

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。

📌 核心特色
  • 精心设计的 Prompt 框架,快速激活 AI 的深层能力
  • 支持参数化替换,灵活适配多种业务场景
  • 经过反复验证的指令结构,显著提升 AI 输出质量和一致性
  • 适用于 Claude、ChatGPT 等主流大语言模型
  • 可作为团队标准 Prompt 模板复用和二次开发
🎯 主要使用场景
  • 快速生成高质量的专业文案、分析报告或结构化内容
  • 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
  • 在不同 AI 工具间复用经过验证的提示词模板
以下安装命令基于项目开发语言和类型自动生成,实际以官方 README 为准。
安装命令
# 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
📋 安装步骤说明
  1. 复制本工具的 Prompt 模板内容
  2. 打开 Claude、ChatGPT 或其他 AI 对话工具
  3. 将 Prompt 粘贴到对话框开头
  4. 根据实际需求替换 [占位符] 中的内容
  5. 发送后 AI 将按照模板格式执行,获得结构化输出
以下用法示例由 AI Skill Hub 整理,涵盖最常见的使用场景。
常用命令 / 代码示例
# 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
以下配置示例基于典型使用场景生成,具体参数请参照官方文档调整。
配置示例
# 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"
📑 README 深度解析 真实文档 完整度 64/100 查看 GitHub 原文 →
以下内容由系统直接从 GitHub README 解析整理,保留代码块、表格与列表结构。

Academic Research Skills for Claude Code

Version DOI License: CC BY-NC 4.0 Sponsor

简体中文版 | 繁體中文版 | 日本語版 | 한국어

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.

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

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_INSUFFICIENTwithout 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.

---

v3.12.0 (2026-06-08) — Kong auto-research feature track: experiment provenance, figure fidelity, cross-paper contradiction, partial-evidence decomposition

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 in POSITIONING.md enumerating 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-pipeline tracks the suite at v3.12.0; the other three skill versions are unchanged. See CHANGELOG.md for the per-issue detail.

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.

Guides & articles

---

Usage

Quick Start

```

Write a paper with guided planning

You: "Guide me through writing a paper on demographic decline"

v3.17.0 (2026-07-16) — Pipeline boundary semantics, canonical cross-model handoff envelope, executable panel checker

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-pipeline tracks the suite at v3.17.0; the other three skill versions are unchanged.

v3.10.0 (2026-06-01) — Triangulation policy layer, Kong survey adoptions, eval harness, scoped-write guard

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 deterministic PreToolUse hook 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-read plugin commands (#190) plus a broken-on-arrival fix (#195); a Simplified-Chinese README (#185); and CI hardening (#156/#155). academic-paper → v3.2.0 and academic-paper-reviewer → v1.10.0 for the Commitment-Ledger and domain-profile additions; academic-pipeline tracks 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.

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.

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 →

ArtifactDescription
[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

---

Start a full research pipeline

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

Check pipeline status

You: "status" ```

Academic Pipeline (v3.21.2)

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.

---

v3.21.1 (2026-08-24) — Bounded workflow substrates, sealed bakeoffs, and transport hardening

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-sol is 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 remains NOT_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 align data_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.

v3.18.0 (2026-07-18) — Self-improvement survey integration: advisory quality layers, risk-stratified claim gate, cross-model reviewer & judge tracks

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 update announce when behind; ARS_UPDATE_CHECK=0 kill switch). academic-pipeline tracks the suite at v3.18.0; the other three skill versions are unchanged.

v3.12.1 (2026-06-15) — Reviewer-response triage modes (PR #433 integration)

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-research three-way-scan — a lightweight WHY/HOW/WHAT paper-comparison triage between quick and lit-review, with per-paper shortlists + a cross-paper synthesis (deep-research 2.9.4 → 2.10.0); academic-paper rebuttal-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_submit when run standalone (enforced by a check_rebuttal_audit_guard() lint with mutation coverage); plus a revision-coach scope extension to pushback/disagreement posture and non-journal scopes, and /ars-3w + /ars-rebuttal-audit slash 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). See CHANGELOG.md for the per-issue detail.
🎯 aiskill88 AI 点评 A 级 2026-05-19

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。

📚 实用指南(长尾问题)
适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 跨境业务、多语言内容运营团队
  • 想快速复用高质量提示词模板的 AI 用户
最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • Python 依赖冲突:建议用 venv / uv 隔离环境
部署方案
  • CLI:直接 npm install -g / pip install,命令行调用
  • 云端托管:可放在 Vercel / Railway / Fly.io 等 PaaS 平台
相关搜索
academic-research-skills 中文教程academic-research-skills 安装报错怎么办academic-research-skills Agent 工作流academic-research-skills 与同类工具对比academic-research-skills 最佳实践academic-research-skills 适合谁用

⚡ 核心功能

👥 适合谁
  • 构建多智能体协作系统的 Agent 开发者
  • 跨境业务、多语言内容运营团队
  • 想快速复用高质量提示词模板的 AI 用户
⭐ 最佳实践
  • Agent 任务先做 dry-run 验证工具调用链,再开启自主执行
⚠️ 常见错误
  • API key 直接提交到 git 仓库(请用 .env 并加入 .gitignore)
  • Python 依赖冲突:建议用 venv / uv 隔离环境

👥 适合人群

内容创作者和自媒体人职场人士和学生ChatGPT / Claude 重度用户希望提升 AI 使用效率的普通用户

🎯 使用场景

  • 快速生成高质量的专业文案、分析报告或结构化内容
  • 利用 Prompt 框架引导 AI 解决特定领域的复杂问题
  • 在不同 AI 工具间复用经过验证的提示词模板

⚖️ 优点与不足

✅ 优点
  • +GitHub 7.4k Star,社区高度认可
  • +无需安装,立即可用
  • +适配所有主流 AI 工具
  • +经社区验证的最佳实践
⚠️ 不足
  • 效果依赖使用者对 Prompt 工程的熟悉程度
  • 不同模型和版本的响应效果可能存在差异
  • 复杂场景需结合实际需求二次调整
⚠️ 使用须知

该工具使用 NOASSERTION 协议,商用场景请仔细阅读协议条款,必要时咨询法律意见。

AI Skill Hub 为第三方内容聚合平台,本页面信息基于公开数据整理,不对工具功能和质量作任何法律背书。

建议在沙箱或测试环境中充分验证后,再部署至生产环境,并做好必要的安全评估。

📄 License 说明

📄 NOASSERTION — 请查阅原始协议条款了解具体使用限制。

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❓ 常见问题 FAQ

模板内置了学术写作规范检查步骤,结合人工审阅可保证质量。
💡 AI Skill Hub 点评

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。

⬇️ 获取与下载
📚 深入学习 学术研究技能框架
查看分步骤安装教程和完整使用指南,快速上手这款工具
🌐 原始信息
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
🔗 原始来源
🐙 GitHub 仓库  https://github.com/Imbad0202/academic-research-skills 🌐 官方网站  https://buymeacoffee.com/crucify020v

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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