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Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
Assist with Colibri: pure-C LLM inference engine for running GLM-5.2 (744B MoE) on consumer machines with ~25 GB RAM. Use when setting up, building, converting models, running inference, configuring expert streaming and caching, optimizing speculative decoding (MTP), GPU integration, and integrating Colibri into production pipelines. Includes build setup, model download & conversion, chat/inference modes, performance tuning, and API integration patterns.
Discover and apply curated prompts from the prompts.chat collection to optimize AI interactions. Use when refining prompt engineering, finding domain-specific prompt templates, improving response quality, or building prompt-based workflows. Triggers on: prompt optimization, prompt templates, prompt engineering, prompt library, curated prompts, prompt discovery, and AI prompt patterns.
Turn ONE topic into a finished Vox-style paper-collage explainer / ad video, end to end on the Atlas Cloud API + local ffmpeg — script, collage keyframes, motion, voice-over, music, captions, all automated. Use this whenever the user wants a "Vox style" video, a paper/torn-paper collage animation, a "motion collage", a narrated explainer or short ad built from AI-generated collage posters, a scrapbook-style tribute, or wants to turn a topic / product / person into a punchy narrated collage video — even if they don't say the word "Vox". Also use when reproducing Stav Zilber / rom1trs / Higgsfield-style collage ad workflows, or when the user asks for a motion collage or a scrapbook-style tribute. Triggers: "vox video", "collage video", "motion collage", "paper collage explainer", "make a collage ad", "turn this topic into a collage video".
Assist with Motion Previs Studio v4: a cross-platform desktop app for AI-film previsualization. Use when setting up, configuring, troubleshooting, or extending motion-previs-studio for pose extraction, depth mapping, camera motion solving, control layer export, and bundle production for AI-video workflows (Seedance, ComfyUI, Blender, Runway, Kling). Includes build setup, feature integration, UI/logic debugging, and export pipeline optimization.
Work with Lapian Notes / 拉片笔记 (github.com/bkingfilm/lapian-notes) — a local- first React/Vite tool that turns a film into an editable shot-by-shot study notebook: local frame extraction, AI-assisted structure analysis (bring your own AI, no API key required), story-line swimlane timeline, structure tree, and audience-emotion curve. Use when the user asks about Lapian Notes, "拉片笔记", "拉片" (shot-by-shot film analysis) tooling, cloning/running this repo (npm run dev, run.bat/run.command), the AI-analysis-package (ZIP) round-trip workflow, or contributing a PR to lapian-notes. Not for generic video editing (use `opencut` for that) or generic film-analysis theory unrelated to this codebase.
Set up, run, and contribute to TokHub (github.com/yaojingang/TokHub) — an open-source AI API relay monitoring, recommendation, and OpenAI-compatible gateway system with L1/L2/L3 channel health probing, usage metering, alerts, audit, and Docker self-hosting. Use when the user asks about TokHub, "AI API 中转站监控", cloning/running the Go + React monorepo (TOKHUB_ROLE, sqlc, TimescaleDB, NATS), the L1/L2/L3 probe algorithm, the OpenAI-compatible `/gateway/v1/*` endpoint, or contributing a PR to TokHub. Do not use for connecting a running agent to a live TokHub instance's own API (that is covered by the project's own bundled `agent-skills/tokhub` skill inside the TokHub repo, not this one).
| name | academic-research |
| description | > |
| compatibility | > |
| allowed-tools | Bash Read Write Edit Glob Grep WebFetch |
| metadata | {"tags":"academic-research, deep-research, paper-writing, peer-review, literature-review, systematic-review, citation-check, research-pipeline, scholarly-publishing, ars","version":"1.0.0","source":"https://github.com/Imbad0202/academic-research-skills","upstream_version":"3.13.0","license":"CC-BY-NC-4.0"} |
A routing-first front door for the full Academic Research Skills (ARS) suite — 4 pipelines, 27 modes, spanning the complete research-to-publication lifecycle.
AI is copilot, not pilot. ARS handles the grunt work (hunting references, verifying citations, checking logical consistency, formatting). You handle the parts that require your brain: defining the question, choosing the method, interpreting results, and writing the sentence after "I argue that."
Read these pipeline references before executing:
# Full upstream suite (all 4 skills, 27 modes — recommended for heavy use)
claude plugin marketplace add Imbad0202/academic-research-skills
# Or install this routing skill only via jeo-skills
npx skills add https://github.com/akillness/jeo-skills --skill academic-research
technical-writingautoresearchmarketing-automationautoresearchtechnical-writing or user-guide-writingBefore routing, identify:
| What the user says | Pipeline | Mode | Oversight |
|---|---|---|---|
| "research [topic]", "deep research", "academic analysis" | deep-research | full | High |
| "quick brief", "30 minute summary" | deep-research | quick | Medium |
| "review this paper's research quality" | deep-research | review | High |
| "literature review", "annotated bibliography" | deep-research | lit-review | Medium |
| "WHY HOW WHAT papers", "3W scan", "compare papers" | deep-research | three-way-scan | Low |
| "verify claims", "fact-check", "evidence verification" | deep-research | fact-check | Medium |
| "guide my research", "help me think through" | deep-research | socratic | Very High |
| "systematic review", "meta-analysis", "PRISMA" | deep-research | systematic-review | Medium |
| "write a paper on X", "academic paper", "research paper" | academic-paper | full | High |
| "guide me through writing", "help me plan my paper" | academic-paper | plan | Very High |
| "build a paper outline" | academic-paper | outline-only | High |
| "I have reviewer comments", "revise my paper" | academic-paper | revision | High |
| "parse reviewer comments into a roadmap" | academic-paper | revision-coach | Medium |
| "write an abstract" | academic-paper | abstract-only | Medium |
| "turn this into a literature review paper" | academic-paper | lit-review | Medium |
| "convert to LaTeX", "convert citations to IEEE" | academic-paper | format-convert | Medium |
| "check citations" | academic-paper | citation-check | Medium |
| "generate AI disclosure statement for NeurIPS" | academic-paper | disclosure | Medium |
| "audit my rebuttal draft against reviews" | academic-paper | rebuttal-audit | Medium |
| "review this paper" → full EIC + R1/R2/R3 + Devil's Advocate | academic-paper-reviewer | full | High |
| "quick assessment of this paper" | academic-paper-reviewer | quick | Low |
| "guide me to improve this paper" | academic-paper-reviewer | guided | Very High |
| "check the methodology" | academic-paper-reviewer | methodology-focus | Medium |
| "verify the revisions" | academic-paper-reviewer | re-review | Medium |
| "calibrate this reviewer against my gold set" | academic-paper-reviewer | calibration | Medium |
| "I want to write a complete research paper" | academic-pipeline | full (10-stage) | Very High |
| "continue pipeline from reset boundary" | academic-pipeline | resume_from_passport | High |
deep-research → topic investigation, literature synthesis, fact-checking, PRISMA
academic-paper → write, outline, revise, abstract, format, rebuttal, disclosure
academic-paper-reviewer → evaluate, review, calibrate reviewer quality
academic-pipeline → full orchestrated 10-stage research → paper → review → revise → finalize
If ambiguous between deep-research and academic-paper: the user doing discovery → deep-research; the user writing or revising a document → academic-paper.
socratic or plan modes: begin Socratic dialogue immediately (one question at a time)full or revision modes: confirm the topic/paper and output format firstcitation-check or fact-check: confirm which claims or sections to targetFollow the pipeline reference for the chosen mode:
Checkpoints marked [USER CHECKPOINT] require explicit user confirmation before continuing. Never skip them.
Every completed mode should return:
Pipeline: <which pipeline>
Mode: <which mode>
Topic/Input: <research topic or paper title>
Output: <the primary artifact — report / draft / review / outline>
Citations: <APA 7.0 / IEEE / inline — as applicable>
Integrity: <claim verification status if fact-check or citation-check was run>
Next step: <recommended follow-up mode or pipeline>
data_access_level: verified_only — ARS never uses unverified claims in outputstask_type: open-ended — all modes are open-ended; ARS does not run experimentsALIGNED / OVERSTATED / NOT_SUPPORTED_BY_PROVENANCE)academic-paper plan mode can learn your voice from past work samples| If the user needs… | Route to |
|---|---|
| ML training experiment loop | autoresearch |
| Prompt / skill eval pipeline | skill-autoresearch |
| Engineering design doc or ADR | technical-writing |
| API portal or SDK reference | api-documentation |
| Karpathy-style ML research | autoresearch |
| General content / blog / newsletter | marketing-automation |
| Code documentation | technical-writing |