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name: gpt-academic description: "Operate GPT Academic chat, academic document, code analysis, agent tooling, and multimodal media workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

GPT Academic

Use this root skill when a user asks an agent to operate, configure, troubleshoot, or choose workflows inside GPT Academic. Keep the root as a router: load the nearest sub-skill for task depth, and use the root references/scripts only for shared setup, provider routing, and environment checks.

Fast orientation

GPT Academic is a Gradio app with prompt buttons and plugin groups for:

• text chat, model routing, internet search, multi-model query, RAG, Mermaid, and conversation archives;
• academic PDFs, Arxiv papers, LaTeX projects, Word/Markdown files, paper reading, document QA, DOC2X/GROBID/NOUGAT parser choices;
• programming workflows such as source-code project analysis, notebook analysis, Markdown translation, and Python docstring generation;
• agentic tools such as Void Terminal, Code Interpreter, Commandline Assistant, and dynamic plugin dispatch;
• multimodal image, voice, audio, video, TTS, and animation workflows.

Install and preflight

From a GPT Academic checkout, use a Python 3.9-3.11 environment and install the repository requirements:

python -m pip install -r requirements.txt
python -m pip install 'setuptools<81'  # only if gradio imports fail on pkg_resources
python -c "import gradio, toolbox, core_functional, crazy_functional, request_llms.bridge_all; print('gpt_academic imports ok')"

Start the UI with the bundled wrapper when you want a local service:

bash scripts/launch_app.sh --repo-root <checkout>

Use config_private.py or environment variables for secrets and machine-specific settings. Do not place API keys into shared prompts, generated docs, or test fixtures.

Route by user intent

If the user asks about...	Read this sub-skill	Why
chat, provider setup, LLM_MODEL, AVAIL_LLM_MODELS, proxy, internet search, SearXNG, multi-model query, RAG, saved chats, Mermaid	sub-skills/conversation/SKILL.md	owns core conversation and model/provider operation
PDFs, Arxiv, DOI, LaTeX, Word, Markdown, paper reading, PDF QA, translation, DOC2X/GROBID/NOUGAT, Google Scholar, batch file query	sub-skills/academic-docs/SKILL.md	owns scholarly and office-document processing
source-code project analysis, code explanation, Python docstrings, notebook analysis, README/Markdown translation, file-pattern selection	sub-skills/programming-code/SKILL.md	owns code and technical documentation workflows
Void Terminal, Code Interpreter, Commandline Assistant, natural-language plugin dispatch, dynamic function generation, config mutation	sub-skills/agent-tooling/SKILL.md	owns agent-like control surfaces and safety decisions
image generation or understanding, voice assistant, audio/video summary, Edge TTS, SoVITS, Bilibili/video resources, Manim animation	sub-skills/multimodal-media/SKILL.md	owns media, speech, and visual workflows

When a request spans several areas, route first to the workflow that owns the input artifact. Example: a PDF with code appendix starts in academic-docs; then cross-link to programming-code for code analysis and conversation for Mermaid output.

Shared references

• references/workflow-map.md — read when a prompt is ambiguous or crosses several GPT Academic workflow families.
• references/configuration.md — read before model/provider, proxy, search, document, media, Azure, vLLM, local-model, or TTS setup.
• references/api-reference.md — read when debugging plugin registry names, toolbox helpers, core_functional, crazy_functional, or request_llms.bridge_all.
• references/troubleshooting.md — read for cross-cutting install/import, Gradio, API-key, proxy, model-name, upload-path, and local-model failures.
• references/repo-provenance.md — read before deciding whether this skill is stale for a newer checkout.
• references/repo-routing-metadata.json — structured router metadata used by the managed repo-skill importer; do not edit it free-form during normal use.

Shared scripts

Run these bundled helpers from the generated skill directory, passing the GPT Academic checkout through --repo-root when needed:

Helper	Use
scripts/inspect_runtime.py --repo-root <checkout>	print a no-secret summary of imports, config snapshot, core buttons, plugin groups, and model registry size
scripts/check_proxy.py --repo-root <checkout> --no-network	inspect masked proxy config without a network call; omit --no-network for a small public-IP probe
scripts/check_doc_backends.py --repo-root <checkout>	inspect PDF/Word/LaTeX/NOUGAT/DOC2X-related imports, commands, and credential presence
scripts/check_media_backends.py --repo-root <checkout>	inspect media, TTS, speech, ffmpeg, Manim, and optional GPU/service readiness
scripts/launch_app.sh --repo-root <checkout>	run import preflight and then launch main.py from a checkout

Safety and backend boundaries

• Remote LLMs, search, Arxiv, DOC2X, Edge TTS, image generation, Azure, and vLLM require network, credentials, or external services; diagnose setup before launching long tasks.
• NOUGAT, local models, SoVITS, and vLLM serving are optional heavy backends. GPU may be recommended, but the core GPT Academic skill remains usable without proving those optional paths.
• Destructive operations include clearing caches/history, inserting Python docstrings, Void Terminal config mutation, generated-code execution, and shell commands. Require explicit user confirmation or a backup plan before those operations.
• Do not tell future agents to open original repo docs or tests as runtime documentation. Use this skill's bundled references and scripts; source tests remain verification evidence only.