도구

Industrial-grade open-source speech recognition toolkit from Alibaba's ModelScope, with an agent-friendly funasr command that turns audio into text, JSON or SRT subtitles in one step, with speaker diarization and timestamps. Strong on Chinese. Requires PyTorch.

Command-line tool for bulk-downloading galleries and posts from many image hosts and social platforms, with flexible file naming and config, and yt-dlp integration for video. Main development has moved to Codeberg; the GitHub repo stays in sync.

AI2's tool for converting PDFs and scans to Markdown with a vision LLM, handling equations, tables, handwriting and multi-column layouts, suited to bulk digitizing papers and archives. Local runs need an NVIDIA GPU with 12GB+ VRAM; otherwise use a paid OpenAI-compatible remote endpoint.

All-in-one video translation and dubbing: chains speech recognition, subtitle translation, multi-speaker AI voiceover and audio-video muxing, with local or online models like Faster-Whisper, DeepSeek and Edge-TTS. cli.py supports headless batch runs. Needs FFmpeg.

Lets Claude Code push ML research forward unattended: literature review, topic selection, experiment deployment, cross-model review loops and paper writing through rebuttal, in 80+ skills. Codex acts as the independent reviewer by default; other model pairings work too.

Browser control from the browser-use team: an editable CDP connection lets agents drive the Chrome you are already using, writing missing helper functions as they go and keeping them for reuse. Also offers an MCP server. Allow remote debugging in Chrome on first use.

Context and harness engineering skills covering context compression and optimization, multi-agent architectures, memory systems, tool design and LLM-as-judge evaluation, activated automatically by task. Aimed at building production-grade agent systems.

Context and harness engineering skills: context fundamentals, degradation diagnosis and compression, multi-agent architectures, memory systems, tool design, LLM-as-judge evaluation and self-improvement loops. Focused on principles and design patterns, not tied to any agent platform.

Official Lark (Feishu) approval skill: list pending, handled and submitted approvals; approve, reject, transfer, return, remind or withdraw; and find and submit native approvals such as leave or expense requests. Needs lark-cli with app setup and sign-in authorization.

Official Lark (Feishu) skill to create and edit Lark Sheets: formulas, charts and pivot tables, conditional formatting and filters, plus import and export of local Excel/CSV, for summaries, budgets and reports. Needs lark-cli on a machine with Node.js, plus app setup and authorization.

Official Lark (Feishu) CLI with 200+ commands for messages, docs, Base, Sheets, calendar, mail, approvals, OKRs, meetings and Minutes, plus 20+ bundled lark-* agent skills and structured output. Create a Lark app and sign in first.

The 20+ skills that ship with the official Lark (Feishu) lark-cli, covering messages, docs, Base, Sheets, calendar, mail, tasks, approvals and meeting notes. Install lark-cli and complete app setup and authorization first.

Official Lark (Feishu) skill to read, create and edit Lark docs and wiki pages, insert or download images and attachments, and work with whiteboards and mind notes. Depends on lark-cli and the lark-shared skill; requires Lark authorization.

Official Lark (Feishu) skill for Lark Base: create tables, add fields, add and edit records, build views, forms and dashboards, or apply ready-made templates, good for sign-ups, asset registers and trackers. Needs lark-cli on a machine with Node.js, plus app setup and authorization.

Official Lark (Feishu) skill: create and assign tasks, set due dates and reminders, add subtasks, organize task lists, comment and mark tasks done, helping project managers and admins track team work. Needs lark-cli with app setup and authorization; install together with lark-shared.

Alibaba's open-source offline data sync tool: JSON job configs batch-sync between heterogeneous sources like MySQL, Oracle, PostgreSQL, Hive, HDFS and MaxCompute, run with python datax.py. Needs JDK 1.8+ and Python; download and unpack the official toolkit.

ModelScope's official training framework for LLMs and multimodal models: the swift command covers pretraining, fine-tuning, RL, inference, evaluation, quantization and deployment for 600+ text and 400+ multimodal models, with Megatron parallelism and a web UI. Usually needs a GPU.

Version datasets and models the way Git versions code: data lives in remote storage like S3 or GCS while versions are tracked in your Git repo. Also offers reproducible pipelines and local experiment tracking, rerunning only the steps affected by a change.

Writes precise prompts for a specific AI tool: identifies the target tool, extracts your intent, asks at most three questions if details are missing, then outputs a lean, paste-ready prompt. Covers dozens of tools including ChatGPT, Claude Code, Cursor, Midjourney and Sora.

Lets agents call thousands of ready-made scrapers and automation Actors from Apify Store to pull data from social media, search engines, maps, e-commerce sites and more. Maintained by Apify; needs an Apify account (OAuth) or API token.

Bundles a whole web page with its CSS, images, fonts and scripts into a single HTML file that opens offline exactly as it looked, good for archiving pages or handing them to an agent for offline analysis. It does not run JavaScript, so pre-render dynamic pages with a headless browser.

Open-source zero-shot TTS via f5-tts_infer-cli: from a few seconds of reference audio it reads Chinese or English text in the same voice, handy for lessons and videos. Needs Python, PyTorch and FFmpeg, ideally a GPU. Pretrained models are non-commercial; get consent before cloning a voice.

Turns 18 kinds of sources, including doc sites, GitHub repos, PDFs and videos, into skill packages for Claude and other platforms, or exports RAG chunks and vector store data. Optional MCP server, and it can call a local agent like Claude Code to enrich content. Needs Python 3.10+.

Nearly 100 skills spanning the AI research workflow: ideation and literature review, fine-tuning and post-training, distributed training, inference serving, evaluation, interpretability and paper writing, tied together by an autoresearch orchestration skill.