Tools

Offline speech transcription and translation based on Whisper; audio never leaves your machine. buzz add exports lectures, interviews and meeting recordings to TXT, SRT or VTT. pip install needs Python 3.12 and FFmpeg; a desktop app is also available. Check transcripts manually.

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.

Unofficial Python library and CLI for driving Google NotebookLM (now Gemini Notebook): bulk-import sources, ask cited questions, and generate and download podcasts, videos, slides, quizzes and flashcards. Ships an agent skill. Relies on undocumented APIs and needs a Google sign-in.

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.

Style and convention linter for Swift, with most rules built on SwiftSyntax. Configure it with .swiftlint.yml, auto-correct with --fix, and run it in an Xcode build phase or as a SwiftPM plugin.

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.

Write your resume in YAML and render a cleanly typeset PDF from the command line: multiple themes, fine control of margins, colors and fonts, strict field validation and multi-language support. A companion agent skill is available. Needs Python 3.12+.

Automattic's offline English grammar checker: checks spelling, grammar and word choice locally in milliseconds without sending your text anywhere. Homebrew installs harper-cli for batch-checking files (still experimental) and harper-ls for editors. English only.

Turns an Obsidian vault into a local-first knowledge base: imports sources into interlinked notes with citations, answers questions from what is already in the vault, and adds 15 skills including search, vault health checks and Canvas knowledge graphs.

Write end-to-end UI tests for Android, iOS and web apps in YAML. The CLI includes an MCP server (maestro mcp) so coding agents can tap, assert and generate tests on simulators. Requires Java 17+.

MCP built into the CLI of the Maestro mobile UI testing framework (maestro mcp): lets agents view the screen, tap, scroll and assert on iOS/Android simulators, Android devices or Chromium, and write and run YAML end-to-end flows. Needs the Maestro CLI and Java 17+. Official.

Simplifies Figma designs into layout and style data for coding agents, so UIs built from the design are more accurate than working from screenshots. Requires a Figma personal access token.

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.

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+.

Codex-native version of Academic Research Skills: one skill chains topic selection and literature review, drafting, simulated peer review, revision guidance and experiment planning, with citation verification and integrity checks. For grad students and faculty. Needs Codex; CC BY-NC 4.0 license.

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.

Plugin marketplace for AI research with nearly 100 skills in 23 categories, from ideation, fine-tuning and post-training to distributed training, inference serving, evaluation and paper writing, coordinated end to end by the autoresearch skill.