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Continuously records your screen and audio locally, extracting on-screen text and transcribing speech, so agents can search pages, docs and meetings you saw and summarize your day. Data stays local. Needs screenpipe running: source is free for personal use, desktop app is a subscription.

Compiles Markdown chapters into an online book with a table of contents, full-text search and themes, good for manuals, lecture notes and course material; agents can edit chapter files and rebuild to preview. Maintained by the Rust team; install a prebuilt binary or via cargo.

Scripts terminal sessions in .tape files and records them to GIF, MP4 or WebM, great for CLI demos and docs; text snapshot output also enables terminal integration tests. By Charm; requires ttyd and ffmpeg.

Official MCP from the FunASR project: transcribes audio files locally with the SenseVoice model, supporting Mandarin, Cantonese, English, Japanese and Korean, with optional segment timestamps. Local inference needs no API key and audio stays on your machine. Docker image available.

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.

Define and run chains of HTTP requests in plain text files, capture variables and assert on status codes, headers and JSONPath or XPath results, for both fetching data and API testing in CI. Open source from Orange.

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.

Strips AI-sounding prose from English drafts: lists phrases to cut and patterns to avoid, then scores directness, rhythm, trust in the reader, authenticity and density, sending anything under 35/50 back for revision. Word lists and examples are English, for blogs, emails and articles.

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.

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.

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.

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.

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.

Value investing research skills that turn the methods of Buffett, Munger, Duan Yongping and Li Lu into 20+ workflows: deep stock research, filing reviews, sector screens, portfolio reviews and thesis tracking, with clear conclusions and cross-checked data. Token-heavy. Not investment advice.

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+.

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.

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.

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.

Edits text to cut AI tells using a numbered rule list: superficial -ing clauses, vague attributions, AI vocabulary, not-just-X-but-Y, rule of three, em dash and colon overuse, chatbot phrases and abstract metaphor nouns, keeping meaning and tone. From Cursor's plugins; invoke manually.

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.