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Found 1,285 Skills
Detect and extract hidden data embedded in images, audio, and other media files using steganalysis tools to uncover covert communication channels.
Observe the user's screen via screenpipe, detect repeated research workflows, match them against existing academic-skills, and draft new skills (or composition recipes that chain existing ones) for the patterns not yet covered. Use when the user asks to analyze their recent work and propose skills based on what they actually do. Requires the screenpipe daemon (https://github.com/screenpipe/screenpipe) running locally on port 3030 — the skill has no other data source and will refuse to run if screenpipe is unreachable. All detection runs locally; only redacted cluster summaries reach the LLM.
Use when creating or revising model PR optimization history documents for SGLang, vLLM, or another serving framework that cite GitHub PRs. Requires manual, per-PR source-diff review and documentation of motivation, key implementation approach, most important code excerpts, reviewed files, and validation implications instead of generated or one-line summaries.
Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a distributed cuPyNumeric array, read a large HDF5 dataset in chunks, hand arrays to an HPC pipeline as a single file, or accelerate HDF5 disk I/O with GPUDirect Storage (GDS). Do not use it for Parquet/cuDF/raw-binary or other sharded/custom layouts (see the cupynumeric-parallel-data-load skill), Zarr or object-store/S3 output, .npz or pickled archives, plain h5py without cuPyNumeric, or pure array compute such as FFT, matmul, or reductions.
Analyze a completed Meticulous test run — fetch the diff summary, inspect representative screenshots, DOM diffs, and timelines. Accepts a test-run ID, a PR number (resolved against the local repo), or otherwise identifies the test-run ID from the local repo's current branch and its associated PR. Use when asked to review Meticulous test results, or while reviewing or babysitting a PR to assess and fix a failing Meticulous Tests CI check.
Help users negotiate job offers and compensation. Use when someone is negotiating salary, equity, or other terms of a job offer, preparing for a compensation conversation, or asking how to ask for more money.
Reviews and improves Claude Code skills against official best practices. Supports three modes - self-review (validate your own skills), external review (evaluate others' skills), and auto-PR (fork, improve, submit). Use when checking skill quality, reviewing skill repositories, or contributing improvements to open-source skills.
This skill should be used when users need to interact with GitHub via the gh CLI. It covers repository management (create, delete, clone, fork), CI/CD workflows (GitHub Actions), Issues, Pull Requests, Releases, and other GitHub operations. Triggers on requests mentioning GitHub, repos, PRs, issues, actions, or workflows.
Guidance for recovering PyTorch model architectures from state dictionaries, retraining specific layers, and saving models in TorchScript format. This skill should be used when tasks involve reconstructing model architectures from saved weights, fine-tuning specific layers while freezing others, or converting models to TorchScript format.
Download videos from YouTube and 1000+ other sites using yt-dlp
Provides unified configuration and log storage services for other skills, supporting data sharing and collaboration between skills
Implement OAuth 2.0 social login with Google, GitHub, and other providers. Handles token exchange, user creation, and account linking.