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Found 1,405 Skills
Run Figma Plugin API scripts for canvas writes, inspections, variables, and design-system work. Prerequisite for every other Figma skill in this catalogue.
Security-first skill vetting protocol for AI agents. Use before installing any skill from the platform skill market, skillhub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns to determine whether a skill is safe to install.
Fix a known bug in the Rock RMS codebase. Guides Claude through root cause analysis, minimal correct fix, and a release-note commit message. Use when the user says "fix this bug", "bugfix", "this is broken", "debug this", describes a bug with file paths or issue numbers, or pastes an error/stack trace with intent to fix. Also use when a bug is found by another skill (e.g. /review-conversion, /check) and the user wants it fixed. Do NOT use for: finding bugs (use /check or /review-conversion), adding features, or refactoring.
Analyzes the variety and depth of assertions across .NET test suites. Use when the user asks to evaluate assertion quality, find shallow testing, identify assertion-free tests (no assertions or only trivial ones like Assert.IsNotNull), flag self-referential or tautological assertions (output equals input on identity/round-trip operations), measure assertion coverage diversity, or audit whether tests verify different facets of correctness. Produces metrics and actionable recommendations. Works with MSTest, xUnit, NUnit, TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), other anti-patterns like flakiness or duplication (use test-anti-patterns), or fixing assertions.
Translate and dub a video into another language with voice cloning and lip-sync, powered by HeyGen Video Translation. The presenter keeps their face, their voice is cloned into the target language, and lips re-sync to the new audio — viewers see the same person speaking natively. Use when: (1) localizing an existing video into one or more languages ("translate this video to Spanish", "make this in French and German", "dub this into Japanese", "I need this in 10 languages for a launch"), (2) the user has a finished video and wants the SAME presenter speaking another language (not a new presenter — that's heygen-video), (3) podcast / audio-only translation ("translate this podcast", "dub the audio but keep my video"), (4) high-stakes translations where the user wants to review/edit subtitles before final render (the proofreads workflow), (5) "translate my video", "dub this", "localize this clip", "make a multilingual version", "subtitle and dub". Returns the translated video URL (or audio file for audio-only mode), one per target language. Chain signal: if the user wants to CREATE a new video in another language (no source video exists yet), route to heygen-video and write the script in the target language — do not use heygen-translate. Use heygen-translate only when there is an existing source video to localize. NOT for: creating new videos from scratch (use heygen-video), avatar creation (use heygen-avatar), TTS-only synthesis (use heygen-video with audio-only output), or text-only translation.
Every Skool community feature, plus a local SQLite mirror, FTS, and cross-community ops no other Skool tool ships.
This skill should be used when the user asks to "create a workflow", "create a getlark test", "add an end-to-end test", "author a larkci workflow", or runs `/getlark:create-workflow`. Converts a natural-language test description (target + ordered steps; target may be a URL, API endpoint, CLI binary, script, or any other software surface) into a `getlark workflows create` invocation with an auto-generated name. Prefer `manage` when the user wants to update or archive an existing workflow, and `invoke-workflow` when they want to run one — this skill only *creates* new workflows.
Use when the user has one or more video clips and wants to add post-production on top — AI-generated cover as first frame, HTML/CSS captions synced to SRT, kinetic illustration overlays at hook moments, chapter chips, end-card CTA, or any other timed motion graphics. Most often used as the downstream of `/wjs-segmenting-video` — pick up where that skill stopped (raw cropped clip + per-clip SRT) and produce the upload-ready MP4. Backed by HyperFrames so everything compiles to ONE final encode — no cascade of re-encodes. Triggers — "加封面", "加字幕", "加动画", "加 CTA", "做后期", "post-production", "title card", "kinetic captions", "end card".
Use when the user has audio or video and wants a timestamped transcript (SRT) in the source language. Routes by source language — Chinese defaults to Volcano (豆包) ASR; other languages (Spanish, English, Portuguese, French, Italian, Japanese, Korean, etc.) use OpenAI Whisper API with word-level timestamps and self-assembled cues. Outputs SRT with punctuation-bounded cues capped for on-screen reading. Triggers — "转写", "转成字幕", "做 SRT", "transcribe", "make subtitles", "speech to text", "出字幕".
Perform static and symbolic analysis of Solidity smart contracts using Slither and Mythril to detect reentrancy, integer overflow, access control, and other vulnerability classes before deployment to Ethereum mainnet.
Hand off the current task to the SLICC browser agent, or install a new skill into SLICC from a GitHub repo. Use this skill when the user says things like "handoff to slicc", "move this to slicc", "move to the browser", "test in the browser", "handoff to browser", "install this skill in slicc", "upskill slicc with this repo", "add this skill to slicc", or otherwise asks you to continue the work inside the SLICC browser agent.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**