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Found 1,536 Skills
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.
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".
Lift a proven skill from a host repo (e.g. your OpenClaw fork) back into gbrain's bundle so other clients can scaffold it. Editorial workflow: the CLI does the file copy + privacy lint; this skill drives the judgment-heavy genericization (scrub real names, generalize triggers, lift fork-specific conventions to references).
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.**
Side-by-side comparison of ruflo vs HAL vs other GAIA harnesses — capability gaps, design decisions, and improvement roadmap
Searches for and retrieves existing visual media (images, logos, icons, photos, graphics, banners, thumbnails, hero images, backgrounds) from sources such as Salesforce CMS, Data 360 or any other source. Use this skill ANY TIME a user request involves finding, searching, getting, fetching, retrieving, grab, looking up, locating media. NEVER call search_media_cms_channels, search_electronic_media tools directly — always go through this skill first. This skill must be activated before any tool is used for media search or retrieval, without exception. Takes PRIORITY and activates FIRST when ANY media search/retrieval is mentioned, regardless of what else happens with the media afterward. Triggers for requests like "search for logo", "find hero image", "get company logo", "locate icons", "fetch background image", "retrieve product photos". Handles the search and source selection workflow. Does not apply when the request is about brand search, to generate NEW images with AI, or edit existing images.
Guided discovery of a product idea by mining what the founder already knows or already does — covers source selection (business vs. expertise), context capture, pattern synthesis, candidate scorecard, and writes `docs/product-idea.md`. Use when the founder says "generate an idea", "help me find an idea", "what should I build", "product idea from my business", "product idea from my expertise", or otherwise needs to discover a product concept worth building.
Translates an image (or a set of image references — screenshots, mockups, Figma URLs, live websites) into two mirrored design-system artifacts: `docs/design.md` (YAML tokens + prose, following Google's open [design.md](https://github.com/google-labs-code/design.md) format, for the coding agent) and `docs/design.html` (a self-contained, token-driven style guide rendering every token and component live, for the human to read). Reads the imagery, asks targeted clarifying questions, derives the design tokens (colors, typography, spacing, rounded, components), and writes both files. Fully standalone — requires no other document or skill. Use when the founder says "create a design system", "design from image", "translate image to design", "create design.md", "image to design system", "extract design tokens", or shares an image with no other clear intent.