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Found 984 Skills
Turn any concept, idea, or description into a polished static HTML visual, then export it as a PNG or SVG image. Use this skill whenever the user wants to create a visual representation of an idea and needs an image file output (PNG or SVG). This includes: infographics, concept diagrams, flowcharts, comparison charts, process visuals, educational diagrams, social media graphics, data visualizations, posters, cards, badges, icons, logos sketches, or any "make me an image of X" request that can be achieved with HTML/CSS/SVG rather than photographic AI generation. Also trigger when the user has an existing HTML visual and wants to export/convert it to PNG or SVG. Trigger phrases include: "create an image of", "make a visual", "design a graphic", "export as PNG", "save as SVG", "concept to image", "turn this into an image", "screenshot this HTML", "generate an infographic", or any request combining a concept description with image output.
Commit changes to git with a descriptive conventional commit message. Use when ready to commit staged or unstaged changes.
When the user wants to analyze Google Search Console data, use GSC API, or interpret search performance. Also use when the user mentions "GSC," "Search Console," "indexing report," "Core Web Vitals," "Enhancements," "Insights report," "search performance," "search queries," "search performance report," "URL inspection," "impressions," "CTR," "average position," "index coverage," "title tag," "meta description," "GSC data analysis," "Search Console API," or "searchanalytics.query."
This skill should be used when the user asks to "analyze skill quality", "evaluate this skill", "review skill quality", "check my skill", or "generate quality report". Evaluates local skills across description quality, content organization, writing style, and structural integrity.
Write, debug, and optimize Triton and Gluon GPU kernels using local source code, tutorials, and kernel references. Use when the user mentions Triton, Gluon, tl.load, tl.store, tl.dot, triton.jit, gluon.jit, wgmma, tcgen05, TMA, tensor descriptor, persistent kernel, warp specialization, fused attention, matmul kernel, kernel fusion, tl.program_id, triton autotune, MXFP, FP8, FP4, block-scaled matmul, SwiGLU, top-k, or asks about writing GPU kernels in Python.
Surgically updates a specific section of .marrow.md without re-running the full extraction. Accepts a new value, color, image, or description and patches only the relevant section — leaving everything else intact. Use this skill when the user wants to update one part of the extracted soul, change the brand accent color, update spacing rules, replace a typeface, refine the brand personality, or extract color from a new reference image. Triggers on: /marrow-update, or prompts like "update the accent color to X", "change the brand color", "update spacing in marrow", "the font changed to X", "update marrow with this new color", "patch marrow", "update just the accent", "marrow-update accent #FF6B6B", "update the palette from this image". Requires .marrow.md to exist. If not found, instructs user to run /marrow first.
Use this skill whenever the user wants to generate sound effects, ambient audio, or short audio clips from a text description. Triggers include: any mention of 'sound effect', 'sfx', 'generate sound', 'make a sound', 'audio effect', 'ambient sound', 'foley', 'sound clip', 'noise', or requests to produce a specific sound (e.g. 'make a gunshot sound', 'generate thunder', 'create the sound of rain'). Also use when the user describes an action or scenario and wants the corresponding audio (e.g. 'someone getting spanked', 'a door slamming', 'cartoon boing'). Do NOT use for speech synthesis, music generation with melody/lyrics, or voice cloning.
Issue Workflow Stage 1 — Convert the user's problem into a reproducible, traceable {slug}-report.md through conversation. The AI only asks "what you saw, how to reproduce it, what should happen" here, and does not guess the root cause for the user (that's Stage 2's responsibility). Meanwhile, this stage is the only official decision point for choosing between the fast track and standard path: Based on the user's description, first review the relevant code; if the root cause can be identified at a glance and the required changes are minor, directly inform the user to take the fast track. Trigger scenarios: The user says "file an issue", "record this bug", "I found a problem". This is the starting point of the issue workflow with no pre-dependencies.
Reviews spec documents for text quality issues without knowledge of the feature description. Fixes errors directly. Invoked by spec-writer, not directly.
Full task lifecycle: create → assign → monitor → review → reject/complete. Use when asked to "add a feature", "fix a bug", "create a task", "加个功能", "修个 bug", or "/ak-task <description>".
Search the web with fastCRW and get titles, URLs, and descriptions. Use when you have a question or topic but not a URL — "search for", "find pages about", "look up", "what is", "who is", "latest news on", "find docs for". Own search backend: self-hosted, no API key, no per-query cost, high recall via meta-search aggregation. Step 1 of the crw workflow ladder.
Supplies the craft of writing good RHDH Jira work in RHIDP, RHDHPLAN, RHDHBUGS, and RHDHSUPP: the Feature, Epic, Story, Task, and Bug description templates with filled examples, the RHDH challenge matrix that stress-tests scope, sizing, acceptance criteria, and Epic independence, the T-shirt and Fibonacci sizing scales, keyword duplicate detection, and the tracer-bullet rules for breaking a Feature into Epics or an Epic into Stories. Use when drafting or judging the text and shape of an issue such as RHIDP-1234 — how big is this, is this AC testable, should this be one Epic or three, does this already exist. Prose and estimation craft only; it runs no Jira command.