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Walk the user through four directional axes (tone register, aesthetic philosophy, audience relationship, sensory ambition) and produce a structured aesthetic brief that downstream content, copy, design, and art-direction skills consume as required input. The aesthetic depth layer, distinct from `creative-brief` (operational kickoff: scope, audience, deliverables, constraints). Use when a project needs aesthetic coherence across many small decisions and the user has only a vague feeling, or when multiple downstream aesthetic-producing skills need a shared brief. Triggers on creative direction, aesthetic direction, set the aesthetic, define the visual direction, what's the vibe, what's the tone, the four axes. Does NOT fire for a general kickoff brief (use `creative-brief`), tactical single-piece work, already-documented direction, purely functional output, or locked production-stage work.

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Reference for the `odu` runner — how to invoke a full pipeline, a single recipe, or a platform-pinned node, and how to attach to a live run, from a project whose CI odu runs. Trigger when the user asks to "run CI", "run the pipeline", "re-run a check", to run named lanes or recipes (e.g. "run fmt and nix", "just the e2e lane", bare selectors like `fmt`/`nix`/`e2e`), or names a recipe by `<recipe>@<platform>`. This skill — not a repo's local `just ci` / `just <recipe>` — is how an odu-run request is served.

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chrome-devtools-mcp launcher — a single bash script that resolves Playwright's bundled Chrome-for-Testing and Node.js directly through Nix. See repo README for usage, override mechanisms, and version compatibility.

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Sync every enabled repo in the curated skill index and open a confirmation-gated PR. Use when refreshing already-indexed sources. Don't use for adding new repos, improving a single skill, or installing skills locally.

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A comprehensive stock deep analysis tool that combines real-time quotes, fundamental metrics, technical indicators, and growth analysis into a single professional report. Supports A-share, US stocks, HK stocks. Generates detailed investment recommendations with risk assessment and actionable trading strategies.

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Trace bugs and manual fixes back to kits and prompts; fix at the source so the iteration loop can reproduce the fix autonomously. Six-step revision process plus the single-failure backpropagation protocol. Use when a manual hot-fix has been applied, when convergence stalls, or when the same class of bug keeps reappearing.

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Annotated data matrix operations for single-cell genomics using AnnData

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Access and analyze the CZ CELLxGENE Census single-cell data

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Discovers user intent and generates a structured, step-by-step plan for model customization workflows. This skill must always be activated alongside any other skill when the user's request relates to model customization — including fine-tuning, training, building, customizing, reviewing data, or getting advice on approach, regardless of domain. Do not skip this skill even if the immediate ask is narrow (e.g., reviewing data format or a single workflow step), because planning discovers the full scope of work needed. Also activate when the user wants to resume, continue, or modify an existing plan.

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Diagnose NCCL failures and adjacent training-pod failures on HyperPod GPU clusters (EKS or Slurm) — training hangs, AllReduce / collective-op timeouts, EFA or libfabric errors, rendezvous failures, EFA TCP fallback, /dev/shm or memlock issues, NCCL version mismatch across pods, container OOM / exit-137 / OOMKilled, GPU OOM (CUDA out of memory), CrashLoopBackOff / Pending pods, MASTER_ADDR DNS, NetworkPolicy blocking. Not for single-node hardware faults (→ hyperpod-node-debugger § G) or cluster-creation EFA / SSM failures (→ hyperpod-cluster-debugger § A / § F).

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This skill should be used when the user asks to "analyze this codebase", "document this service", "generate technical docs", "I inherited this code", "help me understand this system", "create docs for this project", "what does this system look like", "onboard me to this codebase", "this codebase has no docs", "visualize the architecture from code", or any explicit request to produce structured documentation or architecture diagrams from an existing codebase. Specifically optimized for AWS workloads (CDK, CloudFormation, Terraform) with source-of-truth citations. Do NOT activate for code reviews, single-function explanations, generating new code, or general coding tasks.

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Deploy to AWS Elastic Beanstalk, in either Beanstalk Standard mode (EC2) or Beanstalk Cluster mode (managed EKS). Triggers on: elastic beanstalk, EB, beanstalk standard, beanstalk cluster, managed EC2 platform, web app with managed patching, worker on EC2, Heroku alternative, don't want to manage servers or container orchestration, migrate from Heroku, managed operational lifecycle, containerised app without operating Kubernetes, several services sharing a managed cluster, managed EKS without kubectl. Covers web and worker applications on EC2, and containerised single- and multi-service applications on managed EKS.

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Audit technical SEO site-wide by script: crawl, indexation, Web Vitals. A single page → page-seo-analysis. "why isn't Google indexing my pages"

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Run a site-wide SEO audit into a ranked PLAN.md: technical, content, links, local. A single page → page-seo-analysis. "why did our traffic drop"

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Discover and recommend **combinations** of agent skills to complete complex, multi-faceted tasks. Provides two recommendation strategies — **Maximum Quality** (best skill per subtask) and **Minimum Dependencies** (fewest installs). Use this skill whenever the user wants to find skills, asks "how do I do X", "find a skill for X", or describes a task that likely requires multiple capabilities working together. Also use when the user mentions composing workflows, building pipelines, or needs help across several domains at once — even if they only say "find me a skill". This skill supersedes simple single-skill search by decomposing the task into subtasks and assembling an optimal skill portfolio.

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Run fable-mode execution discipline on Claude Fable 5.1 — the top of the escalation ladder and the strongest staged run available. Routes the task to the @fable-orchestrator agent with its model overridden to Fable (`model: "fable"`), which stages the work, delegates artifact production to @fable-worker-sonnet / @fable-worker-haiku, and cold-checks deliverables with @fable-verifier. Trigger when the user explicitly asks for thorough/systematic/"deep work" handling on the strongest model ("fable on fable", "stage this on fable", "deep work mode, fable", "run this on 5.1"), or when a fable-opus run reports the task exceeds Opus's ceiling. Benchmarked 2026-09-02: zero correctness delta on Fable 5.1 at ~1.1× tokens, ~2× wall — a provenance runner, not an accuracy runner. Do NOT use for ordinary single-pass tasks — prefer fable-opus, fable-sonnet, or fable-haiku when the task doesn't need peak reasoning. Fable is plan-included; bulk work still routes down-tier.

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Make one primary CTA feel premium: a soft band of light sweeps across it (slowly at rest, faster on hover). Reserve for the single primary button; keep it subtle. Pure CSS; the sweep stops (solid button) under prefers-reduced-motion. Do not use on secondary/destructive buttons or many at once.

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A soft light rotates around a pill/button border — a gentle animated outline for a single CTA. One per view; keep it slow and low-contrast. Pure CSS; static border under prefers-reduced-motion.

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Give one card or CTA a lively accent: a small bright comet of light travels around its border. Brighter/livelier than shine-border — use for a single "new"/"featured" element, one per view. Pure CSS; fully static under prefers-reduced-motion.

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Stagger a headline's words (or letters) in on load, one after another. Use for a single hero headline or a title card in a launch video. Honors prefers-reduced-motion. One per screen; never use on body copy or must-read content.

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A slider with a springy fill and knob — playful, tactile input. Use for a single prominent control (volume, brightness). Under prefers-reduced-motion it moves without the spring.

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Act as experienced opposing counsel to attack, undermine, and expose weaknesses in a legal argument, submission, witness statement, or structured reasoning. Produces a six-part adversarial analysis: 1. A core theory of attack identifying the single most effective way to defeat the argument; 2. A reconstructed version of the opposing argument stripped of rhetoric to expose its fragility; 3. Primary lines of attack grouped by category (legal misstatement, evidential gaps, causation failures, internal inconsistency, over-reliance on assertion, procedural weakness); 4. An "if I were the judge" section showing how a sceptical tribunal would dismantle the argument; 5. Surgical strikes - 3 to 5 high-impact points ready for oral submissions; and 6. An analysis of what the argument is trying to hide. Written in formal, adversarial British English for a legally trained audience.

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AI-assisted generation of MCQs (multiple-choice questionnaires) with export to Moodle (GIFT, XML), Wooclap (Excel), Kahoot!, and Word. Supports light MCQs (gamified) and in-depth MCQs (evaluative), with single-answer and multiple-answer formats.

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Restructure any rough build, research, or legal-drafting request into an IRAC-shaped prompt — Issue, Rule, Analysis, Conclusion — optimized for a frontier model. It's the bar-exam framework, repurposed as prompt engineering. The skill leads with the issue and ends with the conclusion (where models weight attention most), forces you to name your constraints and non-goals, and specifies what "good" looks like before a single token is generated. Use it before any non-trivial build, or whenever a vague ask deserves a precise brief.

日本語の概要は準備中です。原文の説明を表示しています。