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Found 944 Skills
Guide for theming .NET MAUI apps — light/dark mode via AppThemeBinding, ResourceDictionary theme switching, DynamicResource bindings, system theme detection, and user theme preferences. Use when: "dark mode", "light mode", "theming", "AppThemeBinding", "theme switching", "ResourceDictionary theme", "dynamic resources", "system theme detection", "color scheme", "app theme", "DynamicResource". Do not use for: localization or language switching (see .NET MAUI localization documentation), accessibility visual adjustments (see .NET MAUI accessibility documentation), app icons or splash screens (see .NET MAUI app icons documentation), or Bootstrap-style class theming (see Plugin.Maui.BootstrapTheme NuGet package).
Process and manage corporate actions from announcement through settlement. Use when handling dividends stock splits reverse splits mergers or spin-offs, managing voluntary elections for tender offers rights offerings or exchange offers, calculating record date and ex-date entitlements under current settlement cycles, building client notification workflows for upcoming corporate actions, collecting and submitting voluntary action elections to DTC or custodians, calculating fractional share handling or proration for reorganization events, adjusting cost basis and tax lots after corporate actions, reconciling expected entitlements against actual receipts, investigating missed or incorrectly processed corporate actions, or designing corporate action processing systems and controls.
This skill should be used when the user asks for markup detection, detect manipulation, image tampering, deepfake detection, document integrity, hidden markup, metadata forensics, EXIF analysis, content authenticity, synthetic media, altered image, C2PA, or provenance verification across documents, images, and video. Guides workflow-level assessment of visual tampering indicators (splicing, cloning, inconsistent lighting or shadows, compression artifacts), metadata and provenance checks (EXIF, hashes, source chain), document revision and hidden markup (tracked changes, comments, invisible text), synthetic-media and deepfake red flags, watermarking and content-credentials concepts, and structured reporting with confidence levels and explicit limitations—not training detection models (ml-research-engineer-safeguards), cryptographic watermark design (cryptographer-specialist), full digital forensics lab attribution or legal conclusions, or blockchain-only tracing unless the user scopes on-chain context.
Comprehensive PDF manipulation toolkit for extracting text and tables, creating new PDFs, merging/splitting documents, and handling forms. When Claude needs to fill in a PDF form or programmatically process, generate, or analyze PDF documents at scale.
Interact with Dot. devices through the OpenAPI - control text/image display, query device status, and manage devices.
Use this skill whenever building, reviewing, or refactoring React components that fetch data from APIs — especially at scale (recommender carousels, infinite feeds, pages with many parallel fetches, dashboards). Covers request orchestration (parallelism, batching, deduplication), cache strategy (keys, normalization, staleTime, SWR), backend protection (concurrency caps, debounce/throttle, jittered retries, circuit breakers), prefetching (route loaders, hover/intent, idle, server hydration), failure resilience (AbortController, timeouts, error boundaries, stale fallback, idempotent mutations), and feed/carousel patterns (virtualization, cursor pagination, summary/detail split). Trigger even if the user doesn't explicitly mention "performance" or "scale" — any non-trivial React data-fetching code benefits from these patterns. Includes 5 ready-to-use scaffolding templates (resource query hook, carousel data loader, infinite feed, hover-prefetch link, request collapser).
Deep reference for the Sui object model: ownership types, object abilities, dynamic fields, collections, versioning, transfer patterns, and derived objects. Use this skill whenever the user asks about Sui objects, object ownership (address-owned, shared, immutable, wrapped), how to transfer or share or freeze objects, dynamic fields vs dynamic object fields, Table vs Bag vs VecMap, object versioning, wrapping and unwrapping, the Receiving type, custom transfer rules, hot potato pattern, capability pattern, object deletion, Object Display, or how to model data (inventories, registries, nested items) in Sui Move. Also use when the user needs to choose between ownership types or storage patterns for their use case.
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.**
SpriteRendererComponent.SpriteRUID (world) and SpriteGUIRendererComponent.ImageRUID (UI) — native RUID type support (sprite / animationclip direct playback), thumbnail:// prefix for rendering avataritem / skeleton / animationclip as static thumbnail image, avatar item icon in inventory / shop / UI slot. Use when: assigning any RUID to a sprite renderer component, displaying an avatar item or resource as a thumbnail or icon, using animationclip directly in a renderer, rendering inventory item icons, displaying a thumbnail image in a world entity. Keywords: SpriteRUID, ImageRUID, thumbnail://, animationclip, RUID apply, RUID assign, thumbnail, item icon, sprite RUID, RUID to renderer
Adversarial due-diligence on a benchmark you envy — a founder, KOL, company, or product whose claimed success you suspect is inflated. Inline four-phase orchestration — fan-out collection, adversarial verification grading every claim L1-L4 to split marketing bubble from real signal, attribution weighting (product vs timing vs IP vs luck, what's replicable), then mapping the validated playbook onto the user's own resources. Use whenever the user wants to 尽调/对标/拆解 a competitor or role-model, 抄/偷师 someone's playbook, suspects 水分/泡沫 in their claims (Product Hunt
A shared, file-based town square where multiple coding agents talk, coordinate, and debate — no server required. Use whenever more than one agent works the same repo (parallel Claude Code or Codex sessions, separate git worktrees, a fleet splitting a task) and they must stay out of each other's way or think together. TRIGGER on phrasings like "coordinate with the other agent/session", "post to / check the agora", "ask the other agents", "leave a message for whoever's working on X", "announce what files you're touching", "is anyone else editing this?", or any time you're about to edit shared code while other agents are live. Also trigger when an agent is stuck and wants a peer's second opinion, or when several agents each drafted a design (an API, a schema, an architecture) and the group needs to compare the proposals and converge on the best one. Works for any agent that can run a Python script, not just Claude Code.
Focused pattern for fetching data using URL parameters in Next.js. Covers creating dynamic routes ([id], [slug]) and accessing route parameters in server components to fetch data from APIs. Use when building pages that display individual items (product pages, blog posts, user profiles) based on a URL parameter. Complements nextjs-dynamic-routes-params with a simplified, common-case pattern.