608건 · 관련도순
Use this when performing DataFrame operations — including loading, filtering, joining, aggregating, transforming, or reshaping tabular data with polars or pandas.
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Use when the user wants to pressure-test a product, internal-tool, or OSS concept against Amazon's Working Backwards PRFAQ gauntlet before committing to a spec. Triggers on: "work backwards", "write a PRFAQ", "press release first", "is this idea worth building", "pressure-test this concept", "filter this before brainstorm", "is this a real product". Also catches solution-first pitches ("I want to build X that does Y") and technology-first pitches ("use AI to...") that need customer-first filtering. Produces a binary pass/fail verdict, not a polished doc. Hardcore coaching — direct, skeptical, concrete. On pass, hands off to brainstorm-beagle with a concept brief. Does NOT write code, plan implementation, scaffold projects, or draft specs.
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ADB-based RIPDPI device/emulator debugging: build/install/launch, logcat filtering, fixture port-forwarding, instrumented tests, crash/ANR triage. Use when debugging on a real device or emulator.
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Read and query GCP logs for Hyperlane agents using gcloud CLI. Use when investigating relayer, validator, or scraper logs, debugging message processing, or analyzing operational issues. Provides efficient filtering and context management strategies.
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Generates optimized read queries using Dapper. Includes multi-mapping for joins, pagination, dynamic filtering, CTEs, and best practices for high-performance data access.
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Use whenever you need the Datagrok browser to actually execute JavaScript — adding viewers, filtering, modifying the view, or returning a result widget to the chat. Open this skill before calling the datagrok_exec tool.
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Prisma Client API reference covering model queries, filters, operators, and client methods. Use when writing database queries, using CRUD operations, filtering data, or configuring Prisma Client. Triggers on "prisma query", "findMany", "create", "update", "delete", "$transaction".
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Birding by sight and sound — gestalt, song and call vocabulary, behavioral and habitat clues, and the discipline of producing eBird-grade records. Covers the vocabulary of bird field marks, the primary categories of vocalization, the habits of habitat-filtering, and the protocol for submitting records to a citizen-science database without degrading data quality. Use when the task is bird-specific observation or record-keeping.
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Dasel v3 selector patterns for Maven POM XML files — use when querying dependency versions, filtering by groupId or scope, extracting module hierarchy from parent POMs, or detecting version conflicts across enterprise multi-module Java projects. Load this skill when working with pom.xml files using dasel.
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REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.
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Ensure agent safety - guardrails, content filtering, monitoring, and compliance
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Reconcile one set of artifact kinds (the target) to reflect the current state of another set of artifact kinds (the source), while optionally honoring a filtering hint. Use when the user wants to "reconcile", "sync", "align", or "update" artifacts like SPEC, CODE, DOCS, TASK, INFR, or OTHR against each other.
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Patterns for handling commands, validating input, and filtering messages in XMTP agents. Use when implementing slash commands, validators, or message filters. Triggers on command handling, input validation, or type guards.
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设计列表接口的分页、筛选、排序、搜索时使用。适用于所有列表 API。优先使用 cursor 分页（大数据）或 offset 分页（小数据）+ 统一筛选/排序规范。
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Use when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking APIs, handling authentication or OAuth, testing accessibility (axe-core), file uploads/downloads, date/time mocking, WebSockets, geolocation, permissions, multi-tab/popup flows, mobile/responsive layouts, touch gestures, GraphQL, error handling, offline mode, multi-user collaboration, third-party services (payments, email verification), console error monitoring, global setup/teardown, test annotations (skip, fixme, slow), test tags (@smoke, @fast, @critical, filtering with --grep), project dependencies, security testing (XSS, CSRF, auth), performance budgets (Web Vitals, Lighthouse), iframes, component testing, canvas/WebGL, service workers/PWA, test coverage, i18n/localization, Electron apps, or browser extension testing. Covers E2E, component, API, visual, accessibility, security, Electron, and extension testing.
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Vitest fast unit testing framework powered by Vite with Jest-compatible API. Use when writing tests, mocking, configuring coverage, or working with test filtering and fixtures.
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Execute pending Claude Code Tasks in dependency order with wave-based concurrent execution and adaptive verification. Supports task group filtering and configurable parallelism. Use when user says "execute tasks", "run tasks", "start execution", "work on tasks", or wants to execute generated tasks autonomously.
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Design or improve search experiences, result presentation, and filtering interfaces. Use when the user asks to add search, redesign search results, improve findability, build autocomplete, add filters, or fix zero-results dead ends.
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Audit, classify, and selectively forget stored memories. Covers memory enumeration and classification by type/age/access frequency, staleness detection for outdated references, fidelity checks using external anchors, a decision tree for selective withdrawal, tombstoned deaccession so removed content stays recoverable, counter-memory inoculation for failed strategies that would otherwise be re-derived, preemptive filtering rules for what should never become memories, and an audit trail so forgetting itself is reviewable. Use when memory has grown large and uncurated, when project state has shifted significantly since memories were written, when retrieval quality has degraded, or as periodic maintenance alongside manage-memory.
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Purify water from wild sources using boiling, filtration, and chemical methods. Covers source assessment and priority ranking, sediment pre-filtering, method selection (boiling, chemical, UV, filter), altitude-adjusted boiling procedure, chemical treatment dosages, and safe storage practices. Use when needing drinking water in a wilderness setting, when available water sources are of unknown quality, in an emergency survival situation where dehydration is a risk, or when making water safe for cooking or wound cleaning.
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Search and download images from 15 APIs in parallel (Openverse, Pexels, Pixabay, Met Museum, Cleveland Museum, Art Institute of Chicago, Getty Museum, NASA, Rijksmuseum, Wikimedia Commons, Unsplash, Smithsonian, Europeana, Iconify, Pollinations AI). Supports license filtering, presets (military, museum, stock), cached results, and bulk download with metadata sidecars. Triggers on find images, stock photos, public domain artwork, museum images, CC-licensed, NASA photos, icons.
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Work with the @upstash/search TypeScript/JavaScript SDK, a serverless full-text and semantic search database with built-in reranking. Use when adding search to an app or site, creating a search index, upserting documents with searchable content and filterable metadata, running keyword, semantic, or hybrid search queries, reranking results, filtering with SQL-like or structured filter syntax, paginating with range, fetching or deleting documents, resetting an index, or checking index info. Also use when the user asks for site search, product, document, or knowledge-base search, or a managed search service that needs no cluster to run.
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Work with the @upstash/vector TypeScript/JavaScript SDK, a serverless vector database for embeddings, similarity search, semantic search, and RAG (retrieval-augmented generation). Use when upserting, querying, fetching, ranging, or deleting vectors, upserting raw text against an index with a built-in embedding model, choosing dense, sparse, or hybrid indexes, filtering by metadata, organizing data with namespaces, running resumable queries, or connecting Upstash Vector to an AI or LLM application. Also use when the user asks for a vector store, vector search, nearest-neighbor or kNN search, embeddings storage, semantic cache, recommendations or similarity features, or a hosted vector index that needs no infrastructure.
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Stock Screener — pre-built screens (growth, value, momentum, dividend, earnings beats) plus custom natural language criteria. Returns top 10-20 matches ranked by Screen Fit Score (0-100). Triggered by "trade screen growth/value/momentum/dividend/earnings" or "trade screen custom <criteria>".
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