3,611 skills found ¡ Page 96 of 121
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aiworkskills / aws-wechat-article-topicså ŦäŧåˇééĸīŊįæŦžæ éĸīŊįįščŋŊč¸ĒīŊįŗģåįå â å Ŧäŧåˇ AI ééĸä¸æ éĸįæīŧčĻįįįšč°į ãééĸįåãčĩˇæ éĸãåæčĻãįŗģåææãéĸåčĒåĒäŊįŧčžãå 厚čŋčĨãč§Ļåč¯īŧ**åįŦč§Ļåäģ éå¯šåˇ˛ææ éĸ/æčĻįäŋŽæš**īŧīŧãæšæ éĸããæĸä¸Ēæ éĸããéčĩˇæ éĸããäŧåæ éĸããæ éĸåæŗæŗããæĸä¸Ēæ éĸč¯č¯ããæšæčĻããéåæčĻããäŧåæčĻããæčĻåäŧåä¸ããæ°åééĸãčĩˇæ°æ éĸãįåįŗģå/å 厚æĨåãčŋŊįįšéŊč¯ˇčĩ° aws-wechat-article-mainīŧéčĻå¤į¯č串čīŧå+åŽĄ+æ+é åž+åīŧäščĩ° mainã
aiworkskills / aws-wechat-stickerå Ŧäŧåˇč´´åžīŊäšåŽĢæ ŧīŊå¤åžæ¨éīŊåžįæļæ¯īŊ襨æ å â č´´åžä¸å¤åžæ¨éīŧäģåæææãč§ååžåēãAI įåžå°æ´įģåžåŽĄæ ¸īŧäē§åēä¸įģéŖæ ŧįģä¸įåžį + æ¯åŧ é æãåå¸čĩ°å¸¸č§åžæéžčˇ¯īŧæŧæåžæå formatting + publishīŧīŧ垎äŋĄåįãåžįæļæ¯ãéå¨å Ŧäŧåˇåå°æå¨åãéĸåå ŦäŧåˇčŋčĨãčĒåĒäŊãIP č´Ļåˇãč§Ļåč¯īŧãč´´åžããå¤åžæ¨éããåįģåžããåžįæļæ¯ããäšåŽĢæ ŧããåä¸įģåžããåžįå¸åããåå åŧ åžããä¸åæŖæååžããåĒååžä¸ååããæ¯æįĢ å é åž/å°éĸč¯ˇčĩ° aws-wechat-article-imagesīŧéčĻå¤į¯č串čīŧå+åŽĄ+æ+é åž+åīŧč¯ˇčĩ° aws-wechat-article-maiâĻ
firecrawl / firecrawl-interactDrive a live browser on a scraped page: click, fill forms, log in, paginate, infinite-scroll
nWave-ai / nw-bdd-requirementsBDD requirements discovery methodology - Example Mapping, Three Amigos, conversational patterns, Given-When-Then translation, and collaborative specification
nWave-ai / nw-rigorSelects a quality-vs-token-consumption profile (lean, standard, thorough, exhaustive, custom, inherit) and persists it globally (~/.nwave/global-config.json) or per-project (.nwave/des-config.json)
nWave-ai / nw-tdd-methodologyDeep knowledge for Outside-In TDD - double-loop architecture, ATDD integration, port-to-port testing, walking skeletons, and test doubles policy
nWave-ai / nw-test-design-mandatesDesign mandates for acceptance tests - hexagonal boundary, business language abstraction, user journey completeness, pure function extraction, 3 Pillars (domain language / chained narrative / production composition), and the layered ATD discipline (Universe-bound assertion, layer-dependent PBT mode,âĻ
nWave-ai / nw-test-optimizationMethodology for minimizing test count while maximizing behavioral coverage - behavior definition, anti-pattern catalog, consolidation patterns, stopping criterion, coverage-preserving validation
nWave-ai / nw-jtbd-bdd-integrationTranslating JTBD analysis to BDD scenarios - job story to Given-When-Then patterns, forces-based test discovery, job-map-based test discovery, and property-shaped criteria
nWave-ai / nw-jtbd-coreCore JTBD theory and job story format - job dimensions, job story template, job stories vs user stories, 8-step universal job map, outcome statements, and forces of progress
nWave-ai / nw-speculative-dispatchSpeculative parallel implementation methodology â dispatch N candidate implementations, audit all, score, pick best. Auditability mandate: ALL candidates logged (not just winner).
nWave-ai / nw-spikeRuns a timeboxed PROBE to validate one core assumption, then optionally PROMOTES the probe into a walking skeleton â the first e2e thin slice of the feature, committed and demo-able. Use after DISCUSS when the feature involves a new mechanism, performance requirement, or external integration.
nWave-ai / nw-test-organization-conventionsTest directory structure patterns by architecture style, language conventions, naming rules, and fixture placement. Decision tree for selecting test organization strategy.
nWave-ai / nw-ddd-strategicStrategic DDD â bounded context discovery, context mapping patterns, subdomain classification, ubiquitous language, and organizational alignment
nWave-ai / nw-discussConducts Jobs-to-be-Done analysis, UX journey design, and requirements gathering through interactive discovery
nWave-ai / nw-formal-verification-tlaplusTLA+ and PlusCal for specifying distributed system invariants. Decision heuristics for when formal verification adds value, key patterns, state explosion management, and alternatives comparison.
nWave-ai / nw-jtbd-opportunity-scoringJTBD opportunity scoring and prioritization - outcome statement format, opportunity algorithm, scoring interpretation, feature prioritization, and opportunity matrix template
nWave-ai / nw-outcome-kpi-frameworkOutcome KPI definition methodology - synthesizes Who Does What By How Much (Gothelf/Seiden), Running Lean (Maurya), and Measure What Matters (Doerr) into a practical framework for measurable outcome KPIs
nWave-ai / nw-sd-patterns-advancedAdvanced distributed patterns - event sourcing, CQRS, saga, stream processing, append-only log, exactly-once delivery, sequencer, double-entry ledger, erasure coding, order book, watermarks
nWave-ai / nw-taste-evaluationDesign taste evaluation framework â DVF primary filter, Apple/Google/Jobs design principles as explicit scoring criteria, weighted decision matrix, and option ranking for the DIVERGE wave
nWave-ai / nw-designDesigns system architecture with C4 diagrams and technology selection. Routes to the right architect based on design scope (system, domain, application, or full stack). Two interaction modes: guide (collaborative Q&A) or propose (architect presents options with trade-offs).
nWave-ai / nw-sc-review-dimensionsReviewer critique dimensions for peer review - implementation bias detection, test quality validation, completeness checks, and priority validation
nWave-ai / nw-buddy-wave-knowledgeWave methodology knowledge for the buddy agent â what each wave does, its inputs and outputs, and how to route questions.
nWave-ai / nw-ddd-eventsourcingEvent Sourcing and CQRS as DDD implementation patterns â when to use, aggregate event streams, projections, snapshots, sagas, upcasting, conflict resolution
nWave-ai / nw-discoverConducts evidence-based product discovery through customer interviews and assumption testing. Use at project start to validate problem-solution fit.
nWave-ai / nw-fp-clojureClojure language-specific patterns, data-first modeling, REPL-driven development, and spec
nWave-ai / nw-fp-haskellHaskell language-specific patterns, GADTs, type classes, and effect systems
nWave-ai / nw-fp-hexagonal-architectureHexagonal architecture patterns with pure core and side-effect shell for functional codebases
