3,638 skills found · Page 96 of 122
aiworkskills / aws-wechat-article-review公众号审稿｜公众号校对｜敏感词检测｜内容合规 — 公众号发布前合规审查：敏感词扫描、错别字检测、政治合规、平台规范校验，一次性输出修改清单。面向公众号编辑、自媒体作者、合规岗。触发词：「审稿」「审核」「校对」「合规」「敏感词」「错别字」「稿子检查一下」「稿子帮我看看」「稿子写完了」「文章检查一下」「检查下有没有问题」「能不能发」「发布前检查」。需要多环节串联（写+审+排+配图+发）请走 aws-wechat-article-main。
aiworkskills / aws-wechat-article-assets公众号素材｜业务资料库｜预设包｜.aws 预设包｜主题包｜品牌包｜aiworkskills.cn — 用户业务资料库与预设包管理：业务资料按产品名组织在 `.aws-article/products/{产品名}/`（介绍 .md 直挂产品根 + 配图归 `images/` 子目录含同名说明 .md），AI 与用户对话产出业务介绍内容时引导用户保存；图片入库走 `product_image_ingest.py --product <产品名> --stem <中文名>`。导入 .aws ZIP 预设包（本地文件或 `https://aiworkskills.cn/**/*.aws` URL）合并主…
aiworkskills / aws-wechat-article-topics公众号选题｜爆款标题｜热点追踪｜系列策划 — 公众号 AI 选题与标题生成，覆盖热点调研、选题策划、起标题、写摘要、系列排期。面向自媒体编辑、内容运营。触发词（**单独触发仅限对已有标题/摘要的修改**）：「改标题」「换个标题」「重起标题」「优化标题」「标题再想想」「换个标题试试」「改摘要」「重写摘要」「优化摘要」「摘要再优化下」。新做选题、起新标题、策划系列/内容日历、追热点都请走 aws-wechat-article-main；需要多环节串联（写+审+排+配图+发）也走 main。
aiworkskills / aws-wechat-sticker公众号贴图｜九宫格｜多图推送｜图片消息｜表情包 — 贴图与多图推送：从创意构思、规划图序、AI 生图到整组图审核，产出一组风格统一的图片 + 每张配文。发布走常规图文链路（拼成图文后 formatting + publish），微信原生「图片消息」需在公众号后台手动发。面向公众号运营、自媒体、IP 账号。触发词：「贴图」「多图推送」「发组图」「图片消息」「九宫格」「做一组图」「图片帖子」「发几张图」「不写正文发图」「只发图不写字」。是文章内配图/封面请走 aws-wechat-article-images；需要多环节串联（写+审+排+配图+发）请走 aws-wechat-article-mai…
firecrawl / firecrawl-interactDrive a live browser on a scraped page: click, fill forms, log in, paginate, infinite-scroll
coldteadotai / abideMake your coding agent abide by all your project rules
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,…
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 — 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 — 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 — 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 — 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 — 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 — 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
