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Found 3,019 Skills
Designing first-run product onboarding wizards that get users to the ah-ha moment without overwhelming them. Step architecture, progressive disclosure, escape hatches, completion incentives, drop-off measurement. Honest about tutorial-overload (dump everything upfront), skip-friendly-empty (skipped onboarding leads to abandoned product), and earned-progressive-disclosure (right things at the right moments) patterns. Triggers on onboarding wizard, product onboarding, first-run experience, signup flow, activation flow, FRX, time-to-value, ah-ha moment design. Also triggers when activation rates are low, when users skip onboarding and never return, when onboarding flows are being scoped for the first time, or when audience research shows users not finding key features.
Apply Anthropic's official brand colors and typography to artifacts for consistent visual identity and professional design standards. A reference for shaping your own.
面向电商产品Listing的文字商标检测与侵权风险分析。当用户提到商标检测、商标风险检查、品牌侵权筛查、产品标题商标扫描、文字商标查询、Listing合规检查、知识产权风险评估、text trademark detection, trademark infringement, brand infringement screening, listing compliance, intellectual property risk, Ruiguan时触发此技能。即使用户未明确说"商标"，只要其需求涉及检查产品文本（标题、描述、五点描述）中是否包含可能侵权的商标，也应触发此技能。
Intelligent Bid Document Writing Expert, specializing in preparing bid documents in the fields of engineering consulting, architectural design, and municipal engineering. This skill is activated when users mention terms such as: bid document, tender, bidding, bid proposal, technical bid, commercial bid, bid response, bidding plan, bid document, bid proposal, tender, proposal writing, bid document preparation, bid writing, tender document analysis, scoring standard optimization.
Augment a Wren project with business context that DB schema cannot carry — enum value meanings, units (USD vs cents, ms vs sec), NULL semantics, magic sentinels (-1 = unknown), soft-delete default filters, business synonyms, time-grain / TZ conventions, cross-system identifiers, currency rules, canonical-table preferences, AND named aggregation metrics (ARR, churn, DAU, WAU, NRR) proposed as cubes. Runs in one of two modes selected at session start: `grill` (one question at a time, user-driven) or `auto-pilot` (agent infers and applies, escalates only on conflicts and high-blast-radius additions like new cubes / views / relationships). Reads everything under <project>/raw/ (PDFs, glossaries, handbooks, code, data dictionaries) and optionally samples low-cardinality columns from the live DB (grill mode), compares against the current MDL / cubes / instructions.md / queries.yml / memory pairs, then fills gaps via the ten-category gap catalog and the cube proposal flow. Confirmed findings are written back to the right sink. Use when: user says 'enrich context', 'augment my project', 'grill me on this project', 'auto-fill my context', 'agent doesn't understand our docs / enum values / units / null meanings', 'business context is missing', 'what does status=A mean', 'is this amount in USD or cents', 'we keep getting wrong aggregations', 'add cubes for ARR / DAU / churn', 'we have a handbook / glossary / data dictionary the agent should know'; or after generating an MDL and noticing the agent lacks business semantics.
Ingest video, audio, PDF, book, screenshot, and GitHub repo content into the brain. Multi-format handling with entity extraction and backlink propagation. Covers video-ingest, youtube-ingest, and book-ingest subtypes.
WebKit integration in SwiftUI using WebView and WebPage for embedding web content, navigation, JavaScript interop, and customization. Use when embedding web content in SwiftUI apps.
Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
Expert-level real estate systems, property management, MLS integration, CRM, virtual tours, and market analysis
Create a Positioning & Messaging Pack (positioning statement, messaging hierarchy, value proposition, tagline/headlines, copy set, validation plan). Use for positioning, messaging, value prop, tagline, homepage hero, one-liner, elevator pitch, and press pattern-matching.
FFI cross-language interop expert covering C/C++ bindings, bindgen, cbindgen, PyO3, JNI, memory layout, data conversion, and safe FFI patterns.
Testing patterns and standards for this codebase, including async effects, fakes vs mocks, and property-based testing.