Loading...
Loading...
Found 3,730 Skills
Consumer-side guide for integrating @lodev09/react-native-true-sheet into a React Native app. Use this skill whenever the user wants to add, configure, control, or debug a bottom sheet using TrueSheet — including ref-based sheets, named global sheets, web support with TrueSheetProvider/useTrueSheet, React Navigation or Expo Router sheet flows, Reanimated-driven animations, scrolling content, stacking, headers/footers, detents, side sheets, keyboard handling, dimming, liquid glass, and Jest testing. Also use when the user is migrating from v2 to v3, troubleshooting layout or gesture issues, or asking about any TrueSheet prop, event, or method — even if they don't mention "TrueSheet" by name but describe a bottom sheet in a React Native context.
Use this skill whenever planning, designing, reviewing, or improving search and recommendation systems for a two-sided trust marketplace built on OpenSearch — covers user-intent framing, product-surface architecture, index design, query understanding, retrieval strategy, ranking, search-plus-recs blending, measurement, and a dashboard-and-alerting layer for ongoing decision making. Triggers on tasks involving marketplace search, homefeeds, ranking, relevance tuning, OpenSearch query DSL, analyzers, synonyms, golden sets, NDCG, A/B testing, or diagnosing an existing retrieval system. Use this skill BEFORE marketplace-personalisation when planning new work; hand off when the diagnosed bottleneck is personalisation-specific.
Optimize digital advertising campaigns across Google Ads, Meta Ads, and LINE LAP including bidding strategies, audience targeting, creative testing, and ROAS optimization. Use this skill when the user needs to improve ad performance, reduce CPA, select bidding strategies, or allocate budget across platforms — even if they say 'our ads aren't working', 'reduce our cost per acquisition', 'Google vs Facebook ads', or 'improve our ROAS'.
Orchestrates multi-advisor council debates on high-impact architecture, technology, or product decisions. Dispatches 3-5 domain archetype subagents (pragmatic-engineer, architect-advisor, security-advocate, product-mind, devils-advocate, the-thinker) through opening statements, tensions, position evolution, and synthesis phases. Preserves dissent and delivers actionable recommendations with captured risks. Use when evaluating trade-offs, stress-testing a PRD or tech spec, resolving dilemmas with multiple viable options, or when a decision needs diverse expert perspectives. Don't use for simple yes/no questions, factual lookups, creative brainstorming without tradeoffs, or tasks where a single expert perspective suffices.
Optimize App Store product pages for search visibility and conversion. Covers App Store Optimization ASO strategy, keyword research and keyword field optimization, app title and subtitle keyword placement, App Store description writing for conversion, promotional text rotation strategy, screenshot caption writing and ordering, in-app review prompt timing with RequestReviewAction and AppStore.requestReview, Custom Product Pages for audience segments, in-app events for search indexing, product page A/B testing experiments, localized metadata optimization across markets, and ratings and review management. Use when improving App Store discoverability, optimizing keyword strategy, writing App Store descriptions or promotional text, planning screenshot captions, setting up Custom Product Pages, configuring in-app review prompts, creating in-app events, running product page optimization tests, or developing a ratings management strategy.
[production-grade internal] Audits and implements web/mobile accessibility — WCAG 2.2 AA/AAA compliance, screen reader support, keyboard navigation, color contrast, ARIA patterns, and assistive technology testing. Routed via the production-grade orchestrator (Harden mode).
Expert product discovery guidance for user research and problem validation. Use when conducting user interviews, validating problems, applying jobs-to-be-done framework, sizing opportunities, customer segmentation, competitive analysis, prototype testing, usability testing, designing surveys, or synthesizing research insights. Covers discovery sprints, continuous discovery, and research operations.
Analyze production Agentforce agent behavior using session traces and Data Cloud. TRIGGER when: user queries STDM session data or Data Cloud trace records; investigates production agent failures, regressions, or performance issues; asks about session traces, conversation logs, or agent metrics; wants to reproduce a reported production issue in preview; runs findSessions or trace analysis queries. DO NOT TRIGGER when: user creates, modifies, or debugs .agent files during development (use developing-agentforce); writes or runs test specs (use testing-agentforce); uses sf agent preview for local development iteration; deploys or publishes agents.
Generate Makefiles with testing, linting, formatting, and automation targets for new projects.
Design, test, and optimize prompts for LLM interactions. Cover prompt patterns (few-shot, chain-of-thought, ReAct), system prompt design, output formatting, prompt evaluation, and prompt optimization techniques. Triggers on "write prompt", "optimize prompt", "design system prompt", "few-shot examples", "chain of thought", "prompt evaluation", "LLM output formatting", "prompt testing", or "prompt patterns".
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
MiniQMT Xuntou Quantitative Trading Interface, based on the XtQuant Python library, supports market data acquisition (K-line, tick data, financial data, etc.) and trading operations (order placement, order cancellation, querying assets/orders/positions) for A-shares, futures, and options. It is used when users need to obtain real-time/historical market data from MiniQMT, conduct quantitative trading, or perform backtesting.