Data & AI skills

Use when implementing data governance frameworks, building data catalogs, establishing data lineage, defining data quality rules, or setting up data stewardship programs - covers metadata management, data quality, and complianceUse when ", " mentioned.

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Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% by step 10. Build for reliability first, autonomy second. 2025 lesson: The winners are constrained, domain-specific agents with clear boundaries, not "autonomous everything." Treat AI outputs as proposals, not truth. Use when "autonomous agent, autogpt, babyagi, self-prompting, goal decomposition, react pattern, agent loop, self-correcting agent, reflection agent, langgraph, agentic ai, agent planning, autonomous, agents, langgraph, react, planning, reflection, guardrails, reliability, checkpointing" mentioned.

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World-class caching strategies - cache invalidation, Redis patterns, CDN caching, and the battle scars from cache bugs that served stale data for hoursUse when "cache, caching, redis, memcached, cdn, ttl, invalidation, stale, cache aside, write through, cache stampede, thundering herd, cache warming, etag, cache-control, caching, redis, memcached, cdn, performance, http-cache, ttl, invalidation" mentioned.

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World-class color theory expertise combining the scientific precision of Josef Albers' "Interaction of Color," the systematic thinking of color systems from Pantone and RAL, and the perceptual psychology insights from researchers like Bevil Conway. Color is not just aesthetics - it's communication, emotion, and usability compressed into wavelengths. Great color work is invisible when done right. Users don't notice "nice colors" - they notice when they can't read text, when buttons don't look clickable, when errors don't feel urgent, or when the interface feels "off" without knowing why. Color theory is the science of making the right thing feel obvious. Use when "color theory, color palette, color scheme, color harmony, complementary colors, analogous colors, contrast ratio, dark mode colors, light mode, color tokens, semantic colors, color accessibility, color blindness, color psychology, color system, brand colors, data visualization colors, color, design, accessibility, contrast, dark-mode, theming, tokens, wcag, palette, harmony" mentioned.

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Expert in measuring what matters in communities. Covers health metrics, engagement analytics, sentiment analysis, cohort tracking, and reporting. Knows that good data drives good decisions, and bad metrics drive bad behavior. Use when "community metrics, community analytics, measure community, community health, engagement metrics, community reporting, " mentioned.

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Work with climate data, models, and projections for climate impact assessment, downscaling, and scenario analysis using CMIP6 and other climate datasets. Use when "climate model, climate projection, CMIP6, climate data, downscaling, climate scenario, RCP, SSP, global warming, " mentioned.

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Causal inference specialist for causal discovery, counterfactual reasoning, and effect estimationUse when "causal inference, causal discovery, counterfactual, intervention effect, confounder, structural causal model, SCM, dowhy, causal graph, causal, dowhy, scm, dag, counterfactual, intervention, causalnex, confounding, ml-memory" mentioned.

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Implement comprehensive safety guardrails for LLM applications including content moderation (OpenAI Moderation API), jailbreak prevention, prompt injection defense, PII detection, topic guardrails, and output validation. Essential for production AI applications handling user-generated content. Use when ", guardrails, content-moderation, prompt-injection, jailbreak-prevention, pii-detection, nemo-guardrails, openai-moderation, llama-guard, safety" mentioned.

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Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences. Use when "recommendation system, personalization, collaborative filtering, content-based filtering, user preferences, recommend, suggestions, for you, similar items, you might like, " mentioned.

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The practice of collecting, analyzing, and acting on data to drive product decisions. Great analytics isn't about dashboards—it's about insights that lead to action. Every metric should answer a question that changes behavior. This skill covers event tracking, metrics design, dashboards, user behavior analysis, and data-driven decision making. The best analytics teams measure what matters, not what's easy to measure. Use when "analytics, metrics, tracking, dashboard, funnel, cohort, retention, events, KPI, measure, data, insights, conversion, engagement, analytics, metrics, data, dashboards, tracking, funnels, cohorts, KPIs, insights" mentioned.

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Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when "keywords, file_patterns, code_patterns, " mentioned.

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The fusion of trend prediction and AI content generation: detecting emerging trends before they peak and generating content that rides—or creates—the wave. This is marketing alchemy, transmuting data signals into viral gold. Traditional trend marketing is reactive: see trend, create content, hope you're not too late. AI Trend Alchemy is predictive: detect signal, generate content, be there as the wave crests. The combination of AI trend detection and AI content generation creates a feedback loop that can outpace human competition. This skill combines data science intuition with creative generation, reading the digital tea leaves while simultaneously conjuring the content that captures the moment. It's part analyst, part creator, part prophet. Use when "trend, trending, viral, predict, emerging, what's next, cultural moment, before it peaks, early signal, catch the wave, trend prediction, going viral, trend, prediction, viral, cultural, emerging, timing, alchemy, detection" mentioned.

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Measure what matters. Event tracking design, attribution modeling, funnel analysis, experimentation platforms. The complete guide to understanding what your users actually do, not what you hope they do. Good analytics is invisible until you need it. Then it's the difference between guessing and knowing. Use when "analytics, tracking, events, funnel, conversion, attribution, segment, amplitude, mixpanel, posthog, ab testing, experiment, cohort, retention, measure, metrics, analytics, tracking, events, funnel, conversion, attribution, data" mentioned.

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Inter-agent communication patterns including message passing, shared memory, blackboard systems, and event-driven architectures for LLM agentsUse when "agent communication, message passing, inter-agent, blackboard, agent events, multi-agent, communication, message-passing, events, coordination" mentioned.

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Tools are how AI agents interact with the world. A well-designed tool is the difference between an agent that works and one that hallucinates, fails silently, or costs 10x more tokens than necessary. This skill covers tool design from schema to error handling. JSON Schema best practices, description writing that actually helps the LLM, validation, and the emerging MCP standard that's becoming the lingua franca for AI tools. Key insight: Tool descriptions are more important than tool implementations. The LLM never sees your code - it only sees the schema and description. Use when "agent tool, function calling, tool schema, tool design, mcp server, mcp tool, tool use, build tool for agent, define function, input_schema, tool_use, tool_result, agents, tools, function-calling, mcp, json-schema, anthropic, openai, llm-tools" mentioned.

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Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when "build agent, AI agent, autonomous agent, tool use, function calling, multi-agent, agent memory, agent planning, langchain agent, crewai, autogen, claude agent sdk, ai-agents, langchain, autogen, crewai, tool-use, function-calling, autonomous, llm, orchestration" mentioned.

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Build comprehensive AI-native brand asset systems that maintain consistency across all AI-generated content. Train AI tools on brand guidelines, create reusable prompt libraries, and manage visual/voice assets at scale. Use when ", " mentioned.

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Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just "ChatGPT but different" - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when "AI wrapper, GPT product, AI tool, wrap AI, AI SaaS, Claude API product, " mentioned.

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World-class expertise in measuring, attributing, and optimizing AI-generated content performance. Combining data science rigor with content strategy intelligence to answer the questions traditional content analytics can't: Is AI content performing? Which AI variations convert? What's the true ROI of AI vs traditional content creation? This isn't vanity metrics for robots. This is the discipline of proving AI content drives business outcomes - and using that data to make AI content systems better, faster, and more profitable. Built on the principles of companies like Jasper, Copy.ai, and Notion who've scaled AI content operations with measurement as the foundation. Use when "ai content performance, ai content analytics, measure ai content, ai content roi, ai vs human content, ai content attribution, ai content testing, ai variation testing, ai content dashboard, ai content metrics, prompt performance, ai content conversion, ai content quality score, content velocity, ai content efficiency, ai-content, analytics, measurement, attribution, roi, ab-testing, performance, optimization, data-driven, content-analytics" mentioned.

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WorkRally CLI (workrally) — 面向 AI Agent 的 AIGC 漫剧视频创作全流程工具集。支持 AI 生图、AI 生视频、视频提示词优化、画布生音频/音乐、混元 3D 模型生成、AI 生音频、项目/剧集/场次/分镜的完整 CRUD、资产库、媒资管理、无限画布、文件上传下载等。Use when user asks to generate images, generate videos, generate audio, generate music, generate 3d, optimize video prompts, manage projects, series, shots, upload files, download assets, manage materials, or interact with WorkRally platform via command line.

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Manage public/private repository pairs. Verify parity, detect drift, run sync scripts, and validate no sensitive data leaks to the public repo. Use for projects that maintain separate dev and public repositories.

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Systematic analysis of Google Ads and Meta advertising spend to identify wasted budget. Produces actionable exclusion lists with thematic categorization and statistical validation. Use when onboarding new accounts, performing monthly hygiene, or preparing for budget optimization.

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Create polished HTML technical reports and multi-page documents for consulting deliverables. Features a professional design system with dark cover pages, warm cream content pages, Switzer + Cartograph CF typography, and a curated component library (KPI cards, data tables, callout boxes, recommendation cards, priority banners). Outputs standalone HTML that prints cleanly to PDF.

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Create polished HTML slide decks and PDF-ready documents for consulting deliverables. Uses the RRBC design system with warm light mode, dark mode cover pages, Lora/Inter/Roboto Mono typography, and data visualization palette. Trigger on 'deck', 'slides', 'presentation', 'pitch deck', 'keynote', 'report', or 'PDF'.

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