Results for “ai-q”

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.

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.

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.

Report the brand's organic performance from fb.ai — Search Console clicks/impressions/positions plus AI-engine visibility (whether ChatGPT, Perplexity and friends cite the brand). All traffic-monitor tools are free, so there is no quota gate. Run weekly or monthly.

Orchestrate autonomous AI development pipelines through your Kanban board, managing multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.

Process and analyze high-throughput sequencing data with deepTools for quality control, normalization, comparison, and publication-quality visualizations of ChIP-seq, RNA-seq, and ATAC-seq experiments.

Use before delivering any work product to validate quality. This skill provides a standardized checklist that every agent must complete before marking work as done, ensuring consistent quality across all deliverables regardless of agent or company.

Data pipeline specialist for ETL design, data quality, CDC patterns, and batch/stream processingUse when "data pipeline, etl, cdc, data quality, batch processing, stream processing, data transformation, data warehouse, data lake, data validation, data-engineering, etl, cdc, batch, streaming, data-quality, dbt, airflow, dagster, data-pipeline, ml-memory" mentioned.

Use when the user wants more human-like AI responses — less robotic, less listy, more authentic. Triggers: 'behuman', 'be real', 'like a human', 'more human', 'less AI', 'talk like a person', 'mirror mode', 'stop being so AI', or when conversations are emotionally charged (grief, job loss, relationship advice, fear). NOT for technical questions, code generation, or factual lookups.

Expert knowledge for Azure AI Speech development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using STT/TTS APIs, custom voice/avatars, Voice Live, batch transcription, or containerized speech services, and other Azure AI Speech related development tasks. Not for Azure Communication Services (use azure-communication-services), Azure AI Bot Service (use azure-bot-service), Azure AI Video Indexer (use azure-video-indexer), Azure AI Immersive Reader (use azure-immersive-reader).

Interact with Bitbucket Cloud via helper functions for pull request reviews, repository queries, and pipeline status checks.

Classic code quality indicators — function length, naming, duplication, nesting, magic values, dead code, and React-specific smells. Adapted from Clean Code and Airbnb style guide.

Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pa

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

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.

Monitors and manages Coveo enterprise search platform: sources, indexes, query pipelines, ML models, usage analytics, and security providers, with health checks and license utilization.

Search the web and extract public page content through AIsa's Tavily-backed API relay. Use when: the user needs open-web research, source discovery, current news lookup, or public URL content extraction. Supports concise result sets, deeper research, and news-focused queries.

Benchmark-relative quality gate. Scores the draft against the research dossier's beat spec (depth, consensus coverage, evidence) plus AI-tell and voice signals, runs an adversarial read armed with the SERP benchmark, and emits the verdict that gates the pipeline.

Create distinctive, production-grade frontend interfaces with high design quality. Covers design thinking, typography pairing, color theory, motion design, spatial composition, and code quality. Generates real working code — HTML/CSS/JS or React — with intentional aesthetic direction, not generic AI output.

Requires python3, and AISA_API_KEY. Uses the supplied AISA_API_KEY to send requests to https://api.aisa.one. Native-first ClawHub plugin for `llm-router`. Ships the packaged AIsa skill with an `openclaw.plugin.json` manifest and a Claude-compatible bundle fallback. Unified LLM Gateway - One API for 70+ AI models. Route to GPT, Claude, Gemini, Qwen, Deepseek, Grok and more with a single API key. Use when: the user needs model routing, provider setup, or Chinese LLM access guidance.

This skill should be used when the user asks about crypto prices, market data, K-line charts, funding rates, open interest, long/short ratios, whale orders, liquidation data, crypto news, newsflash, Twitter crypto tweets, trending coins, airdrops, drop radar, airdrop research, project analysis, exchange listings, stock quotes, treasury holdings, or any crypto market query. Also use when user asks about configuring or checking AiCoin API key. Use when user says: 'BTC price', 'check price', 'show K-line', 'funding rate', 'open interest', 'whale orders', 'long/short ratio', 'crypto news', 'newsflash', 'ETF', '监管', '政策', 'trending coins', 'airdrop', 'drop radar', '查行情', '看价格', '大饼多少钱', 'K线', '资金费率', '多空比', '鲸鱼单', '新闻', '快讯', '新闻快讯', '热门币', '空投', '空投项目', '空投机会', '空投研报', '项目分析', '项目详情', '上了哪些交易所', '推特', 'Twitter', 'liquidation map', '配置AiCoin key', 'AiCoin API key', 'AiCoin key安全吗'. Covers 200+ exchanges with real-time data. MUST run node scripts to fetch real data. NEVER generate fake prices or hallucinate market

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Create professional motion graphics and videos from the terminal. AI-powered composition with multi-track layering, choreography, voiceovers, and Lambda rendering. Gated 5-phase workflow ensures quality.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.

Plan and orchestrate multi-slide PowerPoint creation from scratch, classifying each slide type, enforcing visual variety, setting typography/spacing rules, and running text-based QA to catch content issues.

Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.

Read, extract, merge, split, rotate, watermark, encrypt, and OCR PDF files using Python libraries and command-line tools.

Organize, query, validate, and convert neuroscience and biomedical data using the Brain Imaging Data Structure (BIDS) standard.

Create, read, edit, and convert .pptx presentations with design guidance, template manipulation, and visual QA workflows.

Generates creative domain name ideas for your project and checks availability across multiple TLDs (.com, .io, .dev, .ai, etc.). Saves hours of brainstorming and manual checking.

Build production-ready LLM applications, RAG systems, and intelligent agents with architecture design, model selection, and cost controls.