Total 56,290 skills, AI & Machine Learning has 9373 skills
Showing 12 of 9373 skills
Provides image recognition capabilities for non-multimodal models (such as pure text models like deepseek-v4-pro, GLM-5.1, mimo-v2.5-pro, etc.). This skill is automatically triggered when the main model cannot recognize images, when users send screenshots/design drafts/UI screenshots for analysis, or when users say 'Look at this image', 'Analyze this screenshot', 'What's wrong with this image'. It also applies to any scenario where users paste images but the current model does not support image input. Supports simultaneous recognition of multiple images, with primary-backup fallback achieved by configuring multiple image recognition models. It can also be manually triggered using the commands /skill:vision-support or /vision. Iron Rule: The models configured for this skill are only used for image content recognition and will never participate in main logical reasoning. Note: If the current model is itself a multimodal model (such as Claude Sonnet 4, GPT-4o, Gemini, etc. that can directly recognize images), do not use this skill; let the main model recognize directly.
Create Claude Code custom slash commands with proper structure, frontmatter, and best practices. Use this skill whenever the user wants to create a new command, add a slash command, build a custom command, or mentions "create-command", "new command", "add command", or "make a command" for Claude Code. Also trigger when the user wants to turn a workflow into a reusable command.
Transcribe pre-recorded audio files or URLs with Gladia. Use when the user needs batch/async transcription, speaker diarization, subtitles (SRT/VTT), PII redaction, translation, NER, summarization, chapterization, audio-to-LLM, or any audio intelligence on pre-recorded content. Always prefer the official SDK; fall back to raw REST only when SDK cannot satisfy the requirement.
Install and configure the official Gladia SDKs (@gladiaio/sdk for JS/TS, gladiaio-sdk for Python). Use when the user asks about SDK setup, client initialization, API key configuration, choosing between JS and Python, browser usage, retry/timeout settings, error handling, or SDK vs raw API decisions. The SDK is the recommended default for all Gladia integrations.
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).
Guides engineering of multi-agent systems—agent roles and specialization, orchestration topologies (supervisor, peer-to-peer, hierarchical, blackboard), task decomposition and routing, inter-agent messaging (A2A-style patterns), shared vs partitioned state, fan-out/fan-in and DAG workflows, synchronization and consensus, conflict resolution, fault tolerance and retries across agents, cost/latency/token budgets, cross-agent observability, testing multi-agent flows, and deployment (queues, durable workflows). Framework-agnostic; high-level LangGraph, Deep Agents, and agenthub—not single-agent loops (agentic-ai-developer), ML training (ai-engineer), strategy-only whiteboard (enterprise-strategist), or PM planning (technical-program-manager). Use for multi-agent system, multi-agent engineer, agent orchestration, supervisor agent, agent topology, fan-out fan-in, agent handoff protocol, multi-agent workflow, agent coordination, blackboard pattern, hierarchical agents, A2A, agent DAG, multi-agent architecture.
Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says "what's wrong with my app", "why is my eval failing", "analyze errors", "root cause analysis", "diagnose failures", or wants to understand production failure patterns.
Select and configure evaluation metrics for an AI agent. Guides through metric selection using use-case recommendations, custom LLM-based metric creation with prompt engineering, and agent default attachment. Use when user says "set up metrics", "configure metrics", "create a metric", "what metrics should I use", "add evaluation criteria", or "customize scoring".
Refresh AI's understanding of code. Use this skill when the user mentions terms like "refresh", "re-understand", "refresh cache", "reload", etc. This skill compares all modified files, re-reads and understands the code that may have been modified by humans, ensuring that the AI's context is synchronized with the latest code state.
Import datasets from HuggingFace and convert them to Coval test sets. Use when the user wants to create test cases from HuggingFace dataset or repository.
Calculate agreement between human ground truth and machine labels for a text LLM judge metric, then analyze transcripts and reviewer notes to propose an improved metric prompt. One metric at a time.
Reference and consulting skill for OpenClaw — a messaging gateway that connects AI agents to multiple communication platforms (Telegram, Discord, Slack, WhatsApp, iMessage, and more). Use when working with OpenClaw configuration, channels, Gateway setup, skills, cron jobs, MCP servers, memory, OAuth, or troubleshooting. Also use when the user asks how to implement a use case on their OpenClaw bot (daily morning brief, research workflows, competitive radar, decision playbook), how to add a new channel, or how to connect the CodeAlive context engine. Triggers on requests like "configure openclaw", "add Discord to my bot", "set up morning brief", "gateway not starting", "connect CodeAlive search", "OAuth re-auth", or any close paraphrase. Companion of install-openclaw-to-yc — install both together.