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Found 5,658 Skills
Compress an agent's routing file (RESOLVER.md or AGENTS.md) by converting granular skill-per-row tables into functional-area dispatchers. Each area lists sub-skills in a "(dispatcher for: ...)" clause. The LLM reads one area entry and routes to the correct sub-skill. Proven via held-out A/B eval: dispatcher pattern outperforms naive pipe-table compression.
Using the Pi terminal agent — workspace setup, sessions, /commands, compaction, settings.json/AGENTS.md, skill discovery, providers/models, plus theme/keybinding/prompt customization (SYSTEM.md, APPEND_SYSTEM.md, settings.json, keybindings.json). Use for any "how do I configure/run Pi" question.
Use this skill whenever the user is working with the Pydantic AI framework — including building AI agents, defining structured outputs with Pydantic models, wiring up tools/function calling, configuring model providers (OpenAI, Anthropic, Gemini, etc.), managing dependencies via agent context, handling streaming responses, or debugging agent runs. Trigger this skill even for adjacent tasks like "how do I make my agent return JSON", "set up a multi-step agent", "add a tool to my agent", or "validate LLM output with Pydantic" — any time Pydantic AI is mentioned or implied as the target framework.
Web browser automation & testing for AI agents — agent-browser CLI (Chrome/CDP, fill forms, click, scrape, screenshot, dev-server verification with page-load + console-error + UI-element checks) plus Playwright toolkit for local web apps (debugging UI behavior, browser logs, screenshots). Use when the user asks for web QA, dev-server verification after `npm run dev`, or any browser automation against a website. For desktop/Electron/Tauri apps, see `desktop-test-agent-tauri`.
Execute Python code in isolated rootless containers with MCP server proxying for token-efficient agent workflows
Self-improving agent toolkit — forge runtime tools, adapt personality traits, manage skills dynamically, compose multi-step workflows, and self-evaluate performance with bounded autonomy.
Verify claims in agent responses against sources using semantic similarity and web fact-checking.
Use when the user says "get started with Cekura", "set up Cekura", "onboard to Cekura", "I'm new to Cekura", "help me set up my agent", "how do I use Cekura", "walk me through Cekura", "configure my project", "first time using Cekura", or needs guidance on initial platform setup. Covers two onboarding paths: **testing** (default — build evaluators and run simulated calls) and **observability** (ingest production call logs and evaluate them).
Use when the user asks to "create a metric", "write a metric", "design a metric", "build a metric for", "evaluate agent performance", "measure call quality", "track a KPI", "add a workflow metric", "improve my metric", "fix a metric", "debug metric results", "set up quality scoring", or "what metrics do I need". Also relevant when discussing LLM judge prompts, custom code metrics, evaluation triggers, VALID_SKIP patterns, section extraction, or metric best practices for Cekura voice AI agents. Covers both creating new metrics and reviewing, iterating on, or troubleshooting existing ones.
Curated research collection on adaptation strategies for agentic AI systems, covering agent and tool adaptation methods with RL, SFT, and DPO approaches
Integrate the Agentic Commerce Protocol (ACP) for AI-driven commerce between buyers, agents, and businesses
Configure and use ktx to build an executable context layer for AI agents querying data warehouses with semantic layers, wiki knowledge, and approved metrics