Skill of the Month: Award Winning Design by Aman Garg · View skill →
Skills for orchestrating multi-agent systems, building MCP servers, evaluating LLM outputs, and shipping production-grade prompt and retrieval pipelines.
821 live ai agents & llm ops skills for Kiro.
by loreto
Builds the organizational memory schema your AI agent needs to answer why — capturing decision provenance, causal chains, and event context that embedding-based retrieval permanently discards.
by loreto
Architects the right retrieval strategy for every query — teaching your agent when to use RAG, a knowledge graph, or a temporal index instead of defaulting to vector search for everything.
by Timoranjes
Automate Google NotebookLM: create notebooks, add sources, and generate AI audio/podcasts via Python or CLI.
by Roy Yuen
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
by loreto
Give AI agents the ability to trace decision chains, reconstruct causal sequences, and reason over complex event timelines spanning months or years.
by Roy Yuen
Turn your AI agent into a senior engineer with strict task classification and verification-driven coding protocols.
by Shippers
The "Skill for building Skills": Automate creating, testing, and optimisation of custom workflows.
by 高紹育
Deploy a hierarchical team of AI agents to perform 15-30 minute deep-dive research with parallel execution.
by Roy Yuen
High-speed intake for shaping vague prompts, triaging complex tasks, and compressing context for efficient execution.
by loreto
Published AI benchmarks measure brains in jars. They test models in isolation or within a single reference harness — and then attribute all performance to the model. This skill teaches you to decompose agent performance into its two actual components: model capability and harness multiplier. The result is evaluations that predict real-world behavior instead of benchmark theater.
by slava
Ultra-fast discovery and routing for large-scale AI agent skill libraries.
by Roy Yuen
Eliminate hallucinations with an evidence-first verification framework for system state, configs, and file contents.
by Zotri
A production-grade 5-stage subagent dispatch chain to catch bugs and secure solo SaaS deployments.
by Roy Yuen
Generate high-fidelity, structured handoff packets for seamless multi-agent collaboration and session persistence.
by Shippers
Automatically detect, load, and stack the perfect skills combo for any user request.
by loreto
Evaluates AI coding agent platforms across five structural dimensions that determine real-world performance independently of model quality, so teams select on architectural fit rather than benchmark scores.
Transform repetitive, messy prompts into structured, reusable SKILL.md files for your AI agents.
by Sinu
A risk-aware, evidence-based engineering lifecycle protocol for robust agentic task execution and safety.
by Kevin Cline
Autonomous research and task loop that builds on previous findings to solve complex objectives while you sleep.
by Roy Yuen
Professional prompt engineering, audit, and evaluation system for production-grade AI agents and workflows.
by Rafael Silva
Reduce Manus v5 credit consumption by 30-75% through intelligent task routing and autonomous strategy selection.
by Roy Yuen
Python CLI and reusable client for one-shot chat and SSE streaming via the Felo Open Platform.
by Roy Yuen
Turn your AI agent into a coordinator that manages parallel subagents for complex coding and research tasks.
by loreto
RAG fails quietly. It retrieves documents, returns confident-looking answers, and misses the question entirely — because the question required connecting facts across documents, reasoning about sequence, or tracing causation. This skill gives you a five-question diagnostic checklist that classifies any failing query as either RAG-safe or structurally RAG-incompatible, then maps it to the specific failure pattern and the architectural fix that resolves it.
Each skill on this page is a SKILL.md file built for ai agents & llm ops work and confirmed to run in Kiro. Install one into ~/.kiro/skills/, start a new session, and Kiro follows the workflow the creator encoded instead of improvising from a short prompt.
Listings are ranked by installs, upvotes and reviews, so what surfaces first is what other Kiro users actually keep. Free and paid skills compete on the same page, and every listing shows its security scan result before you download anything.
These are the jobs creators are actually shipping skills for in this category. Each one encodes a full workflow, so Kiro follows the same method every time instead of improvising from a one-line prompt.
Start with the outcome, not the label. Open the listing and read the SKILL.md preview: a good ai agents & llm ops skill states exactly what it does, what it needs from you, and what it deliberately refuses to do. Vague descriptions usually mean vague output.
Then check three things: the install count and reviews (other Kiro users voting with their setup), whether the creator ships updates, and whether the skill is universal SKILL.md or agent-specific. Free skills are a fine way to test a creator's style before buying their paid work.
Every paid creator is verified before they can sell, so skills come from practitioners, not scraped prompt packs.
Each version is scanned for prompt injection, credential exfiltration and destructive commands before it goes live.
Some skills are free, but most are paid. Paid skills show the price upfront and include future updates from the creator.
Unzip into ~/.kiro/skills/ and start a new session. No plugin store, no build step.
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