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Skills for retrieval pipelines, evaluations, embeddings, MCP servers, and running language models in production. Measure and operate your AI stack instead of guessing.
558 live llm ops skills for Dots.
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 Roy Yuen
Professional prompt engineering, audit, and evaluation system for production-grade AI agents and workflows.
by Roy Yuen
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
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 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.
by Roy Yuen
Design, debug, and harden AI control loops with explicit contracts and automated verification harnesses.
by Roy Yuen
Architect, scaffold, and harden production-grade AI agents with battle-tested patterns and systematic evaluation.
by Roy Yuen
Design, debug, and optimize production RAG systems with expert architecture, hybrid search, and grounding strategies.
by Joeri Brons
Analyzes your agent conversation history to find and automate your most frequent recurring tasks.
by Leo Li
Automate real Chrome profiles with a professional CLI, SDK, and MCP-ready automation stack for AI agents.
by Roy Yuen
Turn raw agent traces and tool logs into professional production-readiness audits and remediation reports.
Bridge OpenCode to the Agensi marketplace to discover and install AI agent skills via MCP.
Full suite of 15+ GEO+SEO audit tools that scores your visibility across ChatGPT, Perplexity and Google AI Overviews 0-100 and ships the client-ready report.
by tudor
Build a full-stack AI chatbot trained on your own documents across any industry — legal, healthcare, e-commerce, HR, finance, real estate, insurance, education, cybersecurity, government, and more.
Turn complex system documentation into structured, agent-accessible knowledge bases optimized for MCP and AI tools.
by Roy Yuen
Audit your AI agent's evaluation coverage to identify missing release gates and production risks.
Install or audit the real upstream uBrowser MCP runtime, verify its exact 11-tool surface, and get a clear READY or REVIEW report.
Quickstart guide to connect your AI agent to the Agensi marketplace via Model Context Protocol (MCP).
Paste any AI output. Get the production-ready prompt that made it.
Grade and rank sources by type, recency, authority, independence and corroboration, and resolve conflicting evidence, for research briefs, fact-checks and literature reviews.
by LocoLoboZ
A proactive governance layer that validates MCP tool intent and scope to ensure safe, compliant agent behavior.
Analyzes AI agents for performance, reliability, security, and optimization opportunities.
Each skill on this page is a SKILL.md file built for llm ops work and available to use with Dots. Load the instructions through an MCP-compatible agent connection, review the plugins and permissions it needs, and your dot can follow the creator's workflow while handling matching work.
Listings are ranked by installs, upvotes and reviews, so what surfaces first is what other Dots users actually keep. Free and paid skills compete on the same page, and every listing shows its security scan result before you use a skill.
Learn how it works: What is SKILL.md? and SKILL.md format reference.
These are the jobs creators are actually shipping skills for in this category. Each one encodes a full workflow, so Dots 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 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 Dots 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.
Related categories for Dots: Agents & Orchestration, Data & Databases and APIs & Backend.
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 ChatGPT → dots and start a new session. No plugin store, no build step.
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