multi agent orchestration
by Kaymue
Build production multi-agent systems. 12 patterns, 8 anti-patterns, debugging workflow, cost control. LangGraph + AutoGen + CrewAI.
Ship better AI in 30 seconds. Browse 2,000+ expert-built and security scanned skills -> Browse skills
THE AGENSI STORE
99 skills found
by Kaymue
Build production multi-agent systems. 12 patterns, 8 anti-patterns, debugging workflow, cost control. LangGraph + AutoGen + CrewAI.
Adversarial memory audit to remove PII, stale facts, and injected instructions from agent storage.
Hardens AI prompts and agent workflows against logic errors, tool-misuse, and prompt injection.
Score and optimize any website for AI agent-readability using the Agent Ready REST API.
Find the LLM integration code that will not survive a provider being pulled or going down. Flags single-provider lock-in with no alternative, calls with no failover branch, missing timeouts, retries with no limit or backoff, no degraded-mode default, and hardcoded endpoints with no alternate. This is about the model going away, not the model declining.
Inventory every LLM model and provider your code depends on, the AI bill of materials, and flag the dependency risk. It lists each provider, model, and where it's used, then flags hardcoded model ids, single-provider dependency with no alternative, the same model referenced by different ids, model ids with no config or env indirection, and providers pinned in your manifests. Recognizes OpenAI, Anthropic, Google Gemini, and more from an editable list.
by Corey Jacobs
Run a buyer-readiness check before publishing an AI agent skill package.
A pre-submission gate for medical and scientific manuscripts. It audits your near-final draft, abstract, poster, or congress submission for the disclosures journals now screen on intake — AI/LLM-use disclosure (ICMJE-aligned), authorship against the four ICMJE criteria, conflict-of-interest and funding statements, and data availability — then returns a PASS/REVISE verdict naming every gap, plus a ready-to-paste AI-use disclosure statement. Built by a CMPP-certified medical writer.
Stop leaving your AI startup exposed to malicious users trying to steal your proprietary system prompts or bypass your paywalls. The AI Prompt Injection Defense Shield is an automated code review agent that deeply analyzes your Next.js or Python backend, instantly detecting insecure LLM input fields, un-sanitized API data streams, and weak prompt boundaries. By automatically generating the exact copy-paste code patches required to harden your AI wrapper against the latest OWASP top 10 LLM vulnerabilities, this skill allows solo developers and indie hackers to confidently launch their SaaS without the fear of massive, unexpected API billing spikes or catastrophic data leaks.
Generate a spec-compliant llms.txt (and optional llms-full.txt) for your site or repo so AI agents and crawlers can navigate it. Curates the pages that matter, writes the exact llmstxt.org structure — single H1, blockquote summary, and link sections in the precise format agents parse — then validates the format and tells you where to put it. The honest version: a low-cost, machine-readable surface for the agentic web, not an overhyped SEO trick.
Audit any page, article, or doc for how likely AI answer engines — ChatGPT, Perplexity, Google AI Overviews, Claude — are to surface and cite it. Checks answer-first extractability, question-shaped structure, quotable units, factual specificity, sourcing, schema, and freshness, then returns a PASS/REVISE verdict with the prioritized fixes. This is Answer Engine Optimization, not classic SEO — it optimizes content to be quoted inside AI answers.
by Corey Jacobs
Convert a repeatable workflow into a reusable agent skill or staged skillchain candidate.
by SkillForge
Stop fragile agent chains with structured, versioned, and idempotent handoff contracts for multi-agent systems.
by Kaymue
Diagnose broken RAG systems. 8 failure categories: chunking, embeddings, retrieval, reranking, hallucination. Recall@k measurement.
A retrieval architect that diagnoses why RAG returns confident-but-wrong answers, picks the right context architecture (RAG vs knowledge graph vs structured/temporal retrieval) instead of defaulting to vector search, and designs the institutional-memory schema embeddings throw away.
by Ifásola
Diagnose RAG bottlenecks with precision metrics (Recall, MRR, nDCG) to identify retrieval or ranking failures.
Write and review the docs AI agents actually read — AGENTS.md for your repo and llms.txt for your site. Drafts them from scratch or audits existing ones for completeness, clarity, and wasted context, with a PASS or REVISE verdict.
by Kaymue
Audit prompts and MCP tools for prompt injection. 47 attack patterns, OWASP LLM Top 10, generates adversarial tests. CVSS-scored.
Design and write the eval suite for your LLM-powered feature — the metrics that match your failure modes, a golden dataset plan with starter cases, anchored rubrics, LLM-as-judge prompts with the known bias mitigations, and pass/fail gates wired for CI.
by Kaymue
Audit AI/LLM spend across OpenAI, Anthropic, AWS Bedrock, Azure. Find waste, project runway, FinOps report. Free scripts.
Find the code that quietly runs up your LLM bill: uncapped generations, model calls looping with no limit, an expensive model doing a trivial job, and whole files stuffed into prompts. Read-only, every finding explained in plain English.
Turn a one-line job description into a production-ready, guarded, and tested AI agent scaffold.
Model what your LLM app or agent will cost, find where the money goes, and get a plan to cut it. Per-request and monthly projections, ranked cost drivers, an optimization plan with estimated savings, and unit economics against your pricing — with the arithmetic shown.
Turn Claude into a court. One brain, a dozen free workers. King makes your agent the orchestrator, not the laborer — it plans the task, delegates the heavy lifting to a fleet of free-tier LLMs, cross-checks their work, and hands you a single verified answer better than any one model could give. You keep the judgment; the free models burn the tokens. Now with a guided first-run wizard that gets you set up in minutes.