RAG Architecture & Debugging
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
Ship agent workflows in 30 seconds. Browse 2,000+ expert-built and security scanned skill -> Browse skills
THE AGENSI STORE
206 skills found
Design, diagnose, and optimize high-performance RAG systems with an engineering-first framework.
by Ikerg
Audit Snowflake config and SQL against 2026 pricing to find waste and generate instant-fix ALTER statements.
by Nex AI
Deploy a multi-domain Cloudflare Tunnel with systemd hardening and safe-edit workflows for self-hosted apps.
Deploy battle-tested SRE workflows, blameless postmortems, and deployment checklists for high-reliability teams.
Architect safe, staged code migrations with zero-downtime patterns and automated rollback gates.
Adversarial memory audit to remove PII, stale facts, and injected instructions from agent storage.
Scaffold or harden a production-grade GitHub Actions pipeline for WordPress — with a blocking lint gate that stops broken code before it deploys, and a fail notification that makes silent deployment failures impossible.
Flag the destructive operations in a shell command or script before anyone runs it. Catches recursive force deletes, force pushes and history rewrites, database drops and truncates, disk-wipe commands (mkfs, dd to a device), permission blowouts (chmod 777), remote content piped into a shell, broad wildcard deletes, and prod-targeting or disabled-safety flags. Each finding comes with a severity and a safer alternative.
by Nex AI
Deploy production-ready Discord Activities to a Raspberry Pi with Colyseus multiplayer and Cloudflare Tunneling.
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.
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.
A DevSecOps engineer that stands up and tunes static analysis (Semgrep, SonarQube, CodeQL) for high-signal findings — picks the right tool for the stack, writes the config and rulesets, wires a sane CI gate, and tunes out the false positives that get scanners muted.
by Roy Yuen
Transform high-level goals into autonomous Plan-Build-Run-Learn iteration loops with persistent workspace learning.
by Timoranjes
Generate production-ready, secure CI/CD pipelines for GitHub Actions, GitLab CI, and CircleCI across any tech stack.
Cloud architect for migrating apps to Azure App Service, Functions, and Logic Apps with enterprise security.
Scan your OpenClaw config for the settings that quietly hand your agent too much power: unrestricted exec, open inbound DMs, secrets committed in config, the deny-write bypass, sandbox turned off, dangerous Docker binds, and elevated tools. Read-only, plain-English findings, grounded in the OpenClaw docs.
Turn a one-line job description into a production-ready, guarded, and tested AI agent scaffold.
Convert design screenshots into deploy-ready landing pages with optimized AI-generated hero video loops.
Create a system of six reusable, field-specific prompt patterns to ensure consistent AI outputs.
by Roy Yuen
Build, manage, and debug Docker containers and Compose stacks with specialized project workflows.
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.
Map your real cloud dependency tree — data plane and control plane — find the single points of failure ranked by blast radius, catch the classic traps like monitoring that dies with the region it watches, and get a prioritized resilience backlog. The audit every outage post-mortem says to do first.
by Echo Rose
Burn Rate Alert - A Premium AI Agent Skill