prompt engineer
by Roy Yuen
Professional prompt engineering patterns for building robust, secure, and production-ready LLM applications.
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THE AGENSI STORE
54 skills found
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 Roy Yuen
Professional prompt engineering, audit, and evaluation system for production-grade AI agents and workflows.
by Roy Yuen
Design, debug, and harden AI control loops with explicit contracts and automated verification harnesses.
by Roy Yuen
Turn raw agent traces and tool logs into professional production-readiness audits and remediation reports.
by appugouda ai
Turn AWS billing mysteries into 10-minute root cause reports by correlating cost spikes with engineering events.
by Frank Brsrk
Design rigorous chaos engineering experiments and resilience audits to verify production system reliability.
by Roy Yuen
Turn vague prompts into professional task specifications, optimized prompts, and verification test suites.
by Julian
Benchmark your DevOps performance against DORA standards and generate a prioritized 90-day improvement roadmap.
by LocoLoboZ
A structured governance auditor to optimize AI project instructions, clean up context, and manage workspace health.
Lint a prompt template for the issues that cause injection and flaky output. Flags untrusted variables interpolated straight into the instructions (the injection surface), placeholders that are never provided or never used, contradictory instructions, a missing output-format spec where the result is parsed, unbounded context interpolation, and leftover placeholders. It detects problems; it does not write prompts.
by Shandra
Audits Kubernetes manifests, Helm values, deployment logs, and service configs to detect configuration errors and produce safe, reviewable fix plans.
Builds a complete LLM evaluation framework — quality dimensions, a golden dataset, code-based and model-graded rubric graders, judge calibration, and CI regression rules. Use when the user says build LLM evals, create a golden dataset, or set up LLM-as-judge. Do not use when they want to debug one bad model output, not build a repeatable measurement system.
Paste any AI output. Get the production-ready prompt that made it.
Produces a diagnosed and rewritten prompt — component-level failure analysis, structure fixes, few-shot examples, and a regression case set. Use when the user says "fix my prompt", "why does this prompt keep failing", "improve my system prompt", "reduce hallucinations in this prompt", or "my prompt isn't following instructions". Do not use when the request is choosing which model to use, not fixing wording.
by GTDataworks
Convert loose prompt sets into structured, target-ready records with variables, contracts, and eval cases.
Hardens AI prompts and agent workflows against logic errors, tool-misuse, and prompt injection.
Messy, insecure, unfixable — that's what AI builds without architecture. This file is the architecture: 10 years of senior judgement on rendering, caching, security and SEO, so your agent builds it right from day one.
Produces a diagnosis of why a RAG system gives confident-but-wrong answers, or picks between vector search, knowledge graph, and structured/temporal retrieval. Use when the user says "why is our RAG hallucinating", "diagnose this failing query", "should we use a knowledge graph", "pick a retrieval architecture", or "design our memory layer". Do not use when the request is building a RAG system from zero.
Produces a complete AI agent scaffold — charter, system prompt, SKILL.md, tool map, guardrails, runbook, and test suite — from a one-line job description. Use when the user says "build an AI agent for X", "scaffold a new agent", "write a system prompt and SKILL.md", "design an agent's guardrails", or "make an agent workflow runbook". Do not use when the user just wants a single prompt with no full agent architecture.
Produces a personalized set of 6 reusable prompt-habit templates tailored to the user's field. Use when the user says "give me reusable prompt patterns", "build my prompt habits", "stop writing prompts from scratch", "prompt templates for my field", or "make me a prompting system". Do not use when the request is fixing one broken prompt, not building a reusable system.
by John Barros
Orchestrate human-supervised code migrations across repository fleets with verifier loops and judge review gates.
by heyhridyansh
A formal release gatekeeper that audits PRs for deployment risk, migration safety, and operational readiness.