Hands-on prompt engineering and LLM evaluation. Built around a diagnose-then-ship loop: forensic transcript critique ▎ that names the exact failure mode, paired with a fixer that produces complete, deployment-ready rewrites — not ▎ vague advice.
Based on an MIT-licensed open-source agent skill. Unofficial and not affiliated with ASD or STEMG.
I've spent years building production software, designing enterprise architectures, reviewing thousands of code changes, and optimizing AI-assisted development workflows. This Skill packages proven engineering, planning, documentation, testing, and code-review practices into a lightweight workflow that helps AI understand any project before making changes, reducing mistakes, hallucinations, unnecessary token usage, and repeated context loading.
AI Integration — Plans and builds LLM API connections (OpenAI, Anthropic, and more) into real apps. Covers model selection, cost estimation, key safety, error handling, and working code. Adapts to non-engineers and developers alike. Stops the #1 mistake: shipping your API key to the browser.
Turn simple ideas into precise, structured, high-quality prompts for text, image, video, research, coding, business, and creative AI tools.
Compile how you actually decide into one paste-in a file any AI can run. Proven with your own task, two drafts side by side. File always stays yours.
Turn your AI pilot data into a traceable decision pack with TCO, ROI, NPV, payback, sensitivity analysis, evidence gaps, risks, and clear next steps.
Built from auditing AI implementations across dozens of teams — finding where agents break, where prompts drift, where costs balloon, and where humans are still doing work that should be automated. This skill gives you a structured assessment of any AI workflow, with prioritized fixes.