Analysez logs, diagnostics, erreurs et éléments techniques fournis afin d’identifier des causes probables, prioriser les anomalies et préparer un plan de correction.
This agent helps Python teams using the Anthropic or OpenAI SDK safely cut LLM spend without risking regressions. With zero code changes, it maps call sites and runs your test scenarios to rank token waste from missed prompt caching or heavy model tiers. It tests fixes in isolated git worktrees, enforcing a strict "do-no-harm" gate that reverts any patch failing to preserve behavior. It delivers a clear savings audit that converts into verified, behavior-preserving code patches upon opt-in.
Michelin-kitchen audit for your AI coding agent setup: finds where you are still the bottleneck, places you on the trust ladder, and hands you the one upgrade that lets you run more agents with less supervision.
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.
Analyze paid advertising, CRM and revenue data to find where ad spend stops turning into qualified leads, customers and revenue. ProfitLeak AI identifies funnel leaks, calculates key metrics and gives evidence-based actions to improve marketing efficiency.
I design AI prompts professionally and have analyzed thousands of prompts across ChatGPT, Claude, and Gemini. I distilled the 5 essential elements that separate effective prompts from weak ones into this instant diagnostic tool. Rule-based scoring ensures consistent, deterministic results every time.
Audit AI agent workspaces for authority, secrets, data flow, memory, supply-chain, and evidence risks—locally and without executing target code.