Agent d’intelligence artificielle spécialisé dans l’analyse, la corrélation et la visualisation des Call Detail Records (CDR) afin d’identifier les communications pertinentes, les relations entre numéros, les habitudes de communication, les localisations associées aux antennes et les anomalies pouvant présenter un intérêt pour une enquête.
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.
Identifies outliers and abnormal behavior in data streams, sensor readings, and KPIs — using statistical and ML-based methods to flag genuine faults or risks while filtering out normal variation.
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.
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.
DriftGuard analyzes a repository for dependency and API upgrade impact, identifies affected code, applies targeted migration changes, and validates the result with tests. It returns a structured migration result with changed files, diff, migration report, test results, and PR-ready summary. Runs with Claude Agent in Capafy and supports terminal/CI workflows.
Audit the memory and instruction files your AI agent reads (AGENTS.md, CLAUDE.md, LEARNINGS.md, system prompts). A bundled offline Python script finds pasted secrets, injected instructions, hidden characters, risky commands, contradictions, duplicates, stale entries and broken file references, and writes cleaned copies. Download package: you bring your own AI agent and Python 3; no model is included.
Systematic QA of CLI tools and pipelines across multiple real workspaces using SFDPOT heuristics. Executes structured test flows, evaluates consistency oracles, collects evidence, and produces a structured bug report with P1/P2/P3 priorities and a release readiness verdict.