dax

Audit agent configuration surfaces (AGENTS.md, CLAUDE.md, MCP servers, tool registrations, settings, hooks) for consistency, contradictions, and silent overrides. Returns impact-ordered findings tables.

Audit an agent's main loop for halting conditions, mutation budgets, loop breakers, sensitive-path guards, error recovery, and replayability. Returns impact-ordered findings tables.

Review a database schema migration for production safety: lock contention, backfill strategy, online vs offline, rollback path, foreign-key timing, replication lag. Returns impact-ordered findings tables.

Production readiness audit for a service: health checks, rollback strategy, runbook presence, observability wiring, capacity, dependencies. Returns impact-ordered findings tables.

Audit an AI eval suite for coverage, leakage between datasets, deterministic vs stochastic measurement, ladder correctness, and signal quality. Returns impact-ordered findings tables.

Generate or audit an incident runbook for an alert/service from code, dashboards, and alert rules. Output is a runbook plus a findings table of gaps. Findings follow the shared output contract.

Audit observability for a service or system: structured logging, metrics for golden signals, distributed tracing, dashboard quality, alert noise, SLO definitions. Returns impact-ordered findings tables.

Audit prompts and system instructions across a repo for hygiene: hardcoded model assumptions, secret leakage, drift between surfaces, length budget, injection resistance. Returns impact-ordered findings tables.

Review the tools an AI agent has access to: over-broad permissions, side-effecting tools without approval gates, missing audit logs, dangerous defaults, schema correctness. Returns impact-ordered findings tables.

Audit CI/CD workflows for correctness, caching, action pinning, secret hygiene, and job-graph health. Returns impact-ordered findings tables.

Security-focused review of GitHub Actions workflows: pull_request_target misuse, expression injection, privileged token misuse, supply-chain exposure. Returns impact-ordered findings tables.

Audit the tag-triggered release pipeline for reproducibility, provenance, signing, supply-chain integrity, and rollback. Returns impact-ordered findings tables.

Reproduce reported bugs or symptoms by creating minimal, verifiable scripts in the DAX Lab.

Create high-quality MCP (Model Context Protocol) servers that extend DAX's tool capabilities. Use when building MCP servers to integrate external APIs, services, or custom tools into DAX. Supports Python (FastMCP) and TypeScript (MCP SDK). Aligns with DAX's governance model - MCP tools will go through DAX's approval system.

Create and improve DAX skills. Use when users want to create a new skill, modify an existing skill, run evaluations to test skill performance, or optimize skill triggering. This skill provides the complete workflow for building production-ready skills for DAX.

Check whether a DAX release is coherent, verifiable, and safe to ship across package, docs, diagnostics, and release artifacts.

Inspect release artifacts, generated outputs, screenshots, and manifests for completeness, consistency, and operator value.

Review local git changes for correctness, risk, and release impact with findings-first output.

Map a repository quickly and produce a grounded reading order, integration inventory, and next-step recommendation.