sqlfluff-complexity

Analyze sqlfluff-complexity JSON reports (hotspot digest and threshold tuning). Reuse --output when the file is no older than 5 minutes; otherwise run report. Scan paths and output path are always user-specified.

Guide users through configuring sqlfluff-complexity for a SQLFluff project by sampling reports, choosing a preset, explaining thresholds, per-directory strictness (nested .sqlfluff vs path_overrides), validating config, and recommending gradual CI rollout.

Inspect repository layout with tree and find, compare to AGENTS.md conventions, and fix misplaced or overly flat generated files. Use when auditing folder structure, after scaffolding or bulk file generation, when output looks flat, or when asked where code/tests/docs should live. Supports inspecting the repository root or a specified subdirectory.

Run linters and fix violations using Trunk when available; when Trunk is missing, run the equivalent tools from the dev environment (see .trunk/trunk.yaml and pyproject.toml) so agents still enforce Ruff, Pyright, Pylint, Bandit, and other enabled checks where possible.

Run unit tests and automatically fix code failures, regression bugs, or test mismatches. Use when tests are failing, after implementing new features, or to repair "broken" tests.

Build the project and automatically fix packaging or build errors (for example Hatch failures) and related breakage. Use when the project fails to build, shows "broken" states, or after making significant changes.

Run CodeQL security/quality analysis and fix findings. Use when the user asks to run CodeQL, security scan, static analysis, or fix CodeQL findings.

Interactive deep research and decision support: frame the real problem (XY-aware), ask exactly 10 multiple-choice questions one at a time, then produce a rigorous comparative evaluation (default 5 approaches, 0–100 scores) and recommendation. Use when the user wants structured discovery before committing to a solution, a scored comparison of approaches, or to avoid jumping straight to an answer—especially for architecture, strategy, or high-stakes trade-offs.

Initialize a new project from the Python template by renaming packages, updating metadata, and cleaning up documentation. Use when starting a new project, "bootstrapping" from this template, or setting up a fresh repository.

Manage Architecture Decision Records (ADRs). Use this to initialize, create, list, and link ADRs to document architectural evolution. Requires 'adr-tools' to be installed.

At the end of a coding agent session (Cursor, Claude Code, Codex, Gemini CLI, or similar), summarize outcomes, failures, inefficiencies, and root causes, then output a concise postmortem with ranked Must/Should/Consider improvements. Chat-only output; do not edit project files unless the user explicitly asks. Skip nit-picks and one-off mistakes.

Broadly and deeply analyze user intent (avoiding XY problems) and evaluate multiple solution approaches (default 5) with scores from 0 to 100.

Safely upgrade Python dependencies using uv. Use when asked to "upgrade dependencies", "update packages", "check for updates", or fix version mismatches in a Python project.

Scan the repository for vulnerable dependencies and known CVEs using Trivy, OSV-Scanner, and Grype via the Makefile. Use when the user asks to scan for vulnerabilities, check dependencies for CVEs, run OSV/Trivy/Grype, or run make scan-vulnerabilities.

Set up the development environment for the project. Use when starting work on the project, when dependencies are out of sync, or to fix environment setup failures.