
Generate synthetic data with SDV (Synthetic Data Vault). Learn patterns from real data with machine learning and produce privacy-preserving synthetic data. Use cases: (1) single-table synthetic data generation, (2) multi-table (relational DB) synthetic data generation, (3) time-series synthetic data generation, (4) synthetic data quality evaluation, and (5) metadata and constraint setup
Parse PuLP and solver logs (CBC, HiGHS, Gurobi, CPLEX) to diagnose infeasible/unbounded/time-limit/execution failures, extract key metrics, and propose prioritized next debugging actions. Use when given optimization run logs, solver stdout/stderr, or LP/MPS export errors and you need root-cause clues that generalize across optimization problems. When LP/MPS/log artifacts exist, include all of them in the analysis.
Intelligently organize messy directories by analyzing files and grouping them based on AI content analysis, temporal patterns, and metadata. Use when users want to clean up cluttered folders (Downloads, Desktop, Documents), sort files by project or topic, or automatically categorize files. Triggers include requests like "organize this folder", "clean up my Downloads", "group these files by project", or "sort my desktop files".
For early development phases. Prevent excessive fallbacks, backward compatibility code, and duplication during code generation. Use when generating, modifying, or refactoring code.
Detect Python dead-code candidates that are referenced only from tests by running Vulture twice and diffing results (production paths vs production+test paths). Use when auditing cleanup targets, reviewing unused-code reports, or validating whether symbols are reachable only through tests.
Use before opening a GitHub issue to search for likely duplicates with gh, inspect candidate issues, and classify them as duplicate, related, or distinct. Use when drafting GitHub issues, checking whether a bug report already exists, responding to duplicate-bot comments, or deciding whether to comment on an existing issue instead of filing a new one.
Create a finite Markdown questionnaire file that contains grouped questions, recommended options, answer fields, and optional rationale fields, then read the completed file and continue from the user's answers. Use when Codex needs to ask multiple questions for requirements, specifications, acceptance criteria, product decisions, design choices, implementation tradeoffs, or any situation where conversational back-and-forth would fatigue the user or make the remaining question count unclear.