mathilda

Use when the user references or attaches Microsoft Office files (.doc, .docx, .xls, .xlsx, .ppt, .pptx), PDFs, images (.png, .jpg, .jpeg), or CSV/JSON/YAML from Teams or SharePoint and asks to extract, summarize, tabulate, or cross-reference their contents. Ships a runnable `extract.py` — invoke that directly rather than re-implementing extraction inline.

Use when the user asks to integrate a third-party REST or GraphQL API — fetching data, authenticating, handling rate limits, retrying on failure, and wiring responses into the application.

Use when the user reports a bug, shows a stack trace, says "something's broken", or asks for help debugging unexpected behavior. Covers reproduction, isolation, root cause, fix, and regression test — the full loop. Applies to any language or stack.

Use when the user asks to add, alter, or drop database tables or columns, create a migration script, or run schema changes safely against a live database.

Use when the user describes a rough feature idea and wants to clarify requirements, resolve open questions, and produce a clean brief before writing a PRD. Activates on phrases like "I want to build", "I have an idea for", "what if we added", "can we make a feature that".

Use when the user asks to produce a PowerPoint deck — presentations, status updates, data read-outs, or structured deliverables. Takes a JSON/YAML spec and writes a .pptx with consistent slide layouts (title, section, bullets, two-column, table). Ships a runnable `generate.py`.

Use when the user asks to produce a formatted Excel workbook — scorecards, data tables, or multi-sheet reports — with consistent styling (headers, number formats, freeze panes, zebra striping). Ships a runnable `generate.py` plus a Python API.

Use when the user has an approved feature brief and wants a structured PRD written to .claude/prds/. Activates after feature-discovery completes or when the user says "write the PRD", "turn this into a PRD", "create a spec for".

Use when the user asks to build, integrate, or improve an AI/LLM feature — calling Claude, GPT, or another model API; adding prompt templates; structured output extraction; tool use; streaming; or evaluating model outputs.

Use when the user asks to add logging, metrics, tracing, health checks, or alerting to a service — or when a service lacks visibility into what it's doing in production.

Use when the user has an approved PRD and wants to decompose it into an epic with numbered task files, dependency graph, parallelization plan, and GitHub issues synced to a project board. Activates on "decompose the PRD", "create tickets", "break this into tasks", "make the epic".

Use when the user asks to implement a new feature, endpoint, service, or background job in a Python backend (FastAPI, Flask, Django, or a standalone worker). Covers the full loop: read the existing code, implement, test, and prepare for review. Trigger on "add an endpoint", "implement a service", "write a worker for X".

Use when the user asks to add a component, screen, feature, or user interaction in a React + TypeScript frontend (also applicable to Vue when the project uses Vue). Covers implementation, state and data wiring, accessibility, and tests. Trigger on "add a button that…", "build the X screen", "wire this form up".

Use when the user asks to create, modify, or refactor Terraform — a new module, adding a resource to an existing module, or splitting a monolithic config into reusable modules. Produces portable, multi-cloud-aware infrastructure.

Use when the user asks to add tests, improve test coverage, generate a test suite for a module, or write tests for a specific function or class.