cbffae07b8
Review ask (dancafc): resolver/repository locals now carry explicit types (matches: list[ScoredHistoricEpc], records: list[HistoricEpc], df: pd.DataFrame, ...) so the flow reads without chasing callee signatures. CLAUDE.md's Type Safety section gains the rule so future sessions enforce it. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
2.2 KiB
Available Skills
Five Claude Code skills are installed in this repo's dev container. Each maps to a phase of the feature lifecycle.
| Skill | Invoke | When to use |
|---|---|---|
| grill-me | /grill-me |
Before implementing — stress-tests a design through sequential questioning |
| to-prd | /to-prd |
After a planning conversation — formalises context into a GitHub issue PRD |
| grill-with-docs | /grill-with-docs |
When domain terms are drifting or new concepts are landing — challenges plans against CONTEXT.md, sharpens terminology inline, and writes ADRs for load-bearing decisions in docs/adr/. Replaces the older ubiquitous-language skill. |
| tdd | /tdd |
During implementation — enforces vertical-slice TDD (one test → one impl → repeat) |
| improve-codebase-architecture | /improve-codebase-architecture |
During refactoring — surfaces shallow modules and proposes deepening opportunities |
Domain glossary lives at CONTEXT.md; load-bearing decisions live at docs/adr/. The legacy UBIQUITOUS_LANGUAGE.md is a redirect.
Typical session chains
Feature planning:
/grill-me → /to-prd → /grill-with-docs
Implementation:
/tdd (+ /grill-me if a design fork appears mid-session)
Refactoring:
/improve-codebase-architecture → /grill-me → /tdd → /grill-with-docs
First time setting up?
Skills are installed automatically when the dev container is built, via the postCreate step that pulls from Hestia-Homes/agentic-toolkit (see .devcontainer/backend/Dockerfile). If an existing container is missing skills, rebuild the dev container.
Type Safety
All new code must pass pyright with zero errors under typeCheckingMode = strict.
Use Optional over | None
Annotate all function return types. Use dict[str, Any] for untyped external API
payloads — never bare dict. Add pandas-stubs when introducing pandas to a module.
Annotate locals assigned from cross-module calls (e.g. matches: list[ScoredHistoricEpc] = rank_historic_epc(...)) — the reader shouldn't need the callee's signature to follow
the flow; inference-only locals are fine within a module's own helpers.