sdlc-modellisted

# sdlc-model Match the model to the work. A one-line rename doesn't need Opus; a schema migration shouldn't run on Haiku. This predicts the tier **once from the goal** — then the goal's phases run at that tier (the design: "the rest of the steps will be executed with that model"). ## Recommend a tier for a goal ```bash python3 "${CLAUDE_SKILL_DIR}/scripts/predict.py" "<goal text>" # or a .sdlc/goals/NNNN-*.md path ``` Prints one of `haiku | sonnet | opus | fable`: | Tier | When | Signals | |------|------|---------| | **opus** | hard / risky / high blast-radius | migrate, architecture, security, auth, concurrency, performance, breaking change, payments | | **fable** | creative / writing-heavy | vision, narrative, storytelling, blog, marketing copy | | **haiku** | trivial / mechanical | typo, rename, whitespace, reformat, docstring, dead code | | **sonnet** | everything else (default) | ordinary implementation | Deterministic (regex over the goal text — no LLM, no cost, no drift). Conflicts resolve **upward**: "fix the typo in the security module" → `opus`, because under-powering a hard goal costs more than over-powering a trivial one. ## Automatic selection in the loop (config-gated) `.sdlc/config.json` → `model_selection`: - `"off"` (default) — phases run at the session's model, unchanged. - `"auto"` — `/sdlc-loop` predicts a tier per goal and runs that goal's phases at it. The loop resolves the tier with: ```bash python3 "${CLAUDE_SKILL_DIR}/scripts/predict.py"