debug

# calcifier:debug — author-friendly recipe execution Same engine as `calcifier:exec` but with verbose Python logging. Per-step trace records show inputs, redacted outputs, durations, and any `fail()` reason. Intended for skill authors who are iterating on a calcified recipe. ## When to invoke - The user is editing a recipe and wants to see what each `@step` returned. - A previous `calcifier exec` run failed and the user wants more signal. ## Procedure 1. Run `calcifier --version` to confirm install. 2. Run: ``` calcifier debug <recipe-path> --input k=v ... ``` First run on the recipe also needs `--trust`. Agent-backed recipes also require `--agent-config <agents.json>`. Use `--trace-out <path>` to retain incremental trace v1 data if the run is interrupted or output parsing fails. 3. The CLI prints: - The full `RecipeRun` summary (every `@step` invocation with pass/fail and duration). - Verbose Python logging from `calcifier.*` loggers (recipe load, agent invocations, retries). - On failure: the `fail()` reason or the exception type and message. 4. Walk the user through any `[fail]` lines. Common author errors: - `@step` return annotation doesn't match what the LLM emits (rejected as schema mismatch — visible in `AgentError`). - An agent returned an undeclared output field (closed schemas reject it rather than caching false success). - A required artifact was not written or failed host verification. - The declared call or wall-clock budget was exhausted. - Recipe body references a step output before calling it. - Pre-condition check raised `fail("…")` early. ## Output A long, line-by-line transcript of the run. Use it to refine the recipe; commit only after the run reaches `OK`.