2 Commits

Include renames

Author	SHA1	Message	Date
dongarandClaude Opus 5	2b7651fb37	Add ask_expert and eval capture (A14/A16) — step 7c ... Completes step 7. - expert.py: one stateless call for a question needing no repo access. Structured output is contracted at the prompt level, since vendors differ on JSON enforcement, so the reply is parsed and validated locally with one retry — tolerating a fenced code block, because models wrap JSON habitually, but not missing fields - The per-task budget is counted from the canonical log rather than memory, so it survives a restart and cannot hand a circling executor a fresh allowance. Exhaustion raises rather than answering: a caller out of consults should get on with the work or escalate - Consults are recorded as delegation_started/completed with role consultant. A16 asks for a "consult/delegation eval set", and reusing the composite events gets consult evaluation for free rather than inventing a parallel record - evals.py pairs starts with completions into eval records, with verdicts kept outside the canonical log: the log records what happened, a verdict is a later judgement about it, and judgements get revised. Schema v3 adds the outcomes table, so a v1 store now traverses two migrations — a better exercise of the hook than one - ask_expert is offered to the executor as a tool only when a desk is configured, in addition to the observable escalation triggers and never instead of them (A12). Consulting is not mutating, so a completion claim backed only by consults is still challenged Found live: passing an empty message list and putting the question in the system prompt fails outright — the Messages API requires at least one non-system message. The question now travels as a synthesised user message that is never appended to the log, which keeps the consult stateless while remaining a valid request. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>	2026-09-08 19:48:13 +00:00
dongarandClaude Fable 5	ac81dd6234	Add canonical event log and renderer skeleton (A10) ... Build-order step 4, sequenced before the LLM client (step 3) because the client interface consumes the canonical log. - canonical.py: typed events for the full A10 table (user_message through compaction); canonical tool-call IDs minted locally, provider IDs never a join key; strict payload validation on load - StateStore: canonical_events table via schema v2 — the first real migration, exercised in a test against a hand-built v1 database — with append_event/read_events on the same fsync-per-commit path - render.py: request bodies rendered from the log at call time; first adapter targets the OpenAI-compatible shape of LiteLLM's normalized route; compaction events replace their id range at render time while staying in the durable log - Crash integration test now also verifies transcript appends survive process death Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>	2026-09-06 19:51:58 +00:00