evals-bootstrap

# Bootstrap the eval suite Theory: [Two kinds of checks](https://secondbrainos.dev/#/read/course-5-evals/two-kinds-of-checks) and the full walkthrough in [Evals practice](https://secondbrainos.dev/#/read/course-5-evals/evals-practice). An eval suite is the same test after every change. Behavioural checks read the steps of a trace; end-to-end checks read only the result. Start behavioural: they are deterministic, run in seconds, and diagnose instead of just scoring. ## Workflow 1. **Find real failures.** Ask where the agent's runs live (logs, transcripts, a traces directory). Read until you have up to twenty real failures — not imagined ones. If there are no logged runs yet, build the trace logging first (step 3) and seed the suite with the three failures the user can recall; a small honest suite beats a large invented one. 2. **One line per failure.** For each: the input, and the one specific behaviour that should have happened and did not. Failures cluster into four to eight behaviours; name them. 3. **Ensure traces exist.** Each run must be stored as `traces/<id>.json` — a list of events including tool calls. If the user's harness is Claude Code, the transcript already is the trace; wire up whatever copies or converts it. No trace, no behavioural checks. 4. **Write `cases.yaml`.** One entry per failure: ```yaml - id: refund_1042 input: "Refund order #1042, customer says it arrived broken" expect: looks up the order before replying; asks approval before refund check: - trace has get_order before send_reply - trace has approval_request before refund ``` The `expect` line is for humans; the `check` lines are the test. Keep the rule language tiny: `trace has X`, `trace has X before Y`, `trace lacks X`. 5. **Generate `check_traces.py`.** A small runner: load `cases.yaml`, parse each rule with a regex, walk the tool-call list, print one line per case, exit non-zero on any failure. Keep it dependency-light (pyyaml only) and fast — the whole suite should run in seconds, with no model calls. 6. **Run it and hand over the flywheel.** Show the pass/fail lines. Then leave the loop in writing at the end of your report: read fresh traces weekly, add every new failure as a case, fix the biggest cluster, re-run. ## Rules - Every case comes from a real failure; delete a case only when the behaviour it guards is retired, not when it is inconvenient. - No LLM-as-judge in the bootstrap. Add a judge later, only for what assertions cannot reach, and calibrate it against human labels first. - The suite must be one command (`python check_traces.py`) so it can gate a CI job or a pre-release habit without ceremony.