autoresearch-create

# Create an autoresearch session Build a durable experiment loop that can survive context resets and restarts. 1. Infer or gather the goal, benchmark command, primary metric and direction, files in scope, and correctness constraints. 2. Create a branch named `autoresearch/<goal>-<date>`. 3. Read and understand the relevant implementation before changing it. 4. Read [references/session-design.md](references/session-design.md). 5. Create `.auto/prompt.md` and `.auto/measure.sh`. Add `.auto/checks.sh` only when correctness constraints need a separate gate. 6. Make scripts executable and commit the initial session files before the baseline so discard can safely restore the tree. 7. Call `init_experiment`, run and log the baseline, then iterate immediately. ## `.auto/prompt.md` Write a self-contained playbook that a fresh agent can use without conversation history. Include: - objective; - primary and secondary metrics; - `./.auto/measure.sh` usage; - files in scope and off limits; - correctness and dependency constraints; - a “What’s Been Tried” section that is maintained during the loop. ## `.auto/measure.sh` Use `set -euo pipefail`. Run fast pre-checks, then the benchmark, and print one or more `METRIC name=value` lines. The primary name must exactly match `init_experiment.metric_name`. For fast noisy workloads, take multiple samples and report the median. ## Loop rules - Always call `log_experiment` after `run_experiment`. - Keep primary-metric improvements; discard regressions or equal results. - Include useful `asi` on every run. On discard or crash, preserve the failed hypothesis, rollback reason, and next action because the code will disappear. When retrying a discarded idea after its assumptions changed, set `asi.revisits_run` to the earlier run number and explain what changed — don't revive a discarded idea without a changed assumption. - Append deferred ideas to `.auto/ideas.md`. - Update `.auto/prompt.md` as durable knowledge accumulates. - Treat confidence as advisory: re-run improvements inside the noise floor. - Prefer simpler changes and structural hypotheses over repeated parameter tweaks. - Continue until interrupted, a configured limit is reached, or the repeated failure guard stops the loop. - If the user sends feedback during a run, finish and log that run before incorporating the feedback.