Autoresearch Wrapper Delete

Use the run.sh launcher in this skill's folder (the folder containing this SKILL.md). This block finds it in the standard Claude Code and Codex install locations:

AUTORESEARCH_RUNNER=""
if [ -n "${CLAUDE_SKILL_DIR:-}" ]; then
  AUTORESEARCH_RUNNER="${CLAUDE_SKILL_DIR}/run.sh"
elif [ -n "${CLAUDE_PLUGIN_ROOT:-}" ]; then
  AUTORESEARCH_RUNNER="${CLAUDE_PLUGIN_ROOT}/skills/autoresearch-wrapper-delete/run.sh"
else
  for dir in skills/autoresearch-wrapper-delete .agents/skills/autoresearch-wrapper-delete .claude/skills/autoresearch-wrapper-delete "$HOME/.agents/skills/autoresearch-wrapper-delete" "$HOME/.claude/skills/autoresearch-wrapper-delete" "$HOME/.codex/skills/autoresearch-wrapper-delete"; do
    if [ -f "$dir/run.sh" ]; then AUTORESEARCH_RUNNER="$(readlink -f "$dir/run.sh")"; break; fi
  done
fi
[ -n "$AUTORESEARCH_RUNNER" ] || { echo "Could not resolve the autoresearch-wrapper-delete launcher" >&2; exit 1; }

Use:

bash "$AUTORESEARCH_RUNNER" delete --part <part> --metric <metric> --metric-command "<cmd>" --metric-goal <minimize|maximize>

When this skill is invoked:

1. If the user has not scanned yet, run scan first. Pass --no-interactive when stdin is not a terminal.
2. Identify the part to delete, the metric, and the per-arm budget (--rounds and/or --stop-rule).
3. Run delete. It sets up two arms: control (part kept, starts tuning immediately; it is the tuned pre-deletion baseline) and deleted (part removed and committed in its setup worktree).
4. Summarize: run id, transitive dependents of the deleted part, tuning scope, budget, and the generated program.md. The tuning scope defaults to the deleted part's direct dependents (override with --tune-scope); files changed while repairing the deleted arm are added to its scope. evaluate rejects out-of-scope edits; --allow-out-of-scope is for user-approved exceptions and is flagged in compare.
5. Setup phase: fix broken imports and references in the deleted setup worktree, then start its tuning run:

bash "$AUTORESEARCH_RUNNER" arm-ready --run-id <run> --arm deleted

6. Tuning phase, for both arms: evaluate/record the seed with --arm <arm> to get the setup score, then allocate/evaluate/record with --arm <arm> until the budget is spent.
7. Compare and report whether removing the part costs or gains once dependents are re-tuned:

bash "$AUTORESEARCH_RUNNER" compare --run-id <run>

8. Offer finalize --run-id <run> --merge to merge the winner and clean up. Confirm with the user before --merge or --prune-branches.

Example

• /autoresearch-wrapper-delete --part src/legacy_cache.py --rounds 10 --metric throughput --metric-command "python bench.py" --metric-goal maximize