Autoresearch Wrapper

Locating the CLI

Use the launcher next to this skill:

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/run.sh"
elif [ -f ".claude/skills/autoresearch-wrapper/run.sh" ]; then
  AUTORESEARCH_RUNNER="$(readlink -f ".claude/skills/autoresearch-wrapper/run.sh")"
elif [ -f "skills/autoresearch-wrapper/run.sh" ]; then
  AUTORESEARCH_RUNNER="$(readlink -f "skills/autoresearch-wrapper/run.sh")"
else
  echo "Could not resolve the autoresearch-wrapper launcher" >&2; exit 1
fi

Then use bash "$AUTORESEARCH_RUNNER" for all commands below. If the skill lives inside the current repo, python3 scripts/autoresearch_wrapper.py also works.

When this skill is invoked:

1. If invoked with no arguments or just /autoresearch-wrapper, route to the end-to-end wizard:

bash "$AUTORESEARCH_RUNNER" wizard

If you are operating without a real interactive stdin and the CLI cannot complete the prompts directly, emulate the same flow manually:

• Run bash "$AUTORESEARCH_RUNNER" scan --no-interactive
• Start from the compact core-functionality summary and focused dependency graph
• Only ask for the full language/directory listing if the user explicitly wants a broader scan
• Ask which functionality area they want to improve first
• Ask which specific part to optimize
• Ask for the metric name, metric command, metric goal
• Ask for execution mode (sequential / parallel / wild) and rounds
• Persist the configuration with configure
• Ask if the user wants to start the run immediately

If the user explicitly asks to inspect everything, rerun with:

bash "$AUTORESEARCH_RUNNER" wizard --full-summary

1. If the user provides extra text after /autoresearch-wrapper, treat it as the high-level instruction and act accordingly.

1. If the user names a repo-local script path, wrap it with:

bash "$AUTORESEARCH_RUNNER" wrap <script-path>

1. Before starting any run, make sure these are explicit:

• target part
• metric name
• metric goal
• metric command
• sequential, parallel, or wild mode
• rounds or stop rule
• early exit patience (optional)

Persist them with:

bash "$AUTORESEARCH_RUNNER" configure --part <part> --metric <metric> --metric-command "<cmd>" --metric-goal <minimize|maximize> --mode <sequential|parallel|wild> --rounds <n>

1. Prefer Git worktrees. Do not optimize in the primary checkout if the helper can create a worktree-backed run.

1. When the user wants to inspect state, start a run, inspect metrics, create features, delete parts, or monitor progress, prefer these companion Claude skills:

• /autoresearch-wrapper-status
• /autoresearch-wrapper-run
• /autoresearch-wrapper-flow
• /autoresearch-wrapper-create
• /autoresearch-wrapper-delete
• /autoresearch-wrapper-monitor

Examples

• /autoresearch-wrapper scan this repo and summarize the dependency-aware optimization candidates
• /autoresearch-wrapper wrap scripts/bench.py and use the suggested metric preset
• /autoresearch-wrapper optimize src/api.py with metric latency_ms, sequential mode, and 5 rounds

Guidelines

• Do not start a run if the metric is unclear.
• Keep dependency-aware boundaries explicit.
• Respect the wrapper's persisted state in .autoresearch-wrapper/state.json.
• If the user asks to optimize immediately, still confirm or infer the full run gate first.