autoresearch-wrapper-create

name	autoresearch-wrapper-create
description	Create a feature-addition run with multiple candidate implementations. Use when the user wants to add a new feature and compare approaches to find the best capability ceiling.

Autoresearch Wrapper Create

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

Use:

bash "$AUTORESEARCH_RUNNER" create --part <part> --feature "<description>" --candidates <n> --metric <metric> --metric-command "<cmd>" --metric-goal <minimize|maximize>

When this skill is invoked:

1. Identify the target part and the feature to be added.
2. The wrapper identifies affected parts via the dependency graph.
3. Create N candidate worktrees, each for a different implementation approach.
4. Summarize:

• run id
• run type (create)
• feature description
• affected parts
• candidate worktrees (seed + approach-A, approach-B, ...)
• generated program.md with comparison instructions

5. The user then uses evaluate and record against each approach to find the best capability ceiling.

6. If the user has not scanned yet, run scan first.

7. If running interactively, use --no-interactive to skip wizard prompts or omit it to use the interactive wizard.

Example

• /autoresearch-wrapper-create --part src/api.py --feature "add response caching" --candidates 3 --metric latency_ms --metric-command "python bench.py" --metric-goal minimize