| 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:
- Identify the target part and the feature to be added.
- The wrapper identifies affected parts via the dependency graph.
- Create N candidate worktrees, each for a different implementation approach.
- Summarize:
- run id
- run type (
create)
- feature description
- affected parts
- candidate worktrees (seed + approach-A, approach-B, ...)
- generated
program.md with comparison instructions
-
The user then uses evaluate and record against each approach to find the best capability ceiling.
-
If the user has not scanned yet, run scan first.
-
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
