From agenthub
Executes full AgentHub lifecycle: init session, capture baseline, spawn parallel agents on code tasks, evaluate via pytest metrics or LLM judge, merge winner.
npx claudepluginhub stillquietlyloud/claude_skills --plugin agenthubThis skill uses the workspace's default tool permissions.
Run the full AgentHub lifecycle in one command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.
Guides Next.js Cache Components and Partial Prerendering (PPR) with cacheComponents enabled. Implements 'use cache', cacheLife(), cacheTag(), revalidateTag(), static/dynamic optimization, and cache debugging.
Migrates code, prompts, and API calls from Claude Sonnet 4.0/4.5 or Opus 4.1 to Opus 4.5, updating model strings on Anthropic, AWS, GCP, Azure platforms.
Performs token-optimized structural code search using tree-sitter AST parsing to discover symbols, outline files, and unfold code without reading full files.
Run the full AgentHub lifecycle in one command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.
/hub:run --task "Reduce p50 latency" --agents 3 \
--eval "pytest bench.py --json" --metric p50_ms --direction lower \
--template optimizer
/hub:run --task "Refactor auth module" --agents 2 --template refactorer
/hub:run --task "Cover untested utils" --agents 3 \
--eval "pytest --cov=utils --cov-report=json" --metric coverage_pct --direction higher \
--template test-writer
/hub:run --task "Write 3 email subject lines for spring sale campaign" --agents 3 --judge
| Parameter | Required | Description |
|---|---|---|
--task | Yes | Task description for agents |
--agents | No | Number of parallel agents (default: 3) |
--eval | No | Eval command to measure results (skip for LLM judge mode) |
--metric | No | Metric name to extract from eval output (required if --eval given) |
--direction | No | lower or higher — which direction is better (required if --metric given) |
--template | No | Agent template: optimizer, refactorer, test-writer, bug-fixer |
Execute these steps sequentially:
Run /hub:init with the provided arguments:
python {skill_path}/scripts/hub_init.py \
--task "{task}" --agents {N} \
[--eval "{eval_cmd}"] [--metric {metric}] [--direction {direction}]
Display the session ID to the user.
If --eval was provided:
Baseline captured: {metric} = {value}baseline: {value} to .agenthub/sessions/{session-id}/config.yamlIf no --eval was provided, skip this step.
Run /hub:spawn with the session ID.
If --template was provided, use the template dispatch prompt from references/agent-templates.md instead of the default dispatch prompt. Pass the eval command, metric, and baseline to the template variables.
Launch all agents in a single message with multiple Agent tool calls (true parallelism).
After spawning, inform the user that agents are running. When all agents complete (Agent tool returns results):
Run /hub:eval with the session ID:
--eval was provided: metric-based ranking with result_ranker.py--eval: LLM judge mode (coordinator reads diffs and ranks)If baseline was captured, pass --baseline {value} to result_ranker.py so deltas are shown.
Display the ranked results table.
Present the results to the user and ask for confirmation:
Agent-2 is the winner (128ms, -52ms from baseline).
Merge agent-2's branch? [Y/n]
If confirmed, run /hub:merge. If declined, inform the user they can:
/hub:merge --agent agent-{N} to pick a different winner/hub:eval --judge to re-evaluate with LLM judge--template, agents use the default dispatch prompt from /hub:spawn