Skillshub run

One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.

install

source · Clone the upstream repo

git clone https://github.com/ComeOnOliver/skillshub

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/ComeOnOliver/skillshub "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/alirezarezvani/claude-skills/run" ~/.claude/skills/comeonoliver-skillshub-run && rm -rf "$T"

manifest: skills/alirezarezvani/claude-skills/run/SKILL.md

source content

/hub:run — One-Shot Lifecycle

Run the full AgentHub lifecycle in one command: initialize, capture baseline, spawn agents, evaluate results, and merge the winner.

Usage

/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

Parameters

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`

What It Does

Execute these steps sequentially:

Step 1: Initialize

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.

Step 2: Capture Baseline

--eval

was provided:

Run the eval command in the current working directory
Extract the metric value from stdout
Display:
```
Baseline captured: {metric} = {value}
```

Append

baseline: {value}

.agenthub/sessions/{session-id}/config.yaml

If no

--eval

was provided, skip this step.

Step 3: Spawn Agents

Run

/hub:spawn

with the session ID.

--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).

Step 4: Wait and Monitor

After spawning, inform the user that agents are running. When all agents complete (Agent tool returns results):

Display a brief summary of each agent's work
Proceed to evaluation

Step 5: Evaluate

Run

/hub:eval

with the session ID:

If
```
--eval
```
was provided: metric-based ranking with
```
result_ranker.py
```
If no
```
--eval
```
: LLM judge mode (coordinator reads diffs and ranks)

If baseline was captured, pass

--baseline {value}

result_ranker.py

so deltas are shown.

Display the ranked results table.

Step 6: Confirm and Merge

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
Inspect branches manually

Critical Rules

Sequential execution — each step depends on the previous
Stop on failure — if any step fails, report the error and stop
User confirms merge — never auto-merge without asking
Template is optional — without
```
--template
```
, agents use the default dispatch prompt from
```
/hub:spawn
```