Run
One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.
Before & after
Same prompt, same agent — the only difference is the skill.
I can help you run an AgentHub competition round. There are a few different approaches we could take. Do you want me to walk you through the full pipeline manually, or would you prefer an automated end-to-end run? The process usually involves initializing the competition, running a baseline, spawning agent instances, evaluating results, and merging findings. I could do these steps one at a time so you can review between each, or I could chain them together if you're comfortable with minimal stopping points. Do you have a specific competition type in mind, or are you running a standard benchmark? Let me know your preferences and I can get started.
AGENTHUB COMPETITION EXECUTION Configuration • Competition type: Agent capability benchmark • Agents: 3 (baseline LLM + 2 variants) • Dataset: 120 representative prompts • Metric: Accuracy score, latency, cost • Environment: Isolated sandbox AUTOMATED PIPELINE PHASE 1: INIT (2 minutes) → Spin up competition workspace → Load prompt dataset → Initialize baseline agent (vanilla Claude 3.5 Sonnet) → Create config.json with run parameters PHASE 2: BASELINE (8 minutes) → Run baseline on 120 prompts → Collect accuracy, latency, tokens (cost) per prompt → Output: baseline_results.json → Metrics: 87.3% accuracy, avg 340ms latency, 0.42/prompt cost PHASE 3: SPAWN (1 minute) → Instantiate Variant A: Prompt engineering optimized → Instantiate Variant B: System prompt tuned → Configure logging and checkpointing PHASE 4: EVALUATION (15 minutes) → Run all variants in parallel against same 120 prompts → Collect same metrics: accuracy, latency, cost → Variance tracking: ±2% acceptable range → Early stop if Variant accuracy drops below 85% LIVE RESULTS (mid-run) Baseline: 87.3% accuracy (reference) Variant A: 91.2% accuracy (+3.9pp), 380ms latency, 0.48/prompt cost Variant B: 88.1% accuracy (+0.8pp), 315ms latency, 0.39/prompt cost PHASE 5: MERGE (1 minute) → Compare all three results → Identify statistical winner (Variant A: +3.9pp, p < 0.05) → Log decision rationale → Archive config + results + traces → Output: competition_summary.json TOTAL RUNTIME: 27 minutes DELIVERABLES → competition_results.csv (prompt, baseline_score, variantA_score, variantB_score) → trace_logs/ (sampled requests for inspection) → summary.md (winner, metrics, recommendation)
About this skill
name: run description: Use when One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation.
Run
One-shot lifecycle command that chains init → baseline → spawn → eval → merge in a single invocation. Use when the user runs /hub:run or asks to execute a full AgentHub competition end-to-end.
What you get
- Public GitHub repo (alirezarezvani/claude-skills)
- the run skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.
Customize your output
- Fork the repo and adapt the skill's instructions and references to your workflow.
Example output
Activates automatically when your request matches Run; chains with the other skills, agents, and commands in the collection.
Best for
Creators, builders, and teams using Claude Code.
SKILL.md preview
---
name: run
description: Execute the full AgentHub competition lifecycle in one invocation, chaining init, baseline, spawn, eval, and merge stages end-to-end.
version: 1.0.0
category: Development / Engineering
author: AgentVolt
license: proprietary
tags:
- development
- engineering
---
# Run
Execute the full AgentHub competition lifecycle in one invocation, chaining init, baseline, spawn, eval, and merge stages end-to-end.
## When to use
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