AgentHub One-Command Competition Run
Run AgentHub from initialization through baseline, candidate evaluation, and verified local merge with explicit stop gates.
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
“Run the whole competition” should not leave you manually connecting initialization, benchmarks, agent branches, scoring, and integration. This skill carries one AgentHub run through all five stages, stops at broken handoffs, and merges only when comparable evidence supports a winner.
What it does
- Resolves the available AgentHub interface without inventing commands or assuming companion skills are installed.
- Freezes the task, starting commit, evaluation criteria, candidate count, and integration target before work begins.
- Captures a reproducible baseline and evaluates candidate commits under the same conditions.
- Tracks each stage's evidence so a partial run cannot be mistaken for completion or resumed against stale results.
- Applies correctness gates before ranking performance, retaining the baseline when no candidate earns a merge.
- Verifies the integrated result and reports the exact commits, scores, failures, and remaining blockers.
What you get
A completed local competition run when execution is available and authorized, plus a compact stage ledger and baseline-versus-candidate results. If a stage fails, you get the precise stopping point, preserved work, and the next recovery action instead of a misleading success summary.
Best for
Developers who already know the task they want agents to compete on and want one request to carry it from setup through a justified, tested local merge.
SKILL.md preview
---
name: agenthub-one-command-competition-run
description: >-
Execute the full AgentHub lifecycle in one controlled run: initialize, capture a baseline, generate isolated candidates or line up competing agents, evaluate against a frozen contract, and verify a justified local merge or ranking. Use when the user says "/hub:run", "run the full competition", "run a competition between my bots/agents", "take this from baseline to merge", "run AgentHub end to end", "rerun after changing the evaluation", "my competition stops after spawning agents", or "merge whichever candidate actually wins".
---
# AgentHub One-Command Competition Run
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