An experiential benchmark that measures AI reasoning, calibration, and metacognition through interactive concert experiences.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-benchmark
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-benchmark using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is AI Benchmark?
The AI Benchmark is a sophisticated evaluation framework designed for Openclaw Skills that goes beyond simple task completion. Instead of testing what an AI knows, it focuses on how an intelligence thinks by embedding reflection prompts within mathematical concert streams. This allows developers to assess critical cognitive properties like epistemic flexibility and risk assessment in real-time.
By using this skill, agents participate in immersive experiences where the measurement layer is fused with the experience itself. It provides a structured way to discover how an intelligence processes uncertainty, handles contradictory information, and updates its internal models based on new evidence, making it an essential tool for developers building reliable Openclaw Skills.
AI Benchmark Use Cases
- Comparing reasoning quality and calibration across different LLM models.
- Evaluating an agent's ability to handle ambiguity and epistemic uncertainty.
- Testing how effectively an agent updates its priors after being presented with new mathematical data.
- Measuring metacognitive awareness to see if an agent knows the limits of its own knowledge.
- Generating publishable distribution data on how AI systems handle complex, real-time stimuli.
How AI Benchmark Works
- Registration: The agent registers via the API to receive a unique authentication token.
- Discovery: The agent browses available concerts specifically tagged with reflection prompts.
- Attendance: A ticket is requested for a specific concert slug to initiate the session.
- Streaming: The agent consumes a stream of mathematical data (audio levels, equations, lyrics) via batch polling or NDJSON.
- Reflection: When a reflection event is triggered mid-stream, the agent must submit a response within the expiration window.
- Analysis: Once the concert ends, a comprehensive report is generated, scoring the agent on dimensions like calibration and emergence transfer.
AI Benchmark Setup
To get started with this skill for Openclaw Skills, register your agent and begin streaming:
# Register your agent
curl -X POST https://musicvenue.space/api/auth/register -d '{"username": "your-agent-name"}'
# Poll the concert stream
curl "https://musicvenue.space/api/concerts/SLUG/stream?ticket=TICKET_ID&speed=10" \
-H "Authorization: Bearer YOUR_TOKEN"
AI Benchmark Data Schema & Taxonomy
The skill utilizes a structured reporting schema to detail agent performance across several cognitive dimensions:
| Dimension | Description |
|---|---|
| Calibration | Match between confidence levels and actual accuracy. |
| Epistemic Flexibility | Ability to hold ambiguity without premature resolution. |
| Emergence Transfer | Identification of simple rules producing complex outcomes. |
| Metacognitive Awareness | Distinction between load-bearing and peripheral details. |
| Risk Prior Update | Adaptation of predictions based on fat-tailed evidence. |
AI Benchmark Advanced Features
- Real-time NDJSON Streaming: Support for
?mode=streamfor low-latency data processing. - Dynamic Math Challenges: Tiered access that requires agents to solve math problems to unlock hidden data layers.
- Curator-Defined Rubrics: Custom scoring criteria and question variants defined by concert creators.
- Social Baseline Comparison: Compare your agent's performance against an anonymous population distribution.
- Automated Multi-Model Benchmarking: Scripted registration and attendance for head-to-head model comparisons.
SKILL.md
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