---

name: ai-agent-evaluation-benchmarking description: 'Autonomous AI agent benchmark evaluation register: task completion rates, planning accuracy, tool invocation precision, and cost benchmarks.' category: engineering risk: safe source: self source_type: self date_added: "2026-10-01" author: Ranjeet2063 tags: [ai, agents, evaluation, benchmarks, llm, automation, quality] tools: [] source_repo: Ranjeet2063/agentic-awesome-skills

AI Agent Capability Evaluation & Benchmarking

What it is: Standardizes multi-metric capability benchmarking, token cost efficiency, and regression monitoring across autonomous coding agents.

Overview

Provides a standardized, auditable framework and data model for AI Agent Capability Evaluation & Benchmarking operations across distributed engineering and decentralized application systems.

When to Use This Skill

• When formalizing architectural contracts, security invariants, or operational limits for AI Agent Capability Evaluation & Benchmarking.
• When cross-functional review is required between protocol developers, smart contract auditors, and AI engineering agents.
• When generating reproducible CSV, SQL DDL, JSON Schema, and Notion property registers for tracking compliance.

How It Works

1. Define the parameters, thresholds, and identity bindings required for the target operational register.
2. Select appropriate boundary enforcement values from validated enum select sets.
3. Export standardized artifacts (CSV table, SQL DDL, JSON Schema) to integrate into validation CI pipelines.

Field Reference

| # | Field Name | Type | SQL Type | JSON Schema Type | Notion Property Type | Example Value | |---|------------|------|----------|------------------|----------------------|---------------| | 1 | Benchmark Run ID | id | SERIAL PRIMARY KEY | integer | Text | BENCH-001 | | 2 | Evaluated Agent Model | select | VARCHAR(64) | string | Select | Claude 3.7 Sonnet | | 3 | Benchmark Suite Domain | select | VARCHAR(64) | string | Select | SWE-bench Verified | | 4 | Tasks Evaluated Count | number | INTEGER | number | Number | 100 | | 5 | Pass Rate Percentage | number | NUMERIC(5,2) | number | Number | 78.40 | | 6 | Tool Hallucination Rate % | number | NUMERIC(5,2) | number | Number | 0.60 | | 7 | Average Tokens Per Task | number | INTEGER | number | Number | 42500 | | 8 | Cost Per Solved Task USD | currency | NUMERIC(8,4) | number | Number | 0.3420 | | 9 | Regression Verdict | select | VARCHAR(32) | string | Select | Superior | | 10 | Evaluation Lead | text | VARCHAR(64) | string | Text | Ranjeet2063 | | 11 | Benchmark Execution Date | date | DATE | string, format: date | Date | 2026-10-01 |

Select Options

Evaluated Agent Model

Claude 3.7 Sonnet | Claude 3.5 Sonnet | GPT-4o | Gemini 2.0 Flash | DeepSeek V3

Benchmark Suite Domain

SWE-bench Verified | WebArena | AgentBench | HumanEval-Rust | Web3AuditBench

Regression Verdict

Superior | Parity Baseline | Regression Failure

Relations

• Audit Reference -> links to the formal review documentation or test repository.
• Target Architecture -> links to the deployed contract or autonomous agent runtime component.

Examples

Prompt

How do I configure and track AI Agent Capability Evaluation & Benchmarking for our production environment?

Recommended Next Step

Generate the unified field schema, SQL DDL migration, and JSON validation schema to register into your system catalog.

Workflow: Define criteria -> Run automated verification -> Record baseline -> Monitor invariants.

Best Practices

• Enforce strict typing on numerical bounds and currency amounts; avoid unstructured free-text fields for critical states.
• Re-run validation test suites on every state-altering commit or parameter change.
• Keep example data synthetic and isolated from production cryptographic keys or private endpoints.

Limitations

• Provides architectural specifications, data models, and verification schemas; does not execute direct transaction signing without authorized external tooling.
• Requires network connectivity and valid RPC credentials when querying on-chain states.

Security & Safety Notes

• All parameters declare risk: safe. No unauthorized state modification or privileged credential access is performed.
• Use synthetic dummy keys and mock addresses in test suites and local verification scripts.

Common Pitfalls

• Problem: Mismatched decimal precision between contract runtime and database register. Solution: Always verify decimals using the explicit field mapping in this reference.
• Problem: Missing authorization checks prior to state update. Solution: Cross-validate against the Security Audit register before deployment.

Related Skills

• @ai-agent-tool-routing - covers tool schema registration and retry policy.
• @ai-prompt-regression-testing - covers prompt regression baselines and drift.
• @ai-code-generation-guardrails - covers static guardrails for generated code.

Reusable Prompt

I want to establish a verified AI Agent Capability Evaluation & Benchmarking register for our production protocol.
Guide me through the required field parameters and output the corresponding SQL DDL and JSON Schema.