agent-evaluation1Testing and benchmarking LLM agents including behavioral testing, capability assessment, reliability metrics, and production monitoring—where even top agents...
Install via ClawdBot CLI:
clawdbot install abeltennyson/agent-evaluation1Grade Limited — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
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https://api.heybossai.com/v1/pilotAudited Apr 16, 2026 · audit v1.0
Generated May 6, 2026
An e-commerce company deploys an LLM agent for customer support. Using the agent-evaluation skill, they run behavioral regression tests and adversarial tests to ensure the agent handles edge cases like angry customers or out-of-stock items. The evaluation catches flaky responses and prevents production failures.
A fintech startup builds an agent that provides investment advice. They use capability assessment and reliability metrics to verify the agent's accuracy in volatile markets and ensure it doesn't give inappropriate advice. Statistical test evaluation helps confirm consistency across multiple queries.
A health tech company tests a triage agent that prioritizes patient symptoms. They design benchmarks covering rare symptoms and use adversarial testing to see if the agent recommends harmful treatments. The evaluation ensures the agent meets safety standards before clinical deployment.
A legal firm uses an agent to review contracts for clauses. The team implements behavioral contract testing to ensure the agent consistently identifies risky clauses and doesn't change behavior after updates. Regression testing catches any drift in performance.
A software company deploys an agent that reviews pull requests. They set up reliability monitoring to track false positives/negatives in production. When metrics degrade, they use the evaluation framework to diagnose issues and retrain the agent.
Offer a platform where companies can upload their agents and receive automated evaluation reports, including behavioral tests, reliability scores, and benchmark comparisons. Charge per evaluation or subscription.
Provide consulting services to design custom evaluation frameworks for high-stakes agent deployments. Includes benchmark design, adversarial testing, and ongoing monitoring setup. Bill by project or retainer.
Release evaluation tools as open source to build community trust. Offer paid support, custom integrations, and advanced analytics dashboards for enterprises needing compliance and SLAs.
💬 Integration Tip
Integrate evaluation calls as automated CI/CD pipeline steps to catch regressions before deployment. Use environment variables for API keys and set up monitoring dashboards for production metrics.
Scored May 6, 2026
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
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Turn OpenClaw into a learning-loop agent with seeded workspace rules, skill promotion, reflective memory, and proactive maintenance.
Local Python orchestration skill: multi-agent workflows via shared blackboard file, permission gating, token budget scripts, and persistent project context....
Meta-agent skill for orchestrating complex tasks through autonomous sub-agents. Decomposes macro tasks into subtasks, spawns specialized sub-agents with dynamically generated SKILL.md files, coordinates file-based communication, consolidates results, and dissolves agents upon completion. MANDATORY TRIGGERS: orchestrate, multi-agent, decompose task, spawn agents, sub-agents, parallel agents, agent coordination, task breakdown, meta-agent, agent factory, delegate tasks