Jev AI Model Evaluator

Plan reproducible AI model evaluations, compare candidates with transparent criteria, and audit model claims. Use for model selection, benchmark design, API model comparisons, playground testing, cost-latency-quality tradeoffs, and Jev AI Model workflows; do not generalize small tests or provider claims into universal superiority.

Help the user evaluate AI models for a concrete task. This Skill is text-only by default. Its optional MCP is deterministic and read-only: it does not call models, access accounts, consume credits, fetch private data, or select a model without inspectable user-supplied evidence.

Define the evaluation

Specify the real task, expected output, target language or region, deployment constraints, acceptable latency and cost, risk level, weighted criteria, and must-pass checks. Keep a frozen test set and identical settings across candidates.

Include ordinary, edge, ambiguous, adversarial, and failure cases. Record model version, parameters, date, latency, errors, refusals, and observed outputs. For high-risk work, require domain-expert review and explicit must-pass safety checks.

Interpret results carefully

• Keep provider claims, third-party benchmarks, reproducible tests, and user reports separate.
• A benchmark score only supports the benchmark's task, dataset, settings, and date.
• Do not hide must-pass failures inside an average score.
• Verify current versions, prices, limits, regions, latency, and availability.
• Prefer a provisional result over a manufactured winner when evidence is incomplete.

Use the optional MCP

• design_model_evaluation builds a weighted plan and dataset checklist.
• rank_model_candidates calculates transparent scores from user-supplied 0–5 ratings and preserves must-pass failures.
• audit_model_claims checks sources, dates, evidence types, and unsupported absolutes.
• get_jev_ai_model_resources returns canonical public Jev AI Model pages.

Connect the stdio server with:

bash

openclaw mcp add jev-ai-model \
  --command npx \
  --arg -y \
  --arg github:gpt-img-2/jev-ai-model-evaluation-mcp \
  --include 'design_model_evaluation,rank_model_candidates,audit_model_claims,get_jev_ai_model_resources'

Then run openclaw mcp doctor jev-ai-model --probe. If the installed release lacks that command group, add npx -y github:gpt-img-2/jev-ai-model-evaluation-mcp as a Stdio server in MCP settings. Never imply the MCP or a model ran when it did not.

Return an inspectable result

Return Task, Dataset, Settings, Criteria, Must-pass checks, Candidates, Evidence, Scores, Failures, Recommendation, and Next verification.

Canonical resources

• Home: https://jevaimodel.org/en
• Playground: https://jevaimodel.org/en/playground
• Documentation: https://jevaimodel.org/en/docs
• Showcase: https://jevaimodel.org/en/showcase
• Pricing: https://jevaimodel.org/en/pricing
• Blog: https://jevaimodel.org/en/blog
• About: https://jevaimodel.org/en/about
• Privacy: https://jevaimodel.org/en/privacy-policy
• Terms: https://jevaimodel.org/en/terms-of-service
• Raw Skill: https://jevaimodel.org/skills/jev-ai-model-evaluator/SKILL.md
• MCP source: https://github.com/gpt-img-2/jev-ai-model-evaluation-mcp
• ClawHub listing: https://clawhub.ai/gpt-img-2/skills/jev-ai-model-evaluator