Agent Evals Regression Gate Skill

Overview

This skill helps you turn subjective "looks good" checks into measurable, repeatable quality gates for AI agent behavior. It is built for teams shipping agent workflows to production and needing confidence that model, prompt, or tool changes do not silently degrade outcomes.

Compatibility

Native

• Claude Code / Claude: native skill usage via SKILL.md.
• Codex/OpenAI workflows: compatible with Agent Skills-style SKILL.md content as reusable workflow instructions.

Manual Adaptation

• Gemini CLI: native skill usage via .gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md where supported.
• Cursor: use the generated .cursor/rules/*.mdc adapter for project rules.
• OpenClaw and similar agents: use the same skill content as a reusable prompt/workflow file when native skill import is unavailable.

Prerequisites

• Baseline behavior snapshot from current stable version
• Candidate branch or prompt/tool change to evaluate
• Evaluation tasks that reflect real production use

What This Skill Delivers

• Task matrix across happy-path, edge-case, and failure scenarios
• Deterministic scoring rubric and weighted pass/fail thresholds
• Regression report with severity and root-cause candidates
• Release recommendation for merge, patch, or rollback

Eval frameworks you can wire into this gate

This skill defines the regression-gate workflow; you can back the scoring with an existing eval framework:

Use Promptfoo for broad CI assertions, Ragas when the system is retrieval-augmented, or DeepEval if you want pytest-native ergonomics — then feed their results into this gate's pass/fail thresholds.

How to Use This Skill

Prompt Pattern

Apply the agent evals regression gate skill to this workflow.
Generate:
1) Eval set (minimum 30 cases),
2) Rubric and scoring model,
3) Baseline vs candidate score delta report,
4) Merge decision with blocking issues.

Execution Flow

1. Define scope and quality goals (accuracy, safety, latency, format compliance).
2. Build or refresh eval dataset from production-like tasks.
3. Run baseline and candidate under identical conditions.
4. Compute deltas and classify regressions by impact.
5. Block merge when thresholds fail; output remediation actions.

Troubleshooting

Issue: Scores fluctuate heavily between runs
Fix: Reduce nondeterminism (temperature controls, fixed seeds where possible, stable tool mocks).

Issue: Eval passes but production still degrades
Fix: Expand dataset with real production transcripts and failure exemplars.

Issue: Teams disagree on pass criteria
Fix: Move rubric to explicit weighted dimensions and threshold contracts.

Knowledge Freshness

Treat tooling details as time-sensitive. Re-validate APIs, limits, pricing, auth models, and deployment flags immediately before implementation. If docs conflict with prior memory, follow current official docs and release notes.

Retrieval Sources

• https://openai.com/index/new-tools-and-features-in-the-responses-api/

Output Contract

1. Return a concrete plan with implementation order.
2. Provide production-ready commands/config/code snippets (not placeholders).
3. Include explicit assumptions and unresolved risks.
4. Include a verification checklist with pass/fail criteria.

Quality Gates

• All commands are copy/paste ready.
• Security-sensitive steps call out secret handling and least privilege.
• Version-sensitive guidance cites current docs used.
• Rollback path is included for risky changes.
• Final output includes quick validation commands/tests.

• Source repositorygithub.com 2026-04-10
• Documentationopenai.com
• Package (SHA-256 pinned)/downloads/skills/agent-evals-regression-gate.zip