eval-harness

name	eval-harness
description	Assessment-driven development — Quantify code generation quality with pass@k / pass^k metrics, automatically scored by Grader.

Eval Harness

Overview

Evaluation-driven development: Not just "can the code run", but quantify "how good is the code".

When to Activate

• A guard, hook, workflow, or skill needs measurable repair/regression evidence before adoption.
• A user asks whether an agent workflow is actually improving quality rather than only passing one example.
• A change affects scoring, grading, or benchmark thresholds.

Red Flags

• Only one hand-picked success case exists and there is no held-out or unrelated task coverage.
• A probabilistic grader is used where a deterministic build, test, lint, or coverage check is available.
• pass@k improves while pass^k or unrelated-task behavior regresses.

Checklist

• Define deterministic checks before adding model-judged grading.
• Record target and unrelated scenarios with before/after outcomes.
• Report pass@k, pass^k, and regression counts separately.

Core indicators

pass@k (single success rate)

• Generate k candidate solutions, with a probability of at least 1 passing
• Used to evaluate the completion quality of a single task
• Target: pass@1 > 80%

pass^k (continuous success rate)

• The probability of passing all k consecutive tasks at once
• Used to evaluate overall workflow reliability
• Goal: pass^5 > 50% (pass all 5 consecutive tasks in one go)

Grader type

Code Basics Grader (deterministic)

Grader	Check content	Pass conditions
Compilation check	Whether the code can be compiled / type check passed	Zero errors
Test check	Whether all tests passed	Full green
Lint check	Whether the code style conforms to the specification	Zero warnings (or only allowed warnings)
Coverage check	Check whether the test coverage reaches the standard	≥ 80%

Model base Grader (probabilistic)

Grader	Check content	How to grade
Code review	Code quality, readability, security	0-10 points
Requirements matching	Whether the implementation meets the requirements	0-1 matching degree
Architecture evaluation	Is the design reasonable	0-10 points

Usage process

1. Define Evaluation Criteria

   • Extract verifiable passing conditions from requirements
   • Choose the right grader combination

2. Run the evaluation

   • Code base Grader first (fast, deterministic)
   • Model basics Grader supplement (depth, probabilistic)

3. Analysis results

   • pass@1 < 80% → Unclear requirements or problematic implementation strategies
   • pass^5 < 50% → There is a systemic problem with the workflow

4. Improvements

   • Adjust strategies based on failure patterns
   • Updated Grader rules

VibeGuard Integration

• Code base Grader can reuse guard script output (such as guards/<lang>/check_*.sh)
• Security Grader reference vibeguard/rules/security.md
• Quality Grader reference vibeguard/rules/universal.md