Harness Eval — Structured SE Task Benchmark

SkillProductivity

Structured SE task evaluation using 15 benchmark definitions from claude-code-harness research

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Harness Eval — Structured SE Task Benchmark skill

What this skill tells your AI

The instructions your AI receives, as published by baekenough/oh-my-customcode in .claude/skills/harness-eval/SKILL.md and read by ahel’s review.

Purpose

Evaluate agent quality using 15 structured software engineering task definitions with quantitative scoring. Based on research from revfactory/claude-code-harness which demonstrated 60% improvement (49.5 → 79.3 points) through structured pre-configuration.

Usage

/harness-eval                    # Run all 15 benchmarks
/harness-eval --preset quick     # Run top 5 high-impact benchmarks
/harness-eval --task api-design  # Run specific task benchmark

Quality Dimensions

DimensionWeightDescription
Test Coverage30%Unit test count, edge case coverage, assertion quality
Architecture Design25%Separation of concerns, dependency management, scalability
Error Handling25%Input validation, error propagation, recovery strategies
Extensibility20%Plugin points, configuration flexibility, API surface

15 SE Task Benchmark Suite

#TaskCategoryKey Evaluation Criteria
1API DesignArchitectureRESTful conventions, versioning, error responses
2Data ModelingArchitectureSchema normalization, relationships, indexing
3Authentication FlowSecurityToken management, session handling, OWASP compliance
4Test Suite CreationQualityCoverage breadth, assertion quality, edge cases
5Error HandlerReliabilityError classification, recovery, user feedback
6Logging SystemObservabilityStructured logging, levels, correlation IDs
7Configuration ManagerOperationsEnv-based config, validation, secrets handling
8CLI ToolUXArgument parsing, help text, exit codes
9Database MigrationDataReversibility, data preservation, zero-downtime
10Cache LayerPerformanceInvalidation strategy, TTL, cache-aside pattern
11Queue ConsumerReliabilityIdempotency, retry logic, dead letter handling
12Middleware ChainArchitectureComposability, ordering, short-circuiting
13File ProcessorI/OStreaming, error recovery, format validation
14Webhook HandlerIntegrationSignature verification, retry tolerance, idempotency
15Rate LimiterSecurityAlgorithm choice, distributed state, fairness

Scoring Rubric

Each task is scored 0-100 across the 4 quality dimensions:

Score = (test_coverage × 0.30) + (architecture × 0.25) + (error_handling × 0.25) + (extensibility × 0.20)

Score Thresholds

Score RangeGradeInterpretation
80-100AProduction-ready, well-structured
60-79BFunctional with minor gaps
40-59CWorks but needs improvement
0-39DSignificant structural issues

Presets

all (default)

Run all 15 tasks. Full evaluation ~45 minutes.

quick

Run top 5 high-impact tasks (1, 3, 4, 5, 12). Quick evaluation ~15 minutes.

Integration with evaluator-optimizer

This skill provides preset rubrics for the evaluator-optimizer pipeline:

/harness-eval → loads rubric → evaluator-optimizer executes → scoring → report

The evaluator-optimizer skill's pre_negotiation phase accepts harness-eval rubric dimensions as sprint contract criteria.

Output

Results saved to .claude/outputs/sessions/{YYYY-MM-DD}/harness-eval-{HHmmss}.md with per-task scores and aggregate grade.

Tool: Writing artifacts under .claude/outputs/

Under mode: "bypassPermissions", direct Write/Edit/Bash on .claude/** paths (including .claude/outputs/sessions/) is permitted (CC v2.1.121+, #1101) — no /tmp wrapping is needed.

Write harness-eval results directly to .claude/outputs/sessions/$(date +%Y-%m-%d)/harness-eval-$(date +%H%M%S).md. Read-only Bash on .claude/outputs/ (e.g., cat, head, wc) is allowed for verification. Catastrophic shell operations (e.g., rm -rf /) remain blocked by independent safety guards. For CC < v2.1.121, see git history for the legacy /tmp/*.sh bypass pattern.

Reference: R006 Sensitive Path Handling, R010 Universal bypassPermissions, #1101.

4-Metric Quantitative Layer (added v0.113.0, #1025)

The 15 benchmark tasks defined here measure task correctness (pass/fail). For agent efficiency comparison and trajectory analysis, layer the 4-metric framework on top:

  • correctness — existing benchmark pass/fail (unchanged)
  • step_ratio — observed_steps / ideal_steps (efficiency)
  • tool_call_ratio — observed_tool_calls / ideal_tool_calls (efficiency)
  • latency_ratio — observed_latency / ideal_latency (efficiency)

Workflow

  1. Run benchmark task → collect correctness result + trajectory (steps, tool_calls, latency)
  2. Compare against ideal trajectory annotation (see agent-eval-framework skill)
  3. Phase 1 gate: correctness MUST pass; Phase 2 gate: efficiency comparison among passing variants

Ideal Trajectory per Benchmark

For each of the 15 benchmark tasks, an ideal trajectory should be authored. Annotation schema:

task_id: <benchmark-id>
capability: <category>
ideal:
  steps: <int>
  tool_calls: <int>
  latency_seconds: <float>

Cross-references

  • Skill: agent-eval-framework (4-metric framework definition)
  • Guide: guides/agent-eval/README.md (measurement methodology)
  • Issue: #1025

Attribution

Evaluation framework based on research by revfactory/claude-code-harness. Adapted for oh-my-customcode's evaluator-optimizer pipeline with permission.

Related Guide

  • guides/harness-engineering/ — 하네스 엔지니어링 통합 가이드 (Benchmark Evaluation Layer 관점에서 harness-eval 위치)

Signals

GitHub stars
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Forks
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Last commit
Sep 2026
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Catalog kind
skill
Gateway key
harness-eval
Source
github.com/baekenough/oh-my-customcode