# 2026 01 05 00 23 56

> Loki Mode Benchmark Results

- Skill: `jorcan/2026-01-05-00-23-56` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add jorcan/2026-01-05-00-23-56`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jorcan/2026-01-05-00-23-56/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jorcan (https://skillmd.com/u/jorcan)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/jorcan/2026-01-05-00-23-56

---

# Loki Mode Benchmark Results

## Overview

This directory contains benchmark results for Loki Mode multi-agent system.

## Benchmarks Available

### HumanEval
- **Problems:** 164 Python programming problems
- **Metric:** Pass@1 (percentage of problems solved on first attempt)
- **Competitor Baseline:** MetaGPT achieves 85.9-87.7%

### SWE-bench Lite
- **Problems:** 300 real-world GitHub issues
- **Metric:** Resolution rate
- **Competitor Baseline:** Top agents achieve 45-77%

## Running Benchmarks

```bash
# Run all benchmarks
./benchmarks/run-benchmarks.sh all

# Run specific benchmark
./benchmarks/run-benchmarks.sh humaneval --execute
./benchmarks/run-benchmarks.sh swebench --execute
```

## Results Format

Results are saved as JSON files with:
- Timestamp
- Problem count
- Pass rate
- Individual problem results
- Token usage
- Execution time

## Methodology

Loki Mode uses its multi-agent architecture to solve each problem:
1. **Architect Agent** analyzes the problem
2. **Engineer Agent** implements the solution
3. **QA Agent** validates with test cases
4. **Review Agent** checks code quality

This mirrors real-world software development more accurately than single-agent approaches.