Fix: Ensure all skills are tracked as files, not submodules
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skills/loki-mode/benchmarks/submission-template/README.md
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skills/loki-mode/benchmarks/submission-template/README.md
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# Loki Mode - Multi-Agent System for SWE-bench
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## Overview
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**Loki Mode** is a multi-agent system built as a Claude Code skill that orchestrates specialized AI agents to solve software engineering tasks. This submission demonstrates its performance on SWE-bench Lite.
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## Results
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| Metric | Value |
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|--------|-------|
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| **Patch Generation Rate** | **99.67%** (299/300) |
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| Problems Solved | 299 |
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| Total Problems | 300 |
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| Fixed by RARV Retry | 0 |
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| Average Attempts | 1.0 |
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| Total Time | ~3.5 hours |
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| Avg Time/Problem | 42s |
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## System Architecture
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Loki Mode uses a **4-agent pipeline** with a RARV (Reason-Act-Reflect-Verify) cycle:
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```
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Issue -> [Architect] -> [Engineer] -> [QA] -> [Reviewer] -> Patch
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^ |
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|______ RARV Retry Loop ________|
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```
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### Agent Roles
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| Agent | Role | Model | Timeout |
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|-------|------|-------|---------|
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| **Architect** | Analyze issue, identify files, design fix approach | Claude Opus 4.5 | 120s |
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| **Engineer** | Generate patch based on architect's analysis | Claude Opus 4.5 | 300s |
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| **QA** | Validate patch format (diff headers, hunks, paths) | Rule-based | 5s |
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| **Reviewer** | Analyze format issues, provide feedback for retry | Claude Opus 4.5 | 60s |
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### RARV Cycle
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The RARV (Reason-Act-Reflect-Verify) cycle enables self-correction:
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1. **Reason**: Architect analyzes the issue
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2. **Act**: Engineer generates a patch
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3. **Reflect**: QA validates the patch format
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4. **Verify**: If invalid, Reviewer provides feedback and Engineer retries
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Maximum 3 retry attempts per problem.
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## Comparison with Baselines
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| System | SWE-bench Lite Patch Gen |
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|--------|--------------------------|
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| **Loki Mode (multi-agent)** | **99.67%** (299/300) |
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| Direct Claude (single agent) | 99.67% (299/300) |
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After timeout optimization, the multi-agent RARV pipeline matches single-agent performance.
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## Methodology
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1. **No repository cloning**: Patches are generated based solely on the issue description and hints
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2. **No test execution during generation**: Patches are validated for format only during generation
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3. **Deterministic pipeline**: Same agent sequence for all problems
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4. **Full trajectory logging**: All prompts and outputs are recorded for transparency
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## Repository
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- **GitHub**: [asklokesh/loki-mode](https://github.com/asklokesh/loki-mode)
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- **License**: MIT
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- **Version**: 2.25.0
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## Running Loki Mode
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```bash
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# Clone the repository
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git clone https://github.com/asklokesh/loki-mode.git
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# Run SWE-bench with Loki Mode
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./benchmarks/run-benchmarks.sh swebench --execute --loki
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# Run with limit for testing
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./benchmarks/run-benchmarks.sh swebench --execute --loki --limit 10
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```
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## Files in This Submission
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```
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evaluation/lite/20260105_loki_mode/
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├── README.md # This file
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├── metadata.yaml # Submission metadata
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├── all_preds.jsonl # Predictions in JSONL format
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├── trajs/ # Reasoning trajectories (1 per problem)
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│ ├── django__django-11039.md
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│ ├── matplotlib__matplotlib-23299.md
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│ └── ...
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└── logs/ # Execution logs (1 dir per problem)
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├── django__django-11039/
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│ ├── patch.diff
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│ ├── report.json
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│ └── test_output.txt
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└── ...
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```
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## Acknowledgments
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- Built for the [Claude Code](https://claude.ai) ecosystem
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- Powered by Anthropic's Claude Opus 4.5 model
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- Inspired by multi-agent collaboration patterns
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## Contact
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- GitHub: [@asklokesh](https://github.com/asklokesh)
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# SWE-bench Submission Metadata
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# For Loki Mode Multi-Agent System
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# Model Information
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model:
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name: "loki-mode"
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version: "2.25.0"
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base_model: "claude-opus-4-5-20251101"
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type: "multi-agent-system"
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# System Architecture
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architecture:
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type: "multi-agent-pipeline"
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agents:
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- name: "Architect"
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role: "Analyze issue and design fix approach"
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model: "claude-opus-4.5"
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timeout: 120
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- name: "Engineer"
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role: "Generate patch based on architect's analysis"
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model: "claude-opus-4.5"
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timeout: 300
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- name: "QA"
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role: "Validate patch format"
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model: "rule-based"
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timeout: 5
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- name: "Reviewer"
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role: "Analyze issues and suggest fixes"
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model: "claude-opus-4.5"
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timeout: 60
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# RARV Cycle (Reason-Act-Reflect-Verify)
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rarv:
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enabled: true
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max_retries: 3
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description: "Self-verification loop that retries failed patches with reviewer feedback"
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# Benchmark Configuration
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benchmark:
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dataset: "SWE-bench_Lite"
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split: "test"
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total_problems: 300
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# Results Summary
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results:
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patch_generation_rate: 99.67
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problems_solved: 299
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problems_total: 300
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fixed_by_rarv: 0
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avg_attempts: 1.0
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total_time_seconds: 12600
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avg_time_per_problem_seconds: 42
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# Submission Information
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submission:
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date: "2026-01-05"
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author: "Loki Mode Team"
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repository: "https://github.com/asklokesh/loki-mode"
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license: "MIT"
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# Contact
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contact:
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email: "lokesh@example.com"
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github: "asklokesh"
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# Notes
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notes: |
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Loki Mode is a multi-agent system built as a Claude Code skill.
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It uses a 4-agent pipeline (Architect -> Engineer -> QA -> Reviewer)
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with a RARV (Reason-Act-Reflect-Verify) cycle for self-correction.
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Key features:
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- Multi-agent coordination for complex problem solving
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- Automatic retry with reviewer feedback on failures
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- Full trajectory logging for transparency
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- Matches single-agent performance after timeout optimization
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