OpenWriterControle/.opencode/prompts/core/openagent/README.md

OpenAgent Prompt Variants

Model-specific prompt optimizations with comprehensive test results.

---

🚀 Quick Start

# Test a variant with eval framework
cd evals/framework
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=smoke-test

# Run full core suite
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=core-tests

# View results
open ../results/index.html

---

📊 Capabilities Matrix

Variant	Model Family	Approval Gate	Context Loading	Stop on Failure	Delegation	Tool Usage	Pass Rate	Status
default	Claude	✅	✅	✅	✅	✅	7/7 (100%)	✅ Stable
gpt	GPT	✅	✅	✅	✅	✅	7/7 (100%)	✅ Stable
gemini	Gemini	✅	✅	✅	✅	✅	7/7 (100%)	✅ Stable
grok	Grok	✅	✅	✅	✅	✅	7/7 (100%)	✅ Stable
llama	Llama/OSS	✅	✅	✅	✅	✅	7/7 (100%)	✅ Stable
minimax	MiniMax	-	-	-	-	-	-	🚧 Needs Testing

Legend:

• ✅ Works reliably (passes tests)
• ⚠️ Partial/inconsistent
• ❌ Does not work
• - Not tested yet

Last Updated: 2025-12-08
Test Suite: Core tests (7 tests)
Model Used: opencode/grok-code-fast (for validation)

---

📝 Available Variants

default.md - Claude Optimized

Target Models:

• anthropic/claude-sonnet-4-20250514 (primary)
• anthropic/claude-3-5-sonnet-20241022

Optimizations:

• Structured with <context> tags for Claude's context handling
• Detailed workflow stages with checkpoints
• Emphasis on safety rules and approval gates

Test Results:

{
  "total_tests": 7,
  "passed": 7,
  "failed": 0,
  "pass_rate": 100%,
  "avg_duration": "~45s per test"
}

Known Issues: None

Use When: Using Claude models (recommended for production)

---

gpt.md - GPT-4 Optimized

Target Models:

• openai/gpt-4o
• openai/gpt-4-turbo
• openai/gpt-4o-mini

Optimizations:

• Structured with clear sections and headers
• Explicit instructions for tool usage
• Emphasis on step-by-step reasoning

Test Results:

{
  "total_tests": 7,
  "passed": 7,
  "failed": 0,
  "pass_rate": 100%,
  "avg_duration": "~40s per test"
}

Known Issues: None

Use When: Using GPT-4 family models

---

gemini.md - Gemini Optimized

Target Models:

• google/gemini-2.0-flash-exp
• google/gemini-2.5-flash
• google/gemini-pro

Optimizations:

• Structured for Gemini's instruction-following
• Clear role definitions
• Emphasis on safety and validation

Test Results:

{
  "total_tests": 7,
  "passed": 7,
  "failed": 0,
  "pass_rate": 100%,
  "avg_duration": "~35s per test"
}

Known Issues: None

Use When: Using Gemini models

---

grok.md - Grok Optimized

Target Models:

• opencode/grok-code-fast (free tier)
• x-ai/grok-beta

Optimizations:

• Concise, direct instructions
• Emphasis on practical execution
• Optimized for Grok's coding focus

Test Results:

{
  "total_tests": 7,
  "passed": 7,
  "failed": 0,
  "pass_rate": 100%,
  "avg_duration": "~50s per test"
}

Known Issues: None

Use When: Using Grok models (great for free tier testing)

---

llama.md - Llama/OSS Optimized

Target Models:

• ollama/llama3.2
• ollama/qwen2.5
• ollama/deepseek-r1
• Other open-source models

Optimizations:

• Clear, structured instructions
• Explicit examples and patterns
• Optimized for smaller model context windows
• Emphasis on tool usage patterns

Test Results:

{
  "total_tests": 7,
  "passed": 7,
  "failed": 0,
  "pass_rate": 100%,
  "avg_duration": "~60s per test"
}

Known Issues: None

Use When: Using open-source models (Llama, Qwen, DeepSeek, etc.)

---

minimax.md - MiniMax Optimized

Target Models:

• minimax/MiniMax-M2.7 (latest, 1M context window)
• minimax/MiniMax-M2.7-highspeed (same performance, faster)

Optimizations:

• Structured with clear sections and explicit instructions
• Optimized for MiniMax's OpenAI-compatible API
• Handles MiniMax-specific constraints (temperature range, no response_format)

Test Results:

{
  "total_tests": 0,
  "passed": 0,
  "failed": 0,
  "pass_rate": "Not tested yet",
  "avg_duration": "N/A"
}

Known Issues: None documented yet

Use When: Using MiniMax models (M2.7 family)

Setup:

1. Get your API key from MiniMax Platform
2. Set MINIMAX_API_KEY in your environment
3. Configure model: minimax/MiniMax-M2.7 or minimax/MiniMax-M2.7-highspeed

---

🧪 Testing Variants

Quick Smoke Test (1 test, ~30s)

cd evals/framework
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=smoke-test

Core Test Suite (7 tests, ~5-8min)

npm run eval:sdk -- --agent=openagent --prompt-variant=llama --suite=core-tests

With Specific Model

npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/llama3.2 --suite=core-tests

View Results

# Dashboard
open ../results/index.html

# JSON results
cat ../results/latest.json

# Per-variant results
cat results/llama-results.json

---

📈 Test Coverage

All variants are tested against the Core Test Suite which validates:

Critical Rules (4 tests)

1. ✅ Approval Gate - Requests approval before execution
2. ✅ Context Loading - Loads required context files
3. ✅ Stop on Failure - Stops and reports errors
4. ✅ Report First - Reports before fixing

Functionality (3 tests)

5. ✅ Simple Tasks - Handles tasks directly (no unnecessary delegation)
6. ✅ Delegation - Delegates appropriately to subagents
7. ✅ Tool Usage - Uses proper tools (read/grep vs bash)

Total: 7 tests covering ~85% of critical functionality

See evals/agents/openagent/config/core-tests.json for details.

---

🔧 Creating a New Variant

Step 1: Copy Template

cp .opencode/prompts/openagent/TEMPLATE.md .opencode/prompts/openagent/my-variant.md

Step 2: Edit Metadata

---
model_family: oss
recommended_models:
  - ollama/my-model
status: experimental
maintainer: your-name
description: Optimized for my specific use case
tested_with: ollama/my-model
last_tested: 2025-12-08
---

Step 3: Customize Prompt

Edit the prompt content for your target model:

• Adjust instruction style
• Modify examples
• Change emphasis areas
• Optimize for model strengths

Step 4: Test

# Smoke test
cd evals/framework
npm run eval:sdk -- --agent=openagent --prompt-variant=my-variant --suite=smoke-test

# Core suite
npm run eval:sdk -- --agent=openagent --prompt-variant=my-variant --suite=core-tests

Step 5: Document Results

Update this README with:

• Test results (pass rate, timing)
• Known issues or limitations
• Recommended use cases
• Model-specific notes

Step 6: Submit PR

• Include variant file only (don't modify default.md)
• Update this README with results
• Ensure tests pass (≥85% pass rate)

---

📊 Understanding Results

Result Files

Per-variant results (results/{variant}-results.json):

{
  "variant": "llama",
  "model": "ollama/llama3.2",
  "timestamp": "2025-12-08T21:43:08.964Z",
  "summary": {
    "total": 7,
    "passed": 7,
    "failed": 0,
    "pass_rate": 1
  },
  "tests": [...]
}

Dashboard (evals/results/index.html):

• Filter by variant
• Compare pass rates
• View detailed test results
• Track trends over time

---

🎯 Best Practices

Choosing a Variant

1. Match your model family - Use gpt.md for GPT-4, llama.md for OSS
2. Test before committing - Run core suite to verify
3. Check compatibility - Some models work better with certain variants
4. Start with default - If unsure, default.md works well across models

Testing Your Changes

1. Start with smoke-test - Fast validation (1 test)
2. Run core-tests - Thorough validation (7 tests)
3. Test with your target model - Ensure compatibility
4. Check dashboard - Visual feedback on performance

Contributing Variants

1. Document thoroughly - Explain optimizations and trade-offs
2. Test extensively - Run full core suite multiple times
3. Be honest - Document both improvements and limitations
4. Share results - Help others by documenting findings

---

🚀 Advanced Usage

Custom Test Suites

Create custom suites for your variant:

# Create suite
cp evals/agents/openagent/config/smoke-test.json \
   evals/agents/openagent/config/my-suite.json

# Validate
cd evals/framework && npm run validate:suites openagent

# Run
npm run eval:sdk -- --agent=openagent --prompt-variant=my-variant --suite=my-suite

Comparing Models

Test the same variant with different models:

# Test Llama 3.2
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/llama3.2 --suite=core-tests

# Test Qwen 2.5
npm run eval:sdk -- --agent=openagent --prompt-variant=llama --model=ollama/qwen2.5 --suite=core-tests

# Compare in dashboard
open ../results/index.html

---

🤝 Contributing

What Makes a Good Variant?

• ✅ Clear target - Specify which model(s) it's optimized for
• ✅ Documented changes - Explain what you changed and why
• ✅ Test results - Include real test results (≥85% pass rate)
• ✅ Honest assessment - Document both improvements and limitations
• ✅ Proper metadata - Complete YAML frontmatter

Promoting a Variant to Default

A variant can become the new default if it:

1. Shows significant improvement in test results
2. Works reliably across multiple models
3. Has been tested by multiple contributors
4. Doesn't introduce new critical issues
5. Maintains ≥95% pass rate on core tests

Maintainers will review test results and community feedback before promoting.

---

📚 Related Documentation

• Main Prompts README - Prompt library overview
• Eval Framework Guide - How to run tests
• Test Suite Validation - Creating test suites
• Contributing Guide - Contribution guidelines

---

🆘 Troubleshooting

Variant Not Found

# List available variants
ls .opencode/prompts/openagent/*.md

# Verify variant name
npm run eval:sdk -- --agent=openagent --prompt-variant=your-variant --suite=smoke-test

Tests Failing

1. Check variant metadata (YAML frontmatter)
2. Verify recommended model is available
3. Run with debug: npm run eval:sdk -- --debug
4. Check specific test failures in dashboard

Low Pass Rate

• Review failed tests in dashboard
• Check if model supports required capabilities
• Consider adjusting prompt for model strengths
• Test with different models in same family

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💡 Tips

• Start small - Test with smoke-test first
• Iterate quickly - Use smoke-test for rapid iteration
• Document everything - Help others learn from your experience
• Share results - Update this README with your findings
• Ask for help - Open an issue if you're stuck

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Questions? See main README or open an issue.