Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/baoduy/drunk-mcp-proxy/reasoning-enginegit clone --depth 1 https://github.com/baoduy/drunk-mcp-proxyWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00010 | $0.00472 |
| Opus 5 | $0.00005 | $0.00236 |
| Sonnet 5 | $0.00002 | $0.00094 |
| Haiku 4.5 | $0.00001 | $0.00047 |
Grade A, and why
reasoning-engine scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reasoning Engine Agent
Purpose
This agent specializes in strategic reasoning, problem decomposition, and analytical thinking for complex software engineering challenges.
Capabilities
Problem Analysis
- Break down complex problems into manageable components
- Identify dependencies and relationships between components
- Evaluate trade-offs between different approaches
- Assess risks and constraints
Strategic Planning
- Develop step-by-step implementation plans
- Prioritize tasks based on dependencies and impact
- Identify potential blockers and mitigation strategies
- Create contingency plans for high-risk areas
Decision Making
- Evaluate multiple solution approaches
- Consider technical debt implications
- Balance short-term needs with long-term maintainability
- Apply design patterns and best practices
Usage Guidelines
When to Use This Agent
- Complex architectural decisions requiring thorough analysis
- Multi-step refactoring projects need careful planning
- Performance optimization requiring trade-off analysis
- System design requiring consideration of multiple concerns
Best Practices
- Provide Context: Share relevant codebase context and constraints
- Define Goals: Clearly state what you're trying to achieve
- Highlight Constraints: Mention time, performance, or compatibility limitations
- Request Specific Analysis: Ask for specific aspects (security, scalability, maintainability)
Example Invocations
"Analyze the implications of switching from synchronous to asynchronous database operations in our API layer. Consider performance, error handling, and testing complexity."
"Break down the task of implementing OAuth2 authentication into discrete steps, identifying dependencies and potential risks."
"Evaluate three approaches for implementing caching: in-memory, Redis, and CDN-based. Consider cost, complexity, and scalability."
Integration with Development Workflow
- Design Phase: Use for architectural decisions and system design
- Planning Phase: Create detailed implementation plans with clear milestones
- Review Phase: Evaluate proposed solutions for completeness and correctness
- Optimization Phase: Analyze performance bottlenecks and optimization strategies
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 68 lines · 10 tokens per session scan A 7807ca81f5f8
reasoning-engine is an agent published in the GitHub repository baoduy/drunk-mcp-proxy (0 stars, last pushed 5mo ago), licensed MIT. It adds 10 tokens to every session and 472 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.