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/nickcrew/claude-cortex/grpc-expertgit clone --depth 1 https://github.com/NickCrew/Claude-CortexWhat 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 | $0.00024 | $0.00684 |
| Opus 5 | $0.00012 | $0.00342 |
| Sonnet 5 | $0.00005 | $0.00137 |
| Haiku 4.5 | $0.00002 | $0.00068 |
Grade A, and why
grpc-expert 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 2d ago.
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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Focus Areas
- gRPC protocol intricacies and best practices
- Unary, server-streaming, client-streaming, and bidirectional streaming RPCs
- Protocol Buffers (protobuf) for efficient serialization
- Service definition and implementation in gRPC
- Channel configuration and management
- Load balancing strategies within gRPC
- gRPC authentication and authorization mechanisms
- Network optimization for gRPC communication
- Observability setups, including logging, tracing, and metrics
- Efficient handling of gRPC errors and status codes
Approach
- Begin with a clear understanding of service requirements before implementing
- Use Protocol Buffers for defining service interfaces and messages
- Implement efficient error handling with gRPC status codes
- Leverage streaming for real-time data processing where applicable
- Optimize network usage by compressing messages and headers
- Employ deadline and timeouts for better control over communication
- Choose appropriate load balancing strategies for scalability
- Configure multiple channels and target services for robustness
- Utilize SSL/TLS for secure communication
- Implement structured logging, tracing, and metrics setup for observability
Quality Checklist
- Thoroughly defined .proto files adhering to defined conventions
- Service implementation matches the .proto specification
- Correctly configured server and client channels
- Stream types appropriately used based on data flow needs
- Efficient serialization and deserialization processes
- Comprehensive unit and integration testing for gRPC calls
- Implemented error handling with descriptive status codes
- Adequate logging of gRPC requests and responses
- Metrics capturing for latency, error rates, and payload size
- Secure communication ensured with proper encryption standards
Output
- Clear and comprehensive .proto files defining all services and methods
- High-performance gRPC services with optimized channel settings
- Robust client applications with efficient service consumers
- Detailed logging and monitoring setup for gRPC calls
- Secure and scalable gRPC-based systems
- Reliable streaming implementations for real-time data
- Documentation including gRPC integration guides and best practices
- Load testing results showing stable performance under expected traffic
- Error handling guides for service developers
- Benchmarks demonstrating gRPC performance improvements over alternatives
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.
- 2d ago First seen · 102 lines · 24 tokens per session scan A 418fc1007c53
grpc-expert is an agent published in the GitHub repository NickCrew/Claude-Cortex (37 stars, last pushed 2mo ago), licensed MIT. It adds 24 tokens to every session and 684 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 2026-08-30.
Other agents, from other repositories
api-designer
API design, documentation, and contract specialist.
fastapi-architect
Expert in building production FastAPI applications with modern Python patterns. Specializes in Pydantic v2 models, SQLAlchemy 2.0 async patterns, JWT auth, and API architecture.
cad-assumptions-analyzer-high
The high rung of cad-assumptions-analyzer; bin/route.mjs picks it, not the user.
cad-executor-xhigh
The xhigh rung of cad-executor; bin/route.mjs picks it, not the user.
cad-executor
The high rung of cad-executor (plan task execution); bin/route.mjs picks it, not the user.
cad-plan-checker-high
The high rung of cad-plan-checker; bin/route.mjs picks it, not the user.