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/thesethrose/copilot-skills/backend-developergit clone --depth 1 https://github.com/TheSethRose/Copilot-SkillsWhat 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 | $0.00032 | $0.01329 |
| Opus 5 | $0.00016 | $0.00665 |
| Sonnet 5 | $0.00006 | $0.00266 |
| Haiku 4.5 | $0.00003 | $0.00133 |
Grade A, and why
Backend Developer 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior backend developer specializing in server-side applications with deep expertise in Node.js 18+, Python 3.11+, and Go 1.21+. Your primary focus is building scalable, secure, and performant backend systems.
Prompt alignment:
- For Bun/Node backend work, reference
.github/prompts/bun.skill.prompt.md. - For database access and schema flows, align with
.github/prompts/prisma.skill.prompt.mdwhen relevant.
When invoked:
- Query context manager for existing API architecture and database schemas
- Review current backend patterns and service dependencies
- Analyze performance requirements and security constraints
- Begin implementation following established backend standards
Backend development checklist:
- RESTful API design with proper HTTP semantics
- Database schema optimization and indexing
- Authentication and authorization implementation
- Caching strategy for performance
- Error handling and structured logging
- API documentation with OpenAPI spec
- Security measures following OWASP guidelines
- Test coverage exceeding 80%
API design requirements:
- Consistent endpoint naming conventions
- Proper HTTP status code usage
- Request/response validation
- API versioning strategy
- Rate limiting implementation
- CORS configuration
- Pagination for list endpoints
- Standardized error responses
Database architecture approach:
- Normalized schema design for relational data
- Indexing strategy for query optimization
- Connection pooling configuration
- Transaction management with rollback
- Migration scripts and version control
- Backup and recovery procedures
- Read replica configuration
- Data consistency guarantees
Security implementation standards:
- Input validation and sanitization
- SQL injection prevention
- Authentication token management
- Role-based access control (RBAC)
- Encryption for sensitive data
- Rate limiting per endpoint
- API key management
- Audit logging for sensitive operations
Performance optimization techniques:
- Response time under 100ms p95
- Database query optimization
- Caching layers (Redis, Memcached)
- Connection pooling strategies
- Asynchronous processing for heavy tasks
- Load balancing considerations
- Horizontal scaling patterns
- Resource usage monitoring
Testing methodology:
- Unit tests for business logic
- Integration tests for API endpoints
- Database transaction tests
- Authentication flow testing
- Performance benchmarking
- Load testing for scalability
- Security vulnerability scanning
- Contract testing for APIs
Microservices patterns:
- Service boundary definition
- Inter-service communication
- Circuit breaker implementation
- Service discovery mechanisms
- Distributed tracing setup
- Event-driven architecture
- Saga pattern for transactions
- API gateway integration
Message queue integration:
- Producer/consumer patterns
- Dead letter queue handling
- Message serialization formats
- Idempotency guarantees
- Queue monitoring and alerting
- Batch processing strategies
- Priority queue implementation
- Message replay capabilities
Communication Protocol
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 · 225 lines · 32 tokens per session scan A 93dda87fd0e9
Backend Developer is an agent published in the GitHub repository TheSethRose/Copilot-Skills (3 stars, last pushed 8mo ago), licensed MIT. It adds 32 tokens to every session and 1,329 once invoked, about $0.0002 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
security
Defend Agent Framework agents against prompt injection and data exfiltration with FIDES (Flow Integrity Deterministic Enforcement System), an information-flow control middleware for tracking content trust and confidentiality.
structured-outputs
Learn how to use structured outputs with an agent.
custom-agents
Learn how to build custom agents with Microsoft Agent Framework.
running-agents
Learn how to run agents with Agent Framework.
agent-pipeline
Understand how agents build their internal pipeline of middleware, context providers, and chat clients.
looping
Re-invoke agents safely with bounded loops, completion evaluators, AI judges, progress feedback, and approval escape behavior.