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 instructions/marcelroozekrans/agenticai/backendgit clone --depth 1 https://github.com/MarcelRoozekrans/AgenticAIWhat 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 | $0.00424 | $0.00424 |
| Opus 5 | $0.00212 | $0.00212 |
| Sonnet 5 | $0.00085 | $0.00085 |
| Haiku 4.5 | $0.00042 | $0.00042 |
Grade A, and why
AgenticAI backend.instructions.md 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.
What it actually says
Backend Development Guidelines
API Design
RESTful Principles
- Use proper HTTP methods (GET, POST, PUT, PATCH, DELETE)
- Return appropriate status codes
- Use plural nouns for resource endpoints
- Version your APIs (e.g.,
/api/v1/) - Implement pagination for list endpoints
Response Format
{
"data": {},
"meta": {
"timestamp": "ISO8601",
"requestId": "uuid"
},
"errors": []
}
Database Operations
Query Best Practices
- Use parameterized queries (prevent SQL injection)
- Implement proper indexing
- Use transactions for multi-step operations
- Optimize N+1 query problems
- Use connection pooling
Data Validation
- Validate all inputs at the API boundary
- Use schema validation libraries
- Sanitize data before storage
- Implement rate limiting
Authentication & Authorization
- Use industry-standard protocols (OAuth2, JWT)
- Implement proper token refresh mechanisms
- Use secure password hashing (bcrypt, argon2)
- Apply principle of least privilege
- Log authentication events
Error Handling
Error Response Format
{
"error": {
"code": "ERROR_CODE",
"message": "Human readable message",
"details": {}
}
}
Logging
- Log all errors with stack traces
- Include request context (user, request ID)
- Use structured logging (JSON format)
- Don't log sensitive data (passwords, tokens)
Performance
- Implement caching strategies (Redis, in-memory)
- Use async operations where appropriate
- Optimize database queries
- Implement connection pooling
- Monitor and set up alerts
Testing
- Write unit tests for business logic
- Write integration tests for API endpoints
- Use mocking for external dependencies
- Test error scenarios and edge cases
- Maintain test data factories
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 · 86 lines · 424 tokens per session scan A bfcf611e0492
AgenticAI backend.instructions.md is an instructions file published in the GitHub repository MarcelRoozekrans/AgenticAI (1 stars, last pushed 4mo ago), licensed MIT. It adds 424 tokens to every session, about $0.0021 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.
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