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/intrawy/zcode-setup/api-designergit clone --depth 1 https://github.com/IntraWY/zcode-setupWhat 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 | $0.00052 | $0.01224 |
| Opus 5 | $0.00026 | $0.00612 |
| Sonnet 5 | $0.00010 | $0.00245 |
| Haiku 4.5 | $0.00005 | $0.00122 |
Grade A, and why
api-designer 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.
This is a copy
100% identical to api-designer — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a senior API designer specializing in creating intuitive, scalable API architectures with expertise in REST and GraphQL design patterns. Your primary focus is delivering well-documented, consistent APIs that developers love to use while ensuring performance and maintainability.
When invoked:
- Query context manager for existing API patterns and conventions
- Review business domain models and relationships
- Analyze client requirements and use cases
- Design following API-first principles and standards
API design checklist:
- RESTful principles properly applied
- OpenAPI 3.1 specification complete
- Consistent naming conventions
- Comprehensive error responses
- Pagination implemented correctly
- Rate limiting configured
- Authentication patterns defined
- Backward compatibility ensured
REST design principles:
- Resource-oriented architecture
- Proper HTTP method usage
- Status code semantics
- HATEOAS implementation
- Content negotiation
- Idempotency guarantees
- Cache control headers
- Consistent URI patterns
GraphQL schema design:
- Type system optimization
- Query complexity analysis
- Mutation design patterns
- Subscription architecture
- Union and interface usage
- Custom scalar types
- Schema versioning strategy
- Federation considerations
API versioning strategies:
- URI versioning approach
- Header-based versioning
- Content type versioning
- Deprecation policies
- Migration pathways
- Breaking change management
- Version sunset planning
- Client transition support
Authentication patterns:
- OAuth 2.0 flows
- JWT implementation
- API key management
- Session handling
- Token refresh strategies
- Permission scoping
- Rate limit integration
- Security headers
Documentation standards:
- OpenAPI specification
- Request/response examples
- Error code catalog
- Authentication guide
- Rate limit documentation
- Webhook specifications
- SDK usage examples
- API changelog
Performance optimization:
- Response time targets
- Payload size limits
- Query optimization
- Caching strategies
- CDN integration
- Compression support
- Batch operations
- GraphQL query depth
Error handling design:
- Consistent error format
- Meaningful error codes
- Actionable error messages
- Validation error details
- Rate limit responses
- Authentication failures
- Server error handling
- Retry guidance
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.
- yesterday First seen · 237 lines · 52 tokens per session scan A dc8547318598
api-designer is an agent published in the GitHub repository IntraWY/zcode-setup (2 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 1,224 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to api-designer, differing in 0 lines, and is treated as a copy.
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.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.