| schemaVersion | 2026-04-11T00:00:00.000Z |
| skillId | backend/graphql-api-implementation |
| name | graphql-api-implementation |
| displayName | GraphQL API Implementation |
| description | Use when working on GraphQL schemas, resolvers, mutations, subscriptions, and dataloader patterns. Focus on schema clarity, N+1 avoidance, auth boundaries, and client ergonomics. |
| aliases | ["graphql-api-implementation","GraphQL API Implementation","graphqlapiimplementation","GraphQL API","resolver","dataloader","N+1 查询","GraphQL","schema","mutation","服务接口","implementation","Graph","QL","服务端","server side","验证"] |
| version | 0.1.0 |
| sourceHash | sha256:8d2f99fde0531969ac50bb034abca5289c6b120837c0a8cd7fe18c798f6a6512 |
| domain | backend |
| departmentTags | ["backend-platform"] |
| sceneTags | ["architecture","test"] |
GraphQL API Implementation
Use this skill when the task involves GraphQL schemas, resolvers, mutations, subscriptions, and dataloader patterns.
Goal: produce reliable engineering guidance and implementation steps focused on schema clarity, N+1 avoidance, auth boundaries, and client ergonomics.
Working model
- Identify the affected system, data, users, and failure modes.
- Define invariants, inputs, outputs, ownership, and rollback needs.
- Prefer small, auditable changes with explicit validation.
- Call out security, performance, concurrency, and data-loss risks when relevant.
- Finish with concrete verification steps and residual risks.
Rules
- Ground recommendations in the current codebase or runtime evidence.
- Prefer explicit contracts, typed boundaries, and defensive validation.
- Do not hide operational concerns behind generic best practices.
- Include negative cases, edge cases, and failure behavior.
- For review tasks, list findings first with file and line references when possible.
- For test or performance tasks, define the workload, success criteria, and measurement method.
Checklist
- Are assumptions and ownership boundaries explicit?
- Are risky changes reversible or safely deployable?
- Are observability and diagnostics sufficient for production issues?
- Are tests or validation steps targeted to the actual risk?
- Are security and data-integrity concerns addressed?