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 skills add h4vzz/awesome-ai-agent-skills --skill graphql-api-designgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/graphql-api-design)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/graphql-api-design"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/graphql-api-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/graphql-api-design"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/graphql-api-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00029 | $0.03096 |
| Opus 5 | $0.00015 | $0.01548 |
| Sonnet 5 | $0.00006 | $0.00619 |
| Haiku 4.5 | $0.00003 | $0.00310 |
Grade A, and why
graphql-api-design 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 11d 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.
This is a copy
97% identical to graphql-api-design — 2 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 — 349 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GraphQL API Design
This skill enables an AI agent to design complete GraphQL APIs from specifications, schemas, or natural language descriptions. The agent produces type definitions, queries, mutations, subscriptions, input types, enums, and resolver implementations. It applies performance patterns including DataLoader for N+1 prevention, cursor-based pagination via the Relay connection spec, query depth limiting, and schema federation for microservice architectures.
Workflow
-
Model the domain as types: Analyze the application domain and define GraphQL object types, input types, enums, interfaces, and unions. Each type should represent a real entity with fields that match the data consumers actually need. Use non-nullable (
!) annotations deliberately—fields that can genuinely be absent should be nullable. Prefer specific scalar types (e.g.,DateTime,URL) over rawStringfor self-documenting schemas. -
Design queries and mutations: Define Query fields for read operations and Mutation fields for write operations. Queries should be noun-based (
user,posts) while mutations should be verb-based (createPost,updateUser). Each mutation should accept a single input type argument and return a payload type that includes the modified object plus any user-facing errors. This pattern keeps mutations consistent and extensible. -
Implement pagination with connections: For any list field that could return many items, use the Relay connection specification with
edges,node,cursor, andpageInfo. This provides cursor-based pagination that is stable under insertions and deletions, unlike offset-based pagination. Define reusable connection types per entity rather than returning raw arrays. -
Write resolvers with DataLoader: Implement resolvers that use DataLoader to batch and cache database lookups within a single request. Without DataLoader, a query that fetches 50 posts and their authors would make 50 separate author queries (the N+1 problem). DataLoader collapses these into a single batched query. Create a new DataLoader instance per request to avoid leaking data between users.
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.
- 11d ago First seen · 349 lines · 29 tokens per session scan A ba466ce67155
graphql-api-design is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed 2d ago), licensed MIT. It adds 29 tokens to every session and 3,096 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to graphql-api-design, differing in 2 lines, and is treated as a copy.
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