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 skills/librefang/librefang-registry/graphql-expertnpx skills add librefang/librefang-registry --skill graphql-expertgit clone --depth 1 https://github.com/librefang/librefang-registryWrote 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/librefang/librefang-registry/graphql-expert)<a href="https://agentmods.dev/skills/librefang/librefang-registry/graphql-expert"><img src="https://agentmods.dev/badge/skills/librefang/librefang-registry/graphql-expert.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.00635 |
| Opus 5 | $0.00010 | $0.00318 |
| Sonnet 5 | $0.00004 | $0.00127 |
| Haiku 4.5 | $0.00002 | $0.00064 |
Grade A, and why
graphql-expert 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 4d 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-expert — 3 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GraphQL Expert
A backend API architect with deep expertise in GraphQL schema design, resolver implementation, real-time subscriptions, and query performance optimization. This skill provides guidance for building robust, well-typed GraphQL APIs that scale efficiently while maintaining an excellent developer experience for API consumers.
Key Principles
- Design schemas around the domain model, not the database schema; GraphQL types should represent business concepts with clear relationships
- Use input types for mutations and keep query arguments minimal; complex filtering belongs in dedicated input types
- Prevent the N+1 query problem proactively by implementing DataLoader patterns for every resolver that accesses a data source
- Treat the schema as a contract; use deprecation directives before removing fields and version through additive changes rather than breaking ones
- Enforce query complexity limits and depth restrictions at the server level to prevent abusive or accidentally expensive queries
Techniques
- Define types with clear nullability: non-null (String!) for required fields, nullable for fields that may genuinely be absent
- Implement resolvers that return promises and batch data access; use DataLoader to batch and cache database calls within a single request
- Set up subscriptions over WebSocket (graphql-ws protocol) with proper connection lifecycle handling (init, ack, keep-alive, terminate)
- Use fragments to share field selections across queries and reduce duplication in client-side code
- Apply custom directives (@auth, @deprecated, @cacheControl) for cross-cutting concerns like authorization and cache hints
- Implement cursor-based pagination following the Relay connection specification (edges, nodes, pageInfo with hasNextPage and endCursor)
- Structure error responses with extensions field for error codes and machine-readable metadata alongside human-readable messages
Common Patterns
- Schema Federation: Split a monolithic schema into domain-specific subgraphs that compose into a unified supergraph via a gateway, enabling independent team ownership
- Persisted Queries: Hash and store approved queries server-side; clients send only the hash, reducing bandwidth and preventing arbitrary query execution
- Optimistic UI Updates: Design mutations to return the mutated object so clients can update their local cache immediately without a refetch
- Batch Mutations: Accept arrays in input types for bulk operations while returning per-item results with success/failure status for each entry
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.
- 4d ago First seen · 43 lines · 19 tokens per session scan A ea53b35d0198
graphql-expert is a skill published in the GitHub repository librefang/librefang-registry (11 stars, last pushed 10d ago), licensed MIT. It adds 19 tokens to every session and 635 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-expert, differing in 3 lines, and is treated as a copy.
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