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 nimadorostkar/Claude-Skills-collection --skill graphqlgit clone --depth 1 https://github.com/nimadorostkar/Claude-Skills-collectionWrote 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/nimadorostkar/claude-skills-collection/graphql)<a href="https://agentmods.dev/skills/nimadorostkar/claude-skills-collection/graphql"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/graphql/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/nimadorostkar/claude-skills-collection/graphql"><img src="https://agentmods.dev/badge/skills/nimadorostkar/claude-skills-collection/graphql.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.00959 |
| Opus 5 | $0.00016 | $0.00479 |
| Sonnet 5 | $0.00007 | $0.00192 |
| Haiku 4.5 | $0.00003 | $0.00096 |
Grade A, and why
graphql 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.
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GraphQL
Purpose
Design a GraphQL schema that models the domain rather than the database, and operate it without letting a single query take down the service.
When to Use
- Designing a GraphQL schema from scratch.
- Diagnosing slow queries or N+1 resolver behavior.
- Protecting a public GraphQL endpoint from expensive queries.
- Splitting a schema across services with federation.
Capabilities
- Schema design: types, interfaces, unions, connections, nullability.
- Resolver architecture and DataLoader batching.
- Query cost analysis, depth limiting, and persisted queries.
- Error handling that distinguishes partial failures from total ones.
- Federation and schema composition.
Inputs
- The domain model and the client's actual query patterns.
- The data sources behind each field.
- Whether the endpoint is public (untrusted queries) or internal.
Outputs
- A schema with deliberate nullability and stable field names.
- Resolvers that batch, with no N+1 on any documented query.
- Cost limits and a persisted-query allow-list for public endpoints.
Workflow
- Design for the client, not the tables — The schema is a product surface. If it mirrors your database, you have built a slower REST API with worse caching.
- Get nullability right early — A nullable field is a permanent client burden; a non-null field that later fails takes down the whole parent object. Non-null for genuine invariants only.
- Batch every relation — Every resolver that fetches by id gets a DataLoader. Without one,
orders { customer { name } }issues one query per order. - Bound the cost — Depth limit, complexity limit, and pagination caps. Then persisted queries for first-party clients.
- Model errors explicitly — Expected failures (validation, not found) belong in the schema as union results; unexpected failures go to
errors.
Best Practices
- Never expose an unbounded list field. Use the Connection pattern with a
first/aftercap. DataLoaderinstances are per-request. A shared loader is a cache-poisoning bug across users.- Changing a field from nullable to non-null is a breaking change for clients that handle null; the reverse is breaking for clients that do not. Get it right before launch.
- Do not version a GraphQL schema. Add fields, deprecate old ones with
@deprecated(reason:), and remove them once usage reaches zero. - Instrument per-field resolver latency. The slow field is never the one you would guess.
- Introspection on a public production endpoint is a reconnaissance gift. Disable it or restrict it.
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 · 101 lines · 33 tokens per session scan A 33b4366e7db9
graphql is a skill published in the GitHub repository nimadorostkar/Claude-Skills-collection (26 stars, last pushed 24d ago), licensed MIT. It adds 33 tokens to every session and 959 once invoked, about $0.0002 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 2026-08-30.
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