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 j4flmao/agent-skills --skill graphql-optimizationgit clone --depth 1 https://github.com/j4flmao/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/j4flmao/agent-skills/graphql-optimization)<a href="https://agentmods.dev/skills/j4flmao/agent-skills/graphql-optimization"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/graphql-optimization/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/j4flmao/agent-skills/graphql-optimization"><img src="https://agentmods.dev/badge/skills/j4flmao/agent-skills/graphql-optimization.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 | $0.00017 | $0.00308 |
| Opus 5.5 | $0.00007 | $0.00123 |
| Sonnet 5.5 | $0.00003 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
GraphQL Optimization 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 1mo 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.
What it actually says
GraphQL Optimization: Solving N+1
The N+1 Problem
Without optimization, fetching a list of items and their nested relations results in 1 query for the list and N queries for the relations.
Dataloader Solution
Dataloader batches and caches requests to the database.
Diagram
%%{init: {"theme": "default", "flowchart": {"useMaxWidth": true}}}%%
graph TD
A[Resolvers] -->|Individual Requests| B(Dataloader)
B -->|Batched Request| C[(Database)]
C -->|Batched Response| B
B -->|Individual Responses| A
Node.js Example (Dataloader)
const DataLoader = require('dataloader');
const userLoader = new DataLoader(async (userIds) => {
// Single DB query for all users
const users = await db.query('SELECT * FROM users WHERE id IN (?)', [userIds]);
// Ensure the results match the order of userIds
const userMap = users.reduce((acc, user) => ({ ...acc, [user.id]: user }), {});
return userIds.map(id => userMap[id]);
});
// In GraphQL Resolver
const resolvers = {
Post: {
author: (post) => userLoader.load(post.authorId)
}
};
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.
- 1mo ago First seen · 43 lines · 17 tokens per session scan A 2c1b78e2d427
GraphQL Optimization is a skill published in the GitHub repository j4flmao/agent-skills (33 stars, last pushed 10d ago), licensed MIT. It adds 17 tokens to every session and 308 once invoked, about $0.0001 per session on Opus 5.5. 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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