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 MadAppGang/claude-code --skill tanstack-querygit clone --depth 1 https://github.com/MadAppGang/claude-codeWrote 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/madappgang/claude-code/tanstack-query)<a href="https://agentmods.dev/skills/madappgang/claude-code/tanstack-query"><img src="https://agentmods.dev/badge/skills/madappgang/claude-code/tanstack-query/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/madappgang/claude-code/tanstack-query"><img src="https://agentmods.dev/badge/skills/madappgang/claude-code/tanstack-query.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 | $0.00061 | $0.08936 |
| Opus 5.5 | $0.00024 | $0.03574 |
| Sonnet 5.5 | $0.00012 | $0.01787 |
| Haiku 4.5 | $0.00006 | $0.00894 |
Grade A, and why
tanstack-query scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
const response = await axios.get('/api/todos', { params: { filters } }) How it starts
The opening of the file, as written. The whole thing — 1,381 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TanStack Query v5 - Complete Guide
TanStack Query v5 (October 2023) is the async state manager for this project. It requires React 18+, features first-class Suspense support, improved TypeScript inference, and a 20% smaller bundle. This section covers production-ready patterns based on official documentation and community best practices.
Breaking Changes in v5
Key updates you need to know:
-
Single Object Signature: All hooks now accept one configuration object:
// ✅ v5 - single object useQuery({ queryKey, queryFn, ...options }) // ❌ v4 - multiple overloads (deprecated) useQuery(queryKey, queryFn, options) -
Renamed Options:
cacheTime→gcTime(garbage collection time)keepPreviousData→placeholderData: keepPreviousDataisLoadingnow meansisPending && isFetching
-
Callbacks Removed from useQuery:
onSuccess,onError,onSettledremoved fromuseQuery- Use global QueryCache callbacks instead
- Prevents duplicate executions
-
Infinite Queries Require initialPageParam:
- No default value provided
- Must explicitly set
initialPageParam(e.g.,0ornull)
-
First-Class Suspense:
- New dedicated hooks:
useSuspenseQuery,useSuspenseInfiniteQuery - No experimental flag needed
- Data is never undefined at type level
- New dedicated hooks:
Migration: Use the official codemod for automatic migration: npx @tanstack/query-codemods v5/replace-import-specifier
Smart Defaults
Query v5 ships with production-ready defaults:
{
staleTime: 0, // Data instantly stale (refetch on mount)
gcTime: 5 * 60_000, // Keep unused cache for 5 minutes
retry: 3, // 3 retries with exponential backoff
refetchOnWindowFocus: true,// Refetch when user returns to tab
refetchOnReconnect: true, // Refetch when network reconnects
}
Philosophy: React Query is an async state manager, not a data fetcher. You provide the Promise; Query manages caching, background updates, and synchronization.
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 · 1,381 lines · 61 tokens per session scan A c9bf27ac35e7
tanstack-query is a skill published in the GitHub repository MadAppGang/claude-code (286 stars, last pushed 7mo ago), licensed MIT. It adds 61 tokens to every session and 8,936 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…