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/bdiasti/maestro-bundle-cli/integration-apinpx skills add bdiasti/maestro-bundle-cli --skill integration-apigit clone --depth 1 https://github.com/bdiasti/maestro-bundle-cliWhat 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 | $0.00049 | $0.02025 |
| Opus 5 | $0.00024 | $0.01012 |
| Sonnet 5 | $0.00010 | $0.00405 |
| Haiku 4.5 | $0.00005 | $0.00202 |
Grade A, and why
integration-api 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 2d 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.
export const api = axios.create({ How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API Integration
Connect a React frontend to backend APIs using Axios for HTTP, React Query for caching, and Socket.IO for real-time updates.
When to Use
- User needs to set up an HTTP client with Axios
- User wants to create a service layer for API calls
- User needs React Query hooks for data fetching and mutations
- User wants to add real-time WebSocket updates
- User needs to handle API errors, retries, and loading states
Available Operations
- Configure Axios client with base URL, timeouts, and interceptors
- Create a typed service layer for API endpoints
- Build React Query hooks with cache management
- Set up WebSocket connections with Socket.IO
- Handle error states, retries, and optimistic updates
Multi-Step Workflow
Step 1: Install Dependencies
npm install axios @tanstack/react-query socket.io-client
npm install -D @tanstack/react-query-devtools
Step 2: Configure Axios HTTP Client
// src/lib/api.ts
import axios from 'axios';
export const api = axios.create({
baseURL: import.meta.env.VITE_API_URL || 'http://localhost:8000/api/v1',
timeout: 10000,
headers: {
'Content-Type': 'application/json',
},
});
// Add auth token to every request
api.interceptors.request.use((config) => {
const token = localStorage.getItem('token');
if (token) {
config.headers.Authorization = `Bearer ${token}`;
}
return config;
});
// Unwrap response data and handle errors
api.interceptors.response.use(
(response) => response.data,
(error) => {
if (error.response?.status === 401) {
localStorage.removeItem('token');
window.location.href = '/login';
}
return Promise.reject(error.response?.data || error);
}
);
Add the API URL to your environment:
# .env.local
VITE_API_URL=http://localhost:8000/api/v1
Step 3: Create Typed Service Layer
// src/services/itemApi.ts
import { api } from '@/lib/api';
export interface Item {
id: string;
title: string;
description: string;
status: 'pending' | 'in_progress' | 'completed';
priority: 'low' | 'medium' | 'high';
createdAt: string;
}
export interface PaginatedResponse<T> {
items: T[];
total: number;
page: number;
size: number;
}
export interface CreateItemDto {
title: string;
description: string;
priority: 'low' | 'medium' | 'high';
}
export const itemApi = {
list: (params?: { page?: number; size?: number; status?: string }) =>
api.get<PaginatedResponse<Item>>('/items', { params }),
get: (id: string) =>
api.get<Item>(`/items/${id}`),
create: (data: CreateItemDto) =>
api.post<Item>('/items', data),
update: (id: string, data: Partial<CreateItemDto>) =>
api.put<Item>(`/items/${id}`, data),
delete: (id: string) =>
api.delete(`/items/${id}`),
};
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2d ago First seen · 286 lines · 49 tokens per session scan A b22602b07349
integration-api is a skill published in the GitHub repository bdiasti/maestro-bundle-cli (21 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 2,025 once invoked, about $0.0002 per session on Opus 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
agent-host-chat-contributions
Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.