zeta-data-source — community zeta-data-source, zetaterminal, community, ide skills

v1.0.0

关于此技能

非常适合需要全面内容分析和通过REST、aiohttp和限速与多个数据源集成的数据密集型AI代理。 Integrate a new external data source (market data, API, feed) following zetaterminal async patterns. Use when connecting to new APIs, adding data providers, or building data ingestion pipelines.

russiankendricklamar russiankendricklamar
[2]
[0]
更新于: 3/14/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 7/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
7/11
Quality Score
33
Canonical Locale
ru
Detected Body Locale
ru

非常适合需要全面内容分析和通过REST、aiohttp和限速与多个数据源集成的数据密集型AI代理。 Integrate a new external data source (market data, API, feed) following zetaterminal async patterns. Use when connecting to new APIs, adding data providers, or building data ingestion pipelines.

核心价值

赋予代理集成新数据源的能力,使用REST、aiohttp和限速,提供与公共API、认证服务和类似yfinance的库的无缝交互,同时支持RuData/Interfax等服务的限速。

适用 Agent 类型

非常适合需要全面内容分析和通过REST、aiohttp和限速与多个数据源集成的数据密集型AI代理。

赋予的主要能力 · zeta-data-source

使用yfinance库从雅虎金融获取金融数据
通过REST和aiohttp从MOEX ISS获取数据
处理RuData/Interfax的限速请求,具有认证访问

! 使用限制与门槛

  • 需要Python环境
  • 依赖于外部数据源的可用性和认证
  • 某些数据源(如RuData/Interfax)存在限速约束

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

评审后的下一步

先决定动作,再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”,而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核,以及是否已经进入工作流落地阶段。

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

zeta-data-source 是什么?

非常适合需要全面内容分析和通过REST、aiohttp和限速与多个数据源集成的数据密集型AI代理。 Integrate a new external data source (market data, API, feed) following zetaterminal async patterns. Use when connecting to new APIs, adding data providers, or building data ingestion pipelines.

如何安装 zeta-data-source?

运行命令:npx killer-skills add russiankendricklamar/zetaterminal/zeta-data-source。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

zeta-data-source 适用于哪些场景?

典型场景包括:使用yfinance库从雅虎金融获取金融数据、通过REST和aiohttp从MOEX ISS获取数据、处理RuData/Interfax的限速请求,具有认证访问。

zeta-data-source 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

zeta-data-source 有哪些限制?

需要Python环境;依赖于外部数据源的可用性和认证;某些数据源(如RuData/Interfax)存在限速约束。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add russiankendricklamar/zetaterminal/zeta-data-source。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    zeta-data-source 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

zeta-data-source

安装 zeta-data-source,这是一款面向AI agent workflows and automation的 AI Agent Skill。查看评审结论、使用场景与安装路径。

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Zeta Terminal — New Data Source Integration

Existing Sources (reference patterns)

SourceFilePattern
MOEX ISSservices/zcyc_service.pyREST + aiohttp, public API, no auth
RuData/Interfaxservices/rudata_service.pyREST + rate limiting (5 req/s), auth token
Yahoo Financeservices/spectral_regime_service.pyyfinance library, sync wrapped in to_thread

Service Template

backend/src/services/{source}_service.py:

python
1""" 2{Source Name} data service. 3 4API docs: {url} 5Rate limits: {X req/s} 6Auth: {method} 7""" 8import logging 9import aiohttp 10from typing import Any 11 12logger = logging.getLogger(__name__) 13 14# Constants 15BASE_URL = "https://api.example.com" 16TIMEOUT = aiohttp.ClientTimeout(total=30) 17MAX_RETRIES = 3 18 19 20class {Source}Service: 21 """Async client for {Source} API.""" 22 23 def __init__(self, api_key: str | None = None): 24 self.api_key = api_key 25 self._session: aiohttp.ClientSession | None = None 26 27 async def _get_session(self) -> aiohttp.ClientSession: 28 if self._session is None or self._session.closed: 29 headers = {"Content-Type": "application/json"} 30 if self.api_key: 31 headers["Authorization"] = f"Bearer {self.api_key}" 32 self._session = aiohttp.ClientSession( 33 base_url=BASE_URL, 34 headers=headers, 35 timeout=TIMEOUT, 36 ) 37 return self._session 38 39 async def fetch_data(self, params: dict[str, Any]) -> dict[str, Any]: 40 """ 41 Fetch data from {Source}. 42 43 Parameters 44 ---------- 45 params : dict 46 Query parameters. 47 48 Returns 49 ------- 50 dict with keys: data, metadata 51 52 Raises 53 ------ 54 ValueError 55 If API returns an error or invalid data. 56 RuntimeError 57 If connection fails after retries. 58 """ 59 session = await self._get_session() 60 61 for attempt in range(MAX_RETRIES): 62 try: 63 async with session.get("/endpoint", params=params) as resp: 64 if resp.status != 200: 65 text = await resp.text() 66 logger.error("{Source} API error %d: %s", resp.status, text) 67 if resp.status == 429: # Rate limited 68 await asyncio.sleep(2 ** attempt) 69 continue 70 raise ValueError(f"{Source} API returned {resp.status}") 71 72 data = await resp.json() 73 return self._parse_response(data) 74 except aiohttp.ClientError as e: 75 logger.error("{Source} connection error (attempt %d): %s", attempt + 1, e) 76 if attempt == MAX_RETRIES - 1: 77 raise RuntimeError(f"{Source} unavailable after {MAX_RETRIES} retries") from e 78 79 def _parse_response(self, raw: dict) -> dict[str, Any]: 80 """Parse and validate API response.""" 81 # Transform to standard format 82 return { 83 "data": raw, 84 "metadata": { 85 "source": "{source}", 86 "timestamp": datetime.now(datetime.UTC).isoformat(), 87 } 88 } 89 90 async def close(self): 91 if self._session and not self._session.closed: 92 await self._session.close()

Router Pattern

python
1@router.get("/api/{source}/fetch") 2async def fetch_data( 3 param: str = Query(..., description="Query parameter"), 4): 5 service = {Source}Service(api_key=os.environ.get("{SOURCE}_API_KEY")) 6 try: 7 result = await service.fetch_data({"param": param}) 8 return result 9 except ValueError as e: 10 raise HTTPException(status_code=400, detail=str(e)) from e 11 except RuntimeError as e: 12 raise HTTPException(status_code=503, detail="Data source unavailable") from e 13 finally: 14 await service.close()

Data Source Priorities (Aladdin Data Platform roadmap)

SourceDataPriority
CBR (ЦБ РФ)Ключевая ставка, инфляция, денежная массаP1
FREDUS macro (GDP, CPI, Fed Funds Rate)P1
BinanceCrypto prices, order bookP2
MOEX ISS (расширение)Акции, фьючерсы, индексыP1
FinamИсторические данные российских акцийP2

Checklist Before Done

  • Async client with aiohttp (not requests)
  • Retry logic with exponential backoff
  • Rate limiting respected (429 handling)
  • API key from os.environ, never hardcoded
  • Timeout configured (aiohttp.ClientTimeout)
  • Session cleanup (close() method)
  • Response parsed into standard format (data + metadata)
  • Router with proper error handling (400/503)
  • API docs URL in docstring
  • ruff check passes

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