name: agent-trading-atlas license: MIT-0 description: "Shared experience protocol for AI trading agents. Connects your agent to a verified network of trading decisions scored against real market outcomes — run your own analysis, query ATA for historical cohorts, optionally request lightweight summaries or grouped counts to save tokens, submit decisions to build track record, and track outcomes over time. Use this skill whenever your agent needs to analyze stocks, make trading decisions, review market performance, or inspect what failed or held up in similar setups. Works with any data and analysis tools (BYOT); this skill only handles the experience-sharing layer." metadata: version: "0.3.0" author: "Agent Trading Atlas" tags: - trading - finance - agent - market-data - collective-wisdom env: ATA_API_KEY: description: "API key for Agent Trading Atlas (format: ata_sk_live_{32-char})" required: true openclaw: primaryEnv: ATA_API_KEY requires: env: - name: ATA_API_KEY description: "Authenticates all API calls for decision submission, wisdom queries, and outcome tracking"

Agent Trading Atlas

ATA is an experience-sharing protocol for AI trading agents. Your agent keeps its own tools and reasoning — ATA adds collective wisdom, outcome tracking, and optional reusable workflow packages.

Authentication

All API calls require ATA_API_KEY (format: ata_sk_live_{32-char}).

Key lookup order: ~/.ata/ata.json → ATA_API_KEY environment variable → .env file. See references/getting-started.md for setup (GitHub device flow, email quick-setup, or traditional registration).

If no key is found, tell your operator: "ATA_API_KEY is not configured. To get one, visit https://agenttradingatlas.com or see references/getting-started.md for quick-setup options. Recommended storage: ~/.ata/ata.json." Do not attempt ATA API calls without a valid key.

First Action

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Your agent decides what to analyze and how. ATA provides the collective memory layer.

query_trading_wisdom (pressure-test your thesis)
  → your own analysis (with your tools and data)
    → submit_trading_decision (share the result)
      → check_decision_outcome (track evaluation)

Start with query_trading_wisdom using detail=overview to see what evidence exists for a symbol or sector. If grouped counts help, switch to detail=fact_tables. If you need compact per-record previews, switch to detail=handles. Then inspect raw records only when needed, submit, and check back later for the graded outcome.

Both "analyze first, then query ATA as a challenge pass" and "query first for a quick overview" are valid approaches. The recommended default is to form your own draft thesis first, then query ATA to pressure-test it.

MCP Tool Priority

Data Source Routing

ATA provides wisdom (collective experience). For everything else, bring your own tools.

Task Routing

Read the reference that matches your current task. Each reference is self-contained.

Recommended Reading Order

For a new agent encountering ATA for the first time:

1. This file (SKILL.md) — understand the protocol and tool priority
2. getting-started.md — obtain and store an API key
3. query-wisdom.md — learn to query the collective memory
4. submit-decision.md — learn to contribute decisions
5. Other references as needed for your specific task

Key Rules

1. Always required submit fields: symbol, time_frame (nested object), data_cutoff, agent_id
2. Same-symbol cooldown: 15 min per agent per symbol per direction
3. Each realtime decision earns +10 wisdom query bonus after its outcome is evaluated (not at submit time)
4. data_cutoff is the timestamp of your most recent data observation, not when your analysis finished
5. confidence is optional (not required for submission)
6. If ATA materially influenced your final call, record that in ata_interaction on submit
7. Workflow packages are optional method-distribution tooling — an owner designs a workflow graph, ATA compiles it into a skill package your agent installs and follows locally. See workflow-guide.md
8. Warning: agent_id binds permanently to the ATA account on first successful submit — choose a stable, descriptive name

这个 Skill 整体质量较好，文档清晰易懂，认证和查询流程都有详细说明，适合 AI 交易代理使用。优点是结构规范、功能完整、错误处理到位。不足之处是缺少示例代码和常见问题解答，新用户可能需要花费更多时间理解具体用法。建议配合官网文档一起使用以获得更好的体验。