longsizhuo/openinvest/invest
Claude Code and agent skill by longsizhuo/openinvest
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- ext:[{"type":"plugin","description":"Custom plugin integration"}]
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openInvest multi-asset AI investment committee — **daily use**. Read portfolio / live prices / strategy / decision history / adjust positions / run a 4-role LLM committee for an investment verdict. Supports any yfinance symbol (A-share `.SS`/`.SZ` / HK / US / ETF / crypto / commodities), China off-exchange funds (`FUND:<code>`) and any currency. **Two paths** — (1) Coordinator, Claude Code spawns 4 subagents, saves DeepSeek tokens; (2) Direct, any agent (Codex / Hermes / OpenClaw / Cursor / Cli…
Install
npx vskill@latest install longsizhuo/openinvest/investSemi-extensibleMentions customization, but needs skill-specific knowledge. Read the extensible skills standard
SKILL.md
Invest Skill
<skill-dir>is this skill's own directory — the folder that holds this SKILL.md (Claude Codeshows it as the skill's base directory; Hermes / Codex / OpenClaw install it inside the plugin).run.shbelow always means<skill-dir>/scripts/run.sh.
First-time fork users: run the invest-setup skill first to initialize. The main flow lives in this skill (the AI agent uses the CLI/MCP to view the portfolio / run the committee / replay decision history). The Web GUI has been retired (2026-07) — every capability is exposed via CLI subcommands / MCP tools. The backend is distributed from PyPI (pulled on demand via uvx) at the version pinned in run.sh, which moves with each plugin release: updating the plugin updates the backend. run.sh update re-downloads the pinned version if the uv cache breaks.
openInvest multi-asset AI investment committee. This skill is not Claude-exclusive — any agent that can run shell commands can use it; see "Choosing a path" below.
Reply in the language the user is currently speaking unless they explicitly ask to switch languages.
Choosing a path
The first question is not "which brand of agent are you" — it is "is a human present for this invocation":
Why unattended runs always go Direct, even with delegation capability: the Coordinator protocol depends on you improvising "which tool to call and how to assemble the prompt". That is fine normally, but on 2026-07-14 a real Hermes cron ran Coordinator unattended — it did not faithfully call the delegation tool per the protocol, picked its own route instead, then hit the "unattended cron cannot approve dangerous commands" safety gate and stalled. Direct is pure deterministic Python code and never improvises. Saving the small hassle of configuring a key is not worth a probabilistic hang / derailment in unattended scenarios — a bad trade.
| Scenario | Who you are | Path | What to run | Credentials | |
|---|---|---|---|---|---|
| Interactive (user present) | Claude Code (has the Agent({...}) tool) | Coordinator | prepare_committee → spawn 4 subagents → save_committee | No key needed | |
| Interactive (user present) | Hermes (has the delegate_task tool) | Coordinator (Hermes variant) | Same as above; spawn syntax becomes delegate_task(tasks=[...]) | No key needed | |
| Interactive, no delegation capability | Codex / OpenClaw / Cursor / Cline / plain script | Direct | run_committee <SYMBOL> one-shot | Needs LLM_API_KEY | |
| cron / unattended, any agent | Anyone, with or without delegation capability | Direct | run_committee / daily_report | Needs LLM_API_KEY |
All three paths share the same foundation — the same prompts, the same data preparation (regime classification + probability framing + deterministic fact block), and the same on-disk format (memory/.committee/<date>/<asset>.md; both Coordinator variants must call save_committee with --provider <your-brand> so the transcript is not mislabeled as claude, and --model <the model that plays the 4 roles>, e.g. claude-haiku-5-5 — omitted is recorded as unknown, and the scorecard buckets verdicts per <provider>:<model>, so a model swap never mixes old and new track records). The only difference is "who plays the 4 LLM roles": on Coordinator you play them yourself (the model the user already subscribes to); on Direct the configured LLM (billed per token) plays them. Verdicts may differ (different models — useful for cross-validation).
`LLM_API_KEY` is not DeepSeek-only, and the cost is near zero: utils/llm.py works with any OpenAI-compatible endpoint (three envs: LLM_API_KEY/LLM_BASE_URL/LLM_MODEL; DEEPSEEK_* remains supported for compatibility). Want zero cost → providers with free tiers right now (Qwen / Zhipu / MiMo etc.) all work (free-tier terms change — verify before use); don't want the hassle → just use DeepSeek: at daily-report volume (a few assets/day) roughly ¥0.01-0.03 per run, under ¥2 a month. Do not bet the reliability of unattended runs on Coordinator just to save that much.
No sub-task delegation capability and no key configured: do not force your way through Coordinator, do not fabricate a verdict, and do not write code to brute-force around it — just call run_committee (you will get a clear error) and honestly tell the user "LLM_API_KEY needs to be configured".
Decision tree (identical up front, whichever path you take)
Coordinator path details (Claude Code only)
Read references/committee-protocol.md and follow it strictly. Full 6 stages:
This is the start of the file. Read the full SKILL.md on GitHub.
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Source
- SKILL.md in the repositoryCheckingplugin/skills/invest/SKILL.md
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Extension points
pluginCustom plugin integration
Works with
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