longsizhuo
UserResearch-grade investment decision engine for AI agents: isolated multi-agent committee, auditable verdicts, backtests with lookahead protection, published negative results
Categories
Indexed Skills (4)
invest-backup
Back up / restore openInvest's local state — memory/ (holdings, strategy, user profile, committee records, dream logs) + db/ (trade ledger, job run history, market-data cache) + .env (SMTP/API credentials) + user_profile.json. All of this data is .gitignore'd with no historical versions in git, so a single accidental overwrite (e.g. slipping and running some one-off migration/init script) means real data loss — no git revert available. **Proactive trigger scenarios** — "backup invest data / 备份一下 openInvest 的数据", "my holdings/strategy look wiped / 我的持仓/策略好像被清空了", "invest data is lost / invest 数据丢了", "restore invest backup / 恢复一下 invest 的备份", before any reinstall/migration of the openInvest deployment on this machine, or right before running an unfamiliar migration/init script (back up first, then act).
invest-setup
First-time openInvest installation and onboarding. **ONLY use when** user explicitly says "set up invest" / "init invest" / "帮我初始化 invest", OR when `invest` skill's `doctor` returns `status="needs_setup"`. **NOT for daily usage** — once onboarding is done, the `invest` skill takes over (portfolio viewing, committee analysis, buy/sell tracking). Wraps `run.sh init --from-stdin` with the canonical 5-question flow.
invest
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 / HK / US / ETF / crypto / commodities) and any currency. **Two paths** — (1) Coordinator, Claude Code spawns 4 subagents, saves DeepSeek tokens; (2) Direct, any agent (Codex / Hermes / OpenClaw / Cursor / Cline / plain script) runs `run.sh run_committee <SYM>` for a one-shot verdict. **Trigger scenarios** — "show portfolio / 看看我的持仓", "how is my P&L / 我现在涨了多少", "should I buy/sell X / 该不该买卖X", "analyze X / 分析一下X", "run committee on X / 跑委员会", "track AAPL / 跟踪苹果", "add/trim a position, log a trade / 加仓减仓记一笔". **First-time install uses a separate skill `invest-setup`** (switch to it when `doctor` returns `needs_setup`). Backend — longsizhuo/openInvest.
okf-frontmatter
Maintain openInvest's docs (docs/wiki chapters + docs/wiki/adr) under Google's Open Knowledge Format (OKF). Two jobs. (1) Teach agents to maintain docs the OKF way — every doc carries a small YAML frontmatter block as the single source of truth (type, title, tags, intent, schema_source, documents); schema details link to the authoritative code instead of being copied into prose; no more hand-maintained thousand-line markdown. (2) Look docs up fast — grep the literal term FIRST; only when grep is ambiguous (hits scattered across files / synonym mismatch / zero hits) run find_docs.py to rank the owning doc by frontmatter intent, or resolve a doc's schema_source to the real code. Trigger phrases — "which doc covers X", "find the schema for PortfolioResponse", "where is GET /api/holdings documented", "docs for verdict.risk_profile", "add OKF frontmatter to this doc", "lint the wiki", "scaffold a new ADR/chapter". Run: scripts/run.sh find|schema|index|lint|new (or python3 scripts/find_docs.py --repo <path> ...).
Bio shown is the top-scored skill's repo description as a fallback — real GitHub bios land in a future update.