Agent skill

Invest

by longsizhuo in longsizhuo/openInvest

openInvest multi-asset AI investment committee — daily use. An agent skill from longsizhuo/openInvest.

MITAuto-check: notesBusiness, Finance & HR

Install Invest

skills CLI
$ npx skills add longsizhuo/openInvest --skill invest -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install longsizhuo/openInvest invest --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/longsizhuo/openInvest.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/invest .claude/skills/invest && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
invest
GitHub stars
108
Token cost
~5.9k tokens
SKILL.md length
2,457 words
Files
11 (incl. scripts, references)
Repo updated
First seen
Licence
MIT

At a glance

openInvest multi-asset AI investment committee — daily use. An agent skill from longsizhuo/openInvest.

  • Works in 3 steps: explain_decision for the full 4-role… → Combine with status (current portfolio)… → Answer using evidence from the…
  • Scenarios — show portfolio / 看看我的持仓
  • SKILL.md covers Choosing a path, Decision tree (identical up…, Coordinator path details… and Direct path details (any agent), plus 7 more sections
  • Runs Shell scripts from its folder; calls claude; needs LLM_API_KEY and INVEST_API_TOKEN

What it does

Invest is an agent skill from longsizhuo/openInvest. 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 runcommittee <SYM for a one-shot verdict. Trigger…

Its SKILL.md is about 5.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts and reference files (for example `CHANGELOG.md`, `README.md` and `references/adding-assets.md`).

It sits in Business, Finance & HR, covering Stock and market analysis and Subagents. It works with DeepSeek, yfinance and Model Context Protocol. The repository describes itself as: Research-grade investment decision engine for AI agents: isolated multi-agent committee, auditable verdicts, backtests with lookahead protection, published negative results. The licence is MIT.

When your agent uses it

  • 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

Example prompts

  • “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”
  • “/invest”

Requirements

  • Python 3
  • A Bash shell
  • A credential in LLM_API_KEY
  • A credential in DEEPSEEK_API_KEY

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. explain_decision for the full 4-role debate transcript + CIO memo + path snapshot
  2. Combine with status (current portfolio) + GET /api/user (wealth_context) if needed
  3. Answer using evidence from the transcript — do not invent reasons yourself

What it can do on your machine

Read from SKILL.md and the folder at commit 220abd2. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • claude

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • LLM_API_KEY
    • INVEST_API_TOKEN
    • DEEPSEEK_API_KEY
    • CF_ACCESS_CLIENT_SECRET

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Invest loads about 5.9k tokens when it runs, and up to ~22k if it reads all its reference files. Until then it costs about 225 tokens; SKILL.md has 2,457 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~225
When it runs · the whole SKILL.md, loaded when a task matches
~5.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~22k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NoteMentions a .env fileSKILL.md:179
    **Prerequisite**: `.env` has `DEEPSEEK_API_KEY`. If the calling agent runs on the user's
  • NoteMentions a .env fileSKILL.md:191
    With `INVEST_API_BASE` set in `.env`, this machine is a **client**: every subcommand is
  • NoteMentions a .env fileSKILL.md:196
    Minimal client `.env` (no DeepSeek key / Gmail / memory needed):
  • NoteMentions a .env fileSKILL.md:211
    connecting to the hub only needs the two .env lines above). Error includes a hint |
  • NoteMentions a .env fileSKILL.md:340
    atim "key not set" message) → no key in `.env`; run `run.sh init` to configure it

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from longsizhuo/openInvest at commit 220abd2, republished under its MIT licence (© longsizhuo). 2,457 words, ~5,872 tokens.

Download SKILL.mdSave it as .claude/skills/invest/SKILL.md (or your agent's skills folder). This skill also uses 10 other files; get the full folder from GitHub.
name
invest
description
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.
version
0.24.0
platforms
linux, macos

Invest Skill

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); to update, just run run.sh update.

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":

Is a user asking in chat right now ("should I buy X / 该不该买 X") where you can
react in real time (wrong tool picked, blocked by a safety gate — the user sees it
on the spot and corrects it on the spot)?
  → Check whether you have an isolated sub-task delegation capability (Agent({...}) / delegate_task)
    Yes → Coordinator (table below)
    No  → Direct

Is this a cron / scheduled run with nobody watching?
  → Always go Direct, no matter which agent you are or whether you can delegate

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.

ScenarioWho you arePathWhat to runCredentials
Interactive (user present)Claude Code (has the Agent({...}) tool)Coordinatorprepare_committee → spawn 4 subagents → save_committeeNo 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 capabilityCodex / OpenClaw / Cursor / Cline / plain scriptDirectrun_committee <SYMBOL> one-shotNeeds LLM_API_KEY
cron / unattended, any agentAnyone, with or without delegation capabilityDirectrun_committee / daily_reportNeeds 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). 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)

1. Run `run.sh doctor`                               ← mandatory first step
   ├─ status: "ready"        → go to step 2 (keep using this skill)
   └─ status: "needs_setup"  → **switch to the `invest-setup` skill** (not handled here)

2. Pick the subcommand by user intent:

   "show portfolio / how much do I have / 看持仓"     → run.sh status
   "assess the situation / risk / concentration
    / 分析战况"                                       → run.sh status + **`curl /api/user`** for
                                                        wealth_context (**must-read**, avoids
                                                        misjudgment by legacy PWM logic)
   "what is my strategy / 我的策略是什么"             → run.sh strategy
   "recent trades / transaction log / 最近交易"       → run.sh history
   "where is the market / VIX now / 现在大盘"         → run.sh live_prices
   "if X drops 5% how much do I lose / 如果X跌5%"     → run.sh what_if --symbol X --pct -5
                                                        (X is a yfinance symbol in the user's
                                                        portfolio; --gold-pct / --ndq-pct kept
                                                        for legacy usage)
   "should I buy/sell X / analyze X / 该不该买卖X"    → committee protocol ↓
   "track AAPL / I want to watch TSLA / 跟踪苹果"     → see references/adding-assets.md
   "I already held 3000 X before using this /
    补录我原来就有的持仓"                              → run.sh status first — never re-add a
                                                        symbol it lists (double count); else
                                                        run.sh buy ... --existing-position
                                                        (MCP tool: record_existing_position;
                                                        `Unknown tool` = old server, do NOT
                                                        fall back to buy) — records it WITHOUT
                                                        deducting cash; a plain `buy` is a new
                                                        purchase paid from ledger cash

3. Run the committee per your path:
   - Coordinator → read references/committee-protocol.md (spawn 4 subagents)
   - Direct      → just `run.sh run_committee <SYMBOL>` for a JSON verdict

4. After getting the verdict / cio_memo:
   - **`cio_memo` is a Markdown string** (with `# title ## verdict` structure etc.).
     Render it **as Markdown for the user** — do not print raw JSON and make the
     user parse it themselves
   - Execution step: check the next_step field and guide the user as it says.
     **Never** write memory/ directly (see Constraints)

Coordinator path details (Claude Code only)

Read references/committee-protocol.md and follow it strictly. Full 6 stages:

  • Stage 0: same-day check (if memory/.committee/<today>/<asset>.md exists, reuse it directly)
  • Stage 1: prepare_committee to get the brief
  • Stage 2: Round 1 — 3 Agent({...}) in parallel (Macro + Quant + Risk)
  • Stage 3: Round 2 — Cross-challenge (2 Agents)
  • Stage 4 (optional): run Round 3+ if not converged
  • Stage 5: CIO synthesis (you write it yourself, do not delegate)
  • Stage 6: save_committee to persist

Critical warning: the regime_brief / sentiment_brief / valuation_brief / reentry_reference emitted by prepare_committee must be pasted verbatim into the corresponding workers' prompts per the instructions: regime + valuation + sentiment go into Quant; all three blocks + the path reference go into the CIO. Consequences of omission: Quant loses the probability framing and the defensive-sentinel background; the CIO's EXPECTED_PATH is made up out of thin air; INDEP_DEFENSE_FLAG never reaches the transcript → save_committee's deterministic defense downgrade (crash sentinel) breaks entirely.

Direct path details (any agent)

bash
# One command does it all
~/.claude/skills/invest/scripts/run.sh run_committee NDQ.AX

JSON output:

json
{
  "status": "ok",
  "asset": {...},
  "verdict": {"verdict": "ACCUMULATE", "confidence": 0.72, ...},
  "confidence_lookup": "样本不足(n=12)",
  "cio_memo": "<full CIO memo markdown>",
  "transcript_path": "memory/.committee/2026-05-09/NDQ.AX.md",
  "next_step": "..."
}

Options:

  • --force: rerun even if already run today (default reads the cache to save tokens)
  • --max-rounds N: cap on cross-challenge rounds (default 1)
Daily report (cron on the host-agent side)
bash
~/.claude/skills/invest/scripts/run.sh daily_report   # = uvx openinvest daily_report

Runs the full daily-report pipeline (multi-asset committee + Gemini second opinion + translator + discipline ledger); stdout emits markdown identical to the email body, does not send email — delivery belongs to the host agent (a Hermes cron can forward --no-agent --script output as-is; report formatting is guaranteed uniformly by the backend). On a circuit-breaker trip / unconfigured target_assets, stdout is a structured JSON error. Rerunning reruns the entire committee (burns tokens).

Prerequisite: .env has DEEPSEEK_API_KEY. If the calling agent runs on the user's machine but has no key, tell the user to run run.sh init to configure it first. (Remote-mode exception: the key lives on the hub, not needed locally — see next section.)

Remote mode (hub-and-spoke, multiple devices sharing one dataset)

Recommended new path (2026-07, BETA — not yet field-tested by the author in a real multi-device setup): when the hub runs openinvest-mcp --http (remote MCP), spokes register the HTTP MCP directly — all tools fully available, no CLI forwarding: claude mcp add --transport http openinvest https://<hub>/mcp --header "Authorization: Bearer $INVEST_API_TOKEN" The CLI→REST forwarding below is still supported (maintenance mode); the Coordinator protocol (prepare/save) and doctor/event_check still go through it. For deployment see backend wiki 08 §9.

With INVEST_API_BASE set in .env, this machine is a client: every subcommand is auto-forwarded to the remote hub (another machine running the invest web_api), and this machine has no memory/ and should not have one. All data (portfolio/strategy/verdicts/prompts) lives on the hub; change it once and every device sees it.

Minimal client .env (no DeepSeek key / Gmail / memory needed):

bash
INVEST_API_BASE=https://your-hub.example.com   # or http://10.0.0.x:8765
INVEST_API_TOKEN=<the hub's token of the same name>   # only needed if the hub has auth enabled
# If going through Cloudflare Tunnel + Access, use this pair instead:
# CF_ACCESS_CLIENT_ID=...  CF_ACCESS_CLIENT_SECRET=...

Behavior differences (all other commands are forwarded transparently, output has the same shape as local, decision tree applies as usual):

CommandRemote-mode behavior
doctorReturns hub-perspective checks + an extra remote section (api_base / auth mode / connectivity)
initDisabled (data lives on the hub; connecting to the hub only needs the two .env lines above). Error includes a hint
live_prices / correlateStill run locally (pure yfinance, touches no data)
run_committeeRuns on the hub (DeepSeek key is on the hub); the CLI polls automatically until done; same-day cache uses the hub's date semantics
prepare/save_committeeVia hub RPC — the Coordinator protocol (spawn 4 subagents) is completely unchanged
buy/sell/deposit/... write opsLand in the hub ledger (history source: skill_remote)
buy --existing-positionRefused (the REST endpoint can't carry it, and forwarding would deduct hub cash) — run it on the hub, or use the remote MCP tool record_existing_position
event_checkForwards the hub's manual scan; --live / --recall disabled (hub cron already covers them)

Discipline: in remote mode this machine has no memory/, so "reading/writing memory files directly" does not even exist — everything goes through run.sh or the hub API. In the Web API table below, replace :8765 with $INVEST_API_BASE in remote mode, and add Authorization: Bearer $INVEST_API_TOKEN when curling.

Tool lookup (MCP first; long tail in references/tools.md)

MCP users (auto-registered once the plugin is installed; same for Claude Code / Codex): status / strategy / history / live_prices / what_if / discipline / decisions / explain_decision / record_execution / ingest_event / buy / record_existing_position / sell / deposit / withdraw / set_allocations / track_asset / untrack_asset / news_sources / add_news_source / remove_news_source / run_committee — schemas auto-discovered; call them directly, no table lookup needed.

CLI/REST agents, or long-tail operations MCP does not cover (trades intent flow / config whitelist / events / holdings import / gold-specific endpoints) → read references/tools.md (full subcommand table + endpoint table). Full OpenAPI: http://127.0.0.1:8765/openapi.json.

Subcommand names are a closed set — commands outside the table do not exist. When tempted to call names like get_committee_context / analyze_asset, stop and check the table — you are most likely hallucinating; you probably want prepare_committee or run_committee. All output is JSON; always quote numbers from the JSON, never from memory/*.md.

Importing holdings from a broker-app screenshot (you do the OCR; backend has zero dependencies)

When the user sends a broker holdings screenshot: read the image yourself (you have vision), convert each row into text as symbol/quantity/cost/currency/channel, then use import --text "..." (or POST /api/holdings/import) — the backend LLM only parses text, it never receives images. Preview first without --commit; after the user verifies, run with --commit for a non-destructive write.

Feeding news (your search beats any crawler)

You have far stronger search capability than the backend crawler (including Chinese-language sources). When you come across finance news relevant to the user's holdings while browsing/searching, proactively call ingest_event to feed it into the event ledger (MCP tool, or CLI ingest_event --title --url [--snippet --source]) — the backend handles normalization / severity grading / dedup / RAG recall, and resending the same item never double-books. A new item that grades severity ≥ mid, non-neutral, and hits a holding/target automatically re-runs the committee for that symbol (rate-limited and shared with the crawler: 12h per-symbol cooldown that a strictly higher-severity item can bypass, ≤4 per rolling 24h; the result's committee_task_id says whether one fired) — so feed material news only. Ingestion is disabled in advisory mode. A-share / regional-market news especially: that is the crawler's blind spot and you are the only source. If the host has a quotes/news skill installed (e.g. Longbridge), its news is worth feeding too — the ledger cares about the information, not where it came from.

Show full SKILL.md (919 more words)Show less

Decision-loop workflow (Decision Review + Reflection)

openInvest does the bookkeeping; you do the collection — that is the host agent's core duty (issue #133 Decision 2).

User asks "why HOLD today / why did it tell me to sell" (Decision Review):

  1. explain_decision <decision_id> for the full 4-role debate transcript + CIO memo + path snapshot (+ confidence_lookup, the number to quote next to the verdict — see Constraints)
  2. Combine with status (current portfolio) + GET /api/user (wealth_context) if needed
  3. Answer using evidence from the transcript — do not invent reasons yourself

User reacts to a recommendation with "I didn't buy / I bought / I disagree" (Reflection):

  1. First ask one question about the reason (valuation too high? insufficient funds? disagrees with the committee? forgot?) — do not skip this; the reason is the one piece of information the system cannot obtain on its own
  2. record_execution <decision_id> [--rejected] --reason "..." to write it back (idempotent; the user may change their answer anytime)
  3. If the user actually traded → guide them to log the trade (a same-symbol, same-direction fill within 7 days is also auto-matched as a fallback)

User asks "how often did I follow the advice / is the committee reliable": decisions --days 90 → adoption rate + the full verdict↔intervention↔execution↔outcome chain per decision (lists the newest 20 — narrow with --symbol / --verdict, --limit 0 for all; count and summary always cover every match); pair with discipline for the counterfactual P&L of rule-blocked actions (its inaction rate counts live verdicts only). For hit rates use GET /api/verdict_review/summary: it returns separate live / backtest / contaminated buckets — quote only live as performance, never add the buckets together, and say "sample too small" when a rate is null (n<30). Weekend decisions on weekend-closed assets reuse Friday's close, so they are left out of every bucket and counted in weekend_dup_excluded — that is why live.n is below the number of live decisions. When the user rejects the same class of recommendation several times in a row, proactively point out "the divergence pattern between you and the committee" — that is not a bad thing; it is a signal worth recording.

Constraints (guard these, do not break them)

  • Before analyzing portfolio / concentration / risk, always read GET /api/user for wealth_context — forget this and you repeat the 2026-05-12 mistake: the user had entered a large family backup, the agent ran status without reading user, and per legacy PWM logic shouted "60% concentration overweight → recommend TRIM". Wrong. The correct approach:
    • No wealth_context filled in → judge liquidity by portfolio cash + use the 25-35% concentration alert band
    • Filled in → use the WealthContextOfficer perspective:
      • Concentration % is computed as portfolio_value / (portfolio + emergency_buffer_cny), not portfolio alone
      • family_backup_available=true → low portfolio cash is not a liquidity risk
      • account_purpose="零花钱账户" (pocket-money account) → tolerate larger drawdowns; "退休金" (retirement fund) → lean toward trimming
    • The cap on any add-on buy is always portfolio cash (never touch the backup) — this never changes
  • Do not run daily_report proactively in conversation — unless the user explicitly says "run the deep analysis" / "run full report" or you are setting up the daily cron. That path burns DeepSeek tokens. In conversation, single-asset run_committee is enough.
  • Never fabricate live prices. Always go through run.sh status or live_prices. yfinance may return stale data; watch the is_stale flag.
  • Never write memory/ directly. All state changes go through CLI subcommands / MCP tools (atomic write + fcntl lock + audit trail). Direct editing causes schema drift + concurrent-write corruption.
  • Do not rerun the committee for the same asset on the same day — run_committee reads the cache by default; on the Coordinator path, check with ls memory/.committee/<today>/<SYM>.md first.
  • Do not fabricate CIO confidence. When workers disagree sharply, honestly write confidence: 0.4-0.5.
  • Next to a verdict, quote confidence_lookup, not confidence. run_committee (CLI + MCP), explain_decision, /api/committee_sessions and /api/committee/{task_id} return it: how similar verdicts turned out 30 days later (deterministic table refreshed by the daily verdict_review job from live verdicts plus the prospective paper fleet; when this install has n<30 for that verdict it falls back to a fleet-only default table shipped with the package). The text ends with its source — 含纸面舰队样本 / 默认表 / 本机样本 — keep it when you quote the number: paper-fleet runs use a neutral portfolio and no event/valuation/sentiment inputs. For HOLD it is the share whose 30-day move stayed inside the normal volatility band, shown next to the market's own sideways base rate — that measures how sideways the market was, not accuracy, so never call it a hit rate. n<30 reads "样本不足(n=…)"; rule-forced HOLDs / self-reported ≤0.4 read "输入缺失/强制 HOLD". confidence is the CIO's self-reported number, kept for the record only (historically no better than a constant); if you mention it, label it self-reported (自报).
  • Do not leak the user's email or other personally identifying information — never hard-code it in output.

Where to look when something breaks

Read the doctor JSON output carefully. Every check has a hint field telling you how to fix it. If doctor is all green but a subcommand still fails, read references/troubleshooting.md.

Common Direct-path errors:

  • error: DEEPSEEK_API_KEY 未设 (backend's verbatim "key not set" message) → no key in .env; run run.sh init to configure it
  • error: asset X not in strategy.target_assets → add X to the strategy first, see references/adding-assets.md

References index

FileWhen to read
references/tools.mdFull subcommand table + Web API endpoint table (long-tail operations MCP does not cover)
references/committee-protocol.mdRunning the committee on the Coordinator path (Claude Code only)
references/two-paths.mdUnderstanding Coordinator vs Direct / DeepSeek cron triggering
references/adding-assets.mdUser wants to track a new symbol
references/troubleshooting.mddoctor all green but still failing
references/onboarding.mdFirst-time install goes to the invest-setup skill; this file is kept as a detailed reference

For deeper architectural context see the project wiki: github.com/longsizhuo/openInvest/tree/main/docs/wiki

© longsizhuo, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (scripts, references) in plugin/skills/invest of longsizhuo/openInvest.

  • SKILL.md
  • CHANGELOG.md
  • README.md
  • references/adding-assets.md
  • references/committee-protocol-hermes.md
  • references/committee-protocol.md
  • references/onboarding.md
  • references/tools.md
  • references/troubleshooting.md
  • references/two-paths.md
  • scripts/run.sh

Open the folder on GitHubat commit 220abd2

Compare with similar skills

Invest next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Invest compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Invest this skilllongsizhuo/openInvest108—~5.9kAutomated safety check: NotesMIT
ApocdataApocData/ApocData-skill104—~1.9kAutomated safety check: PassApache-2.0
Polymarket Tennislivetennisapi/livetennisapi-mcp152—~3kAutomated safety check: PassMIT
Go Spec Reviewerinference-gateway/inference-gateway214—~1.2kAutomated safety check: PassApache-2.0
Wind MCP SkillWind-Alice/AliceMarket1341 repos~1.3kAutomated safety check: PassNone
Stock Market Data MCP QueryYourdaylight/stock_datasource189—~1.1kAutomated safety check: PassMIT

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More from longsizhuo/openInvest

  • Invest Setup

    longsizhuo/openInvest

    First-time openInvest installation and onboarding. An agent skill from longsizhuo/openInvest.

    108 GitHub stars~2.7k tokensUpdated today
    Auto-check: notes
  • Okf Frontmatter

    longsizhuo/openInvest

    Maintain openInvest's docs (docs/wiki chapters + docs/wiki/adr) under Google's Open Knowledge Format (OKF).

    108 GitHub stars~1.5k tokensUpdated today
    Auto-check passed
  • Invest Backup

    longsizhuo/openInvest

    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…

    108 GitHub stars~1.2k tokensUpdated today
    Auto-check: notes

Questions about Invest

What does Invest do?

openInvest multi-asset AI investment committee — daily use. An agent skill from longsizhuo/openInvest. Invest is an agent skill from longsizhuo/openInvest. openInvest multi-asset AI investment committee — daily use.

When should I use Invest?

Invest fits situations like: 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.

How do I install Invest in Claude Code?

Run `npx skills add longsizhuo/openInvest --skill invest -a claude-code`. Or copy the skill folder (plugin/skills/invest in longsizhuo/openInvest) into .claude/skills/invest in your project. Claude Code loads it when a task matches its description.

How do I install Invest in Codex?

Run `npx skills add longsizhuo/openInvest --skill invest -a codex`. Or copy the skill folder (plugin/skills/invest in longsizhuo/openInvest) into .agents/skills/invest in your project. Codex loads it when a task matches its description.

Can I use Invest in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add longsizhuo/openInvest --skill invest -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/invest, .gemini/skills/invest, .github/skills/invest and .opencode/skills/invest in your project.

What does Invest need to run?

Going by SKILL.md and its folder, Invest needs a shell for the scripts in its folder, the command-line tools its instructions call (claude) and credentials named LLM_API_KEY, INVEST_API_TOKEN, DEEPSEEK_API_KEY and CF_ACCESS_CLIENT_SECRET. Our summary lists: Python 3; A Bash shell; A credential in LLM_API_KEY; A credential in DEEPSEEK_API_KEY.

Does Invest access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Invest safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Invest use?

Invest is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Invest use?

About 5.9k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16k tokens, read only when the agent opens those files.

What are the alternatives to Invest?

Skills that share tags, products or a category with Invest: Apocdata (ApocData/ApocData-skill, 104 stars), Polymarket Tennis (livetennisapi/livetennisapi-mcp, 152 stars), Go Spec Reviewer (inference-gateway/inference-gateway, 214 stars) and Wind MCP Skill (Wind-Alice/AliceMarket, 134 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Invest?

longsizhuo (a GitHub user) maintains it in longsizhuo/openInvest, which has 108 GitHub stars. The repository was last updated on October 11, 2026.

Source: longsizhuo/openInvest on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.