AC 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.
openInvest multi-asset AI investment committee — daily use.
As a process C 54/100 · Has gaps — weak spots: result and completion, when it triggers, consistency
How to improve
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
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low Exfiltration
net-credential-useSKILL.md:211Credential used in a network call (verify the destination is the intended service) (quoted — discussed, not commanded)add `Authorization: Bearer $INVEST_API_TOKEN` when curling.
quoted
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-long-hermesdescription is 893 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
Process rating: all ten parameters 54/100
- 0Result and completion. Does not say what the result is
- 0Consistency. Frontmatter name (invest) differs from the folder (openinvest)
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
- 70Inputs and preconditions. Inputs and preconditions are listed
- 70Execution cost. Instruction body is 4769 tokens
- 85Steps. 27 steps, 3 vague phrases
- 100Failures and branches. 1 branches, has a failure section
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (9 tags): a typed call is more reliable
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 893: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +5Description quotes 7 example trigger phrases
- +4Structure: 14 headings
- +3Step-by-step instructions: 27 items
- +4Has examples (6 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.