Borrowing it
Nothing to install: this file belongs to PatrickSUDO/fadacai-portfolio. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/PatrickSUDO/fadacai-portfolio/main/.agents/skills/ev-check/SKILL.mdgit clone --depth 1 https://github.com/PatrickSUDO/fadacai-portfolioWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/patricksudo/fadacai-portfolio/ev-check)<a href="https://agentmods.dev/skills/patricksudo/fadacai-portfolio/ev-check"><img src="https://agentmods.dev/badge/skills/patricksudo/fadacai-portfolio/ev-check/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/patricksudo/fadacai-portfolio/ev-check"><img src="https://agentmods.dev/badge/skills/patricksudo/fadacai-portfolio/ev-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 5 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00072 | $0.01939 |
| Opus 5 | $0.00036 | $0.00970 |
| Sonnet 5 | $0.00014 | $0.00388 |
| Haiku 4.5 | $0.00007 | $0.00194 |
Grade A, and why
ev-check scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EV / Probability Distribution Honesty Check
對當前持倉執行嚴謹的機率分布與 expected value 計算。強制 first-principles，不接受偷懶輸出。
此 skill 可獨立呼叫做 ad-hoc check，或被其他 skill（briefing / portfolio-review / stock-analysis / todo）在輸出 Verdict / 機率分布前 mandatory 呼叫。
Arguments
/ev-check→ 預設 30 天 horizon/ev-check 7d//ev-check 14d//ev-check 30d→ 自選時間窗/ev-check 30d nvda-bear→ 用戶指定情境主題（agent 會以此為主要 catalyst 反推）
Workflow
Step 1: 收集 raw 輸入
呼叫以下 MCP 取數據（可平行）：
mcp__firstrade-server__get_account_position— 持倉mcp__firstrade-server__get_account_balance— 帳戶總值 + 現金mcp__technical-mcp__get_batch_indicators(所有持倉, period=3mo)— RSI / momentum / trendmcp__technical-mcp__get_sector_rotation(period=3mo)— leading / laggingmcp__yfinance-advanced__get_stock_info(top 11 by MV)— 52w high/low、fundamentalsmcp__fmp-mcp__getEarningsCalendar(today, today+horizon)— binary catalysts in window- （平行 agent）
mcp__eodhd-mcp__get_sentiment_trend(top 8 by MV, days=30)— 7d/30d sentiment - Read
briefing-out/cache/macro-snapshot.json— macro state（fed_funds / 2s10s / HY OAS / VIX / CPI / regime_tag）；status == "skipped"或缺失 → 1i 標unavailable並註明
Step 2: 整理成 9 項 Input Enumeration
按 probability-honesty-checker agent 的 Step 1 contract，整理：
- 1a. RSI 分布（每個 bucket 檔數 + % of port）
- 1b. 距 52w 高位置（中位數、最大、最小）
- 1c. 已實現波動（5d / 2d / 最大單日）
- 1d. Binary catalysts table（catalyst / 日期 / 影響持倉 % / base rate）
- 1e. 集中度（top 1、top 5、最大板塊）
- 1f. 板塊輪動曝險（leading 持倉 % / lagging 持倉 %）
- 1g. Sentiment 健康度
- 1h. Thesis 健康度（從 plan.md + 近期新聞）
- 1i. Macro state（從 macro-snapshot.json：fed_funds + 30d change / 2s10s + regime / hy_oas + regime + pct_1y / vix + regime / cpi_yoy + trend / regime_tag；agent 缺 1i 會回 INVALID INPUT）
不齊全 → 不能進下一步，必須補齊。
Step 3: 呼叫 probability-honesty-checker subagent
Agent(
subagent_type: "probability-honesty-checker",
description: "EV check for [horizon]",
prompt: """
執行 6 步強制流程計算當前組合 [horizon] 機率分布與 EV。
時間窗: [horizon]
情境主題（如有）: [user-specified]
## Step 1 輸入資料（8 項齊全）:
[貼上 Step 2 整理好的資料]
## 額外 context:
[plan.md 摘要 / 用戶提到的特定 catalyst / 最近 N 天的事件]
請按你的 6 步流程輸出：
1. Input Enumeration（confirm 我給的齊全）
2. 形狀反推
3. Conditional Probabilities
4. Aggregated Scenario Probabilities
5. EV Calculation
6. Self-Audit Checklist
+ 給主 skill 的精簡輸出
"""
)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 145 lines · 72 tokens per session scan A 0fc37342efa4
ev-check is a skill published in the GitHub repository PatrickSUDO/fadacai-portfolio (139 stars, last pushed yesterday), licensed MIT. It adds 72 tokens to every session and 1,939 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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