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/stock-analysis/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/stock-analysis)<a href="https://agentmods.dev/skills/patricksudo/fadacai-portfolio/stock-analysis"><img src="https://agentmods.dev/badge/skills/patricksudo/fadacai-portfolio/stock-analysis/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/stock-analysis"><img src="https://agentmods.dev/badge/skills/patricksudo/fadacai-portfolio/stock-analysis.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 | $0.00043 | $0.09243 |
| Opus 5 | $0.00022 | $0.04621 |
| Sonnet 5 | $0.00009 | $0.01849 |
| Haiku 4.5 | $0.00004 | $0.00924 |
Grade A, and why
stock-analysis 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 11d 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 — 525 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Analysis
💡 模型指引：session context < 100k →
/model sonnet；> 100k → 先/compact再 Sonnet，或直接/model opus（長 context 推理品質 Opus 更穩定）。重大決策（>5% 倉位）一律用 Opus。
Generate a standardized research report for one or more stock tickers.
Step 0: 分析前準備
預設模式（無 --current）— 純獨立分析
- 跳過 plan.md、feedback/*.md、持倉、journal 偵測
- 分析不考慮現有倉位或投資計畫，僅基於公開市場數據
- 保留 Step 0e：Verdict 之前必須完成「核心 thesis / 證偽條件 / 機率分布」三題
Step 0.5 (共用): Macro + Earnings + Fundamentals Cache Load
讀以下四份 cache：
briefing-out/cache/macro-snapshot.json— 用於 Step 0e 第一性檢查的 macro ground statebriefing-out/cache/earnings-history.json— 該 TICKER 的 trailing 8Q beat rate + surprisebriefing-out/cache/earnings-dates.json— 該 TICKER 的下次 earnings 日期briefing-out/cache/fundamentals-snapshot.json（TTL 24h）— TICKER 的三錨點輸入（pe_ratio/peg_ratio/wall_street_target/growth/margins）+forward_estimates（賣方共識 fwdEPS curr_fy/next_fy + EPS 修正動能）
若 TICKER 不在 earnings cache 中（如新標的）→ 跑一次 python3 tools/earnings_history.py --force；或標 (earnings cache miss)。
fundamentals cache 處理：
- TICKER 在 cache 且 mtime < 30h → 使用，供三錨點估值 + probability agent 1d/1h
- TICKER 不在 cache 或 mtime > 30h → 先跑
python3 tools/fetch_fundamentals.py --ticker TICKER（單票 fetch + merge 進 cache，含 A4self_valuation），再讀 cache。這樣 cache miss/stale 也能取得 A4，不再直接標(self-val N/A)。Agent 3 仍同批抓get_fundamentals_snapshot+get_earnings_history作即時三錨點交叉（fetch_fundamentals 失敗時的 fallback）。 - 只有
fetch_fundamentals --ticker真的失敗（EODHD 無資料/token 缺）才標(self-val N/A)。 pe_ratio == 0.0 / null→ 丟棄 A1 錨；peg_ratio == 0.0 / null→ 丟棄 A2 錨，標(anchor unavailable)
這些 cache 資料用於：
- Section「Investment Thesis」: 引用 trailing 8Q beat rate 強化/弱化基本面論點
- Section「三錨點公允價」: A1/A2/A3 錨點計算 Fair PE + EV（取代手寫點估計）
- Section「Verdict」前呼叫
probability-honesty-checker時，強制將 macro + base rate 帶入 prompt（Step 1d、1h、1i 必填）
--current 模式 — 整合持倉與計畫
啟用後執行完整 AGENTS.md Step 0 統一規範（0a → 0b → 0c → 0d → 0e）：
- 讀
plan.md+feedback/*.md；了解此標的在計畫中的角色 - 呼叫
get_account_position取即時持倉 - 今日 journal 不存在 → 執行 gap-fill + 變動偵測 + 自動建立 journal
- 報告額外輸出「持倉確認」與「配置計畫定位」兩節
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.
- 11d ago First seen · 525 lines · 43 tokens per session scan A 80be2244e98f
stock-analysis is a skill published in the GitHub repository PatrickSUDO/fadacai-portfolio (139 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 9,243 once invoked, about $0.0002 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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