Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add fatfingererr/macro-skills --skill analyze-retail-inverse-etf-allocationgit clone --depth 1 https://github.com/fatfingererr/macro-skillsWrote 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/fatfingererr/macro-skills/analyze-retail-inverse-etf-allocation)<a href="https://agentmods.dev/skills/fatfingererr/macro-skills/analyze-retail-inverse-etf-allocation"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/analyze-retail-inverse-etf-allocation/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/fatfingererr/macro-skills/analyze-retail-inverse-etf-allocation"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/analyze-retail-inverse-etf-allocation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00066 | $0.01928 |
| Opus 5 | $0.00033 | $0.00964 |
| Sonnet 5 | $0.00013 | $0.00386 |
| Haiku 4.5 | $0.00007 | $0.00193 |
Grade A, and why
analyze-retail-inverse-etf-allocation 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.
What it actually says
<essential_principles> 散戶槓桿反向 ETF 做空配置分析 核心原則
步驟：
- 下載反向/正向槓桿 ETF 與基準指數的 OHLCV 資料
- 計算美元成交量比率（或 AUM proxy）作為做空配置指標
- 以滾動分位數標準化，偵測極低做空配置事件
- 找出歷史類比事件，計算前瞻風險統計
- 產生結論與圖表
不適用情境：
- 非美股市場（ETF 標的不同）
- 極短期日內交易擇時
- 機構級避險策略監控（本指標偏散戶行為）
<quick_start> 快速開始
# 安裝依賴
pip install yfinance pandas numpy matplotlib
# 完整分析（預設參數）
python scripts/inverse_etf_analyzer.py --start 2012-01-01 --end 2026-02-01
# 快速檢查當前狀態
python scripts/inverse_etf_analyzer.py --start 2012-01-01 --end 2026-02-01 --quick
# 自訂 ETF 清單
python scripts/inverse_etf_analyzer.py --start 2012-01-01 --end 2026-02-01 \
--inverse SPXU SDS SH --long UPRO SSO SPY
</quick_start>
- 完整分析 - 計算做空配置比率、偵測極端值、事件研究、前瞻風險統計
- 視覺化 - 產生 SPX + 做空配置 + 事件標記圖表
- 歷史事件對照 - 列出歷史上做空配置極低的事件及後續市場表現
- 快速檢查 - 僅查看當前做空配置狀態與分位數
等待回應後再繼續。
讀取工作流程後，請完全遵循其步驟。
<reference_index>
參考文件 (references/)
| 文件 | 內容 |
|---|---|
| methodology.md | 做空配置比率計算方法、分位數標準化、事件研究框架 |
| data-sources.md | 資料來源說明（Yahoo Finance ETF OHLCV）、fallback、授權 |
| input-schema.md | 所有輸入參數定義、型別、預設值、驗證規則 |
| </reference_index> |
<workflows_index>
| Workflow | Purpose |
|---|---|
| analyze.md | 完整分析工作流（資料取得→指標計算→事件偵測→風險統計→結論生成） |
| visualize.md | 圖表生成工作流（SPX + 做空配置雙軸圖 + 事件垂直線） |
| historical-episodes.md | 歷史事件對照工作流（列出過去觸發事件與後續表現） |
| </workflows_index> |
<templates_index>
| Template | Purpose |
|---|---|
| output-json.md | JSON 輸出模板（程式/儀表板消費） |
| output-markdown.md | Markdown 輸出模板（人類閱讀/社群分享） |
| </templates_index> |
<scripts_index>
| Script | Purpose |
|---|---|
| inverse_etf_analyzer.py | 主分析腳本：資料取得、指標計算、事件偵測、風險統計 |
| visualize_allocation.py | 視覺化腳本：雙軸圖表生成 |
| </scripts_index> |
<success_criteria> Skill 成功執行時：
- 成功下載所有指定 ETF 的 OHLCV 資料
- 計算出做空配置比率（short_alloc）與分位數
- 偵測出歷史觸發事件（若存在）
- 計算每次事件的前瞻風險統計
- 產生 JSON + Markdown 結論輸出
- 結論包含 caveat（風險分布訊號，非擇時工具） </success_criteria>
<examples_index>
範例輸出 (examples/)
| 文件 | 內容 |
|---|---|
| sample_output.json | 完整分析的 JSON 範例輸出 |
| </examples_index> |
What ships with it
13 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- examples/sample_output.json 3.5 KB
- manifest.json 3.6 KB
- references/data-sources.md 2.6 KB
- references/input-schema.md 2.1 KB
- references/methodology.md 4.4 KB
- scripts/inverse_etf_analyzer.py 17 KB runs code
- scripts/visualize_allocation.py 6.7 KB runs code
- skill.yaml 6.3 KB
- templates/output-json.md 3.0 KB
- templates/output-markdown.md 1.9 KB
- workflows/analyze.md 2.9 KB
- workflows/historical-episodes.md 2.0 KB
- workflows/visualize.md 1.3 KB
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 · 146 lines · 66 tokens per session scan A 4c4f6162eea2
analyze-retail-inverse-etf-allocation is a skill published in the GitHub repository fatfingererr/macro-skills (3 stars, last pushed 7mo ago), licensed MIT. It adds 66 tokens to every session and 1,928 once invoked, about $0.0003 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 2026-08-31.
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