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-copper-supply-concentration-riskgit 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-copper-supply-concentration-risk)<a href="https://agentmods.dev/skills/fatfingererr/macro-skills/analyze-copper-supply-concentration-risk"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/analyze-copper-supply-concentration-risk/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-copper-supply-concentration-risk"><img src="https://agentmods.dev/badge/skills/fatfingererr/macro-skills/analyze-copper-supply-concentration-risk.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 | $0.00070 | $0.01776 |
| Opus 5 | $0.00035 | $0.00888 |
| Sonnet 5 | $0.00014 | $0.00355 |
| Haiku 4.5 | $0.00007 | $0.00178 |
Grade A, and why
analyze-copper-supply-concentration-risk 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 12d 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>
市場敘事必須可量化驗證。三大命題對應三組指標:
| 命題 | 核心問題 | 量化指標 |
|---|---|---|
| A. 集中度 | 供應是否過度集中? | CR4, CR5, 份額排名 |
| B. 結構衰退 | 智利是否結構性衰退? | 峰值年份、峰值回撤 |
| C. 替代依賴 | 是否依賴秘魯/DRC? | 秘魯+DRC 合計份額 vs 智利份額 |
注意:由於 MacroMicro 只提供 5 個國家的細分數據,HHI 指標不適用於本分析。
唯一主要來源,使用 Chrome CDP 全自動抓取 Highcharts 圖表數據。
- URL: https://en.macromicro.me/charts/91500/wbms-copper-mine-production-total-world
- 口徑: mined copper content(礦場產量的銅金屬含量)
- 可用序列: World, Chile, Peru, DRC, China, US
</essential_principles>
輸出兩層分析:
- Concentration: 國家份額排名、CR4/CR5
- Chile vs Replacers: 智利 vs 新興替代國(Peru + DRC)份額對比
<quick_start>
全自動執行(無需手動操作 Chrome)
Step 1:安裝依賴
pip install requests websocket-client pandas numpy matplotlib
Step 2:一鍵抓取數據(自動啟動/關閉 Chrome)
cd scripts
python fetch_copper_production.py
腳本會自動:
- 啟動 Chrome 調試模式
- 等待頁面載入(~40 秒)
- 提取 Highcharts 數據
- 儲存到
cache/copper_production.csv - 關閉 Chrome
Step 3:生成 Bloomberg 風格視覺化圖表
python visualize_copper_concentration.py
輸出:output/copper_concentration.png
</quick_start>
- 快速圖表 - 直接生成 Bloomberg 風格集中度圖表
- 完整分析 - 1970 年至今的集中度趨勢分析(含數據表)
- 智利趨勢 - 智利產量份額與峰值回撤分析
- 替代評估 - 秘魯+DRC 替代依賴度分析
請選擇或直接提供分析參數。
路由後,執行對應命令。
<directory_structure>
analyze-copper-supply-concentration-risk/
├── SKILL.md # 本文件(路由器)
├── skill.yaml # 前端展示元數據
├── scripts/
│ ├── fetch_copper_production.py # 全自動 CDP 數據爬蟲
│ └── visualize_copper_concentration.py # Bloomberg 風格視覺化
├── cache/
│ ├── copper_production.csv # 數據快取
│ └── copper_production_cache.json # 原始 JSON 快取
└── output/
└── copper_concentration.png # 輸出圖表
</directory_structure>
<scripts_index>
| Script | Command | Purpose |
|---|---|---|
| fetch_copper_production.py | python fetch_copper_production.py |
全自動 CDP 抓取(自動啟動/關閉 Chrome) |
| fetch_copper_production.py | --force-refresh |
強制重新抓取(忽略快取) |
| fetch_copper_production.py | --start-year 1970 |
指定起始年份 |
| visualize_copper_concentration.py | python visualize_copper_concentration.py |
生成 Bloomberg 風格圖表 |
| visualize_copper_concentration.py | --output path/to/output.png |
指定輸出路徑 |
| </scripts_index> |
視覺化輸出:Bloomberg 風格銅供應集中度儀表板
包含兩張圖(上下排列):
- 國家份額堆疊面積圖:Chile, Peru, DRC, China, US, Others
- 智利 vs 新興替代國:Chile vs Peru+DRC 份額對比,標記交叉點
配色:Bloomberg 深色主題
- 背景:
#1a1a2e - Chile:
#ff6b35(橙紅) - Peru:
#00bfff(天藍) - DRC:
#00ff88(綠) - Peru+DRC:
#00d4aa(青綠)
快速繪圖:
cd scripts
python visualize_copper_concentration.py
輸出路徑:output/copper_concentration.png
<output_example> 2023 年關鍵指標:
| 國家 | 份額 |
|---|---|
| Chile | 23.5% |
| Peru + DRC | 25.2% |
| China | 7.5% |
| US | 5.0% |
關鍵發現:
- 智利份額峰值:37.2% (2004)
- 智利當前份額:23.5% (2023)
- 峰值回撤:13.7pp
- 2023 年 Peru+DRC 首次超越智利(份額逆轉) </output_example>
<success_criteria> 分析成功時應產出:
- 數據已從 MacroMicro 全自動抓取並快取
- 國家份額排名(Chile, Peru, DRC, China, US, Others)
- 智利峰值年份與回撤分析
- 秘魯+DRC 替代趨勢
- Bloomberg 風格視覺化圖表
- 明確標註數據來源 </success_criteria>
What ships with it
23 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/concentration_analysis.json 2.8 KB
- examples/full_report.md 6.9 KB
- manifest.json 3.3 KB
- references/chile-supply-dynamics.md 4.7 KB
- references/concentration-metrics.md 6.6 KB
- references/data-sources.md 2.5 KB
- references/failure-modes.md 7.2 KB
- references/geopolitics-risk.md 8.6 KB
- references/methodology.md 4.5 KB
- references/replacement-countries.md 5.7 KB
- scripts/copper_concentration_analyzer.py 19 KB runs code
- scripts/fetch_copper_production.py 15 KB runs code
- scripts/visualize_copper_concentration.py 9.7 KB runs code
- skill.yaml 18 KB
- templates/config.yaml 2.7 KB
- templates/output-json.md 6.8 KB
- templates/output-markdown.md 7.3 KB
- workflows/analyze-chile-trend.md 9.2 KB
- workflows/analyze-concentration.md 2.4 KB
- workflows/analyze-replacement.md 12 KB
- workflows/full-report.md 11 KB
- workflows/ingest-data.md 14 KB
- workflows/scenario-analysis.md 12 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.
- 12d ago First seen · 173 lines · 70 tokens per session scan A 6090fad9da05
analyze-copper-supply-concentration-risk is a skill published in the GitHub repository fatfingererr/macro-skills (3 stars, last pushed 7mo ago), licensed MIT. It adds 70 tokens to every session and 1,776 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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