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 byteseek/Mira --skill commodity-cycle-analysisgit clone --depth 1 https://github.com/byteseek/MiraWrote 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/byteseek/mira/commodity-cycle-analysis)<a href="https://agentmods.dev/skills/byteseek/mira/commodity-cycle-analysis"><img src="https://agentmods.dev/badge/skills/byteseek/mira/commodity-cycle-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/byteseek/mira/commodity-cycle-analysis"><img src="https://agentmods.dev/badge/skills/byteseek/mira/commodity-cycle-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 336 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00030 | $0.02893 |
| Opus 5 | $0.00015 | $0.01447 |
| Sonnet 5 | $0.00006 | $0.00579 |
| Haiku 4.5 | $0.00003 | $0.00289 |
Grade A, and why
commodity-cycle-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 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 — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Commodity Cycle Analysis Skill
这个 skill 用于研究实物大宗商品、商品期货曲线、资源周期和商品价格对资产的传导。
它不是泛宏观综述，也不是资源股单票模板。它服务于一个核心问题：
当前商品价格到底由供需平衡、库存、成本曲线、政策/地缘风险、金融条件还是仓位驱动？这个驱动是否足以改变目标资产的盈利、估值、风险溢价或交易节奏？
Use When
- 研究对象是原油、成品油、天然气、LNG、煤炭、铜、铝、镍、锂、铀、铁矿、钢、黄金、白银、农产品或其他商品。
- 用户问商品价格、期货曲线、库存、供需平衡、成本曲线、OPEC、制裁、出口限制、矿山供给、天气、WASDE、EIA、IEA、LME、CFTC 或商品 ETF。
- 单票、ETF 或产业研究的主变量是商品 beta，而不是公司自身执行、技术路线或普通宏观风险偏好。
- 需要判断资源股、能源股、材料股、化工、航运、消费或通胀资产受到商品冲击的方向和幅度。
Avoid When
- 目标资产的主要变量是公司订单、融资、生存性、监管审批、技术验证或并购催化剂。
- 商品价格只是背景，不能改变收入、利润率、资本开支、估值、资金流或仓位。
- 没有可用的供需、库存、曲线或成本数据，只能复述价格走势。
- 研究对象是泛宏观 regime，且不需要拆具体商品的物理平衡表。
Required Inputs
commodity_or_assetmarket_scope例如global/US/China/Europe/multiresearch_questionresearch_cutoff_datethesis_horizon例如days_weeks/1Q_2Q/2Q_8Q/cyclecurrent_market_pricing至少包括现货、近月、远月、曲线形态或相关 ETF/股票表现中的两项。commodity_sources至少覆盖官方/行业数据、市场价格或仓位数据、公司/行业披露、机构或 practitioner 解释中的两类。
Core Principle
先拆物理平衡，再拆金融定价；先问价格在反映什么，再问这个反映能不能持续。
商品研究不能只看价格涨跌。必须把结论落到至少一条可证伪链条：
demand shock -> inventory draw -> curve backwardation -> producer cash flow revisionsupply disruption -> spot premium -> cost passthrough -> downstream margin compressioncost curve reset -> marginal supply discipline -> long-dated price supportpolicy/geopolitics -> trade flow rerouting -> regional basis widening -> asset impactreal rates / dollar -> investment demand -> precious metal price -> miner multipleweather / crop condition -> yield revision -> stock-to-use ratio -> futures curvepositioning squeeze -> price overshoot -> roll yield / equity beta risk
If no credible chain exists, commodity stays as context and should not enter the core thesis.
Analysis Sequence
0. Routing Snapshot
Start every formal note with:
task_modecommodity_or_assetmarket_scopetime_boundarydominant_driverone ofphysical_balance,inventory_cycle,cost_curve,policy_geopolitics,financial_conditions,positioning,mixedcommodity_weightone ofnone,context,secondary,primaryrouting_mismatch_riskexpected_output_package
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 · 352 lines · 30 tokens per session scan A ab027173cb01
commodity-cycle-analysis is a skill published in the GitHub repository byteseek/Mira (267 stars, last pushed yesterday), licensed Apache-2.0. It adds 30 tokens to every session and 2,893 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.
Other skills, from other repositories
cost
Deep cost exploration and transparency. Shows real token usage, session costs, campaign spend, burn rates, and model breakdown. Reads Claude Code's native session data for exact numbers. Complements /dashboard with focused cost views.
creating-financial-models
A financial analysis toolkit for valuing companies, projects, or acquisitions and testing how assumptions affect the results.
aml_screening
Screen transactions for money laundering patterns and risk indicators.
get_financial_statements
Get financial statements (income, balance, cash flow) for a company.
analyze_risk_metrics
Calculate risk metrics (VaR, Sharpe, volatility) given a set of returns.
fetch_economic_indicators
Fetch economic indicators like GDP, CPI, Unemployment.