UZI-Skill is a collection of coding-agent skills for analyzing individual stocks across Chinese, Hong Kong, and United States markets using public data, investor viewpoints, quantitative rules, and institutional analysis methods. It is for users who want an agent to produce detailed stock-analysis reports. The catalogue entries are the commands, skills, instructions, plugin, agent, and hook that provide this workflow in supported coding agents.
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.
git clone --depth 1 https://github.com/wbh604/UZI-SkillWrote 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/commands/wbh604/uzi-skill/ai-readiness)<a href="https://agentmods.dev/commands/wbh604/uzi-skill/ai-readiness"><img src="https://agentmods.dev/badge/commands/wbh604/uzi-skill/ai-readiness/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/commands/wbh604/uzi-skill/ai-readiness"><img src="https://agentmods.dev/badge/commands/wbh604/uzi-skill/ai-readiness.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 | $0.00031 | $0.00663 |
| Opus 5 | $0.00015 | $0.00331 |
| Sonnet 5 | $0.00006 | $0.00133 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
ai-readiness 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.
What it actually says
/ai-readiness <股票代码>
评估这一家公司在 AI 浪潮里的暴露度 / 就绪度 / 卡位强度。
改编自 anthropics/financial-services 的 PE「ai-readiness」组合扫描法，
适配成单只个股，并复用 Serenity 的 ai_chokepoint_score 作为卡位强度锚。
工作流
- 采集数据 →
extract_features(raw, dims)（已自带 AI 卡位派生特征） - 调用：
from lib.tier1.ai_readiness import build_ai_readiness result = build_ai_readiness(features, raw)
三道 Gate（个股版）
| Gate | 判据 | 数据源 |
|---|---|---|
| ① 是否真在 AI 产业链上 | ai_chain_hit |
关键词命中 |
| ② 是否有可验证的 AI 真实收入/订单/产能 | 订单/长协/产能/景气证据 | 5_chain · 15_events · 7_industry |
| ③ 卡位是否不可替代且可持续 | ai_irreplaceable + moat |
14_moat |
三 yes = 「强就绪 / Go」；否则「观察 / Wait」并注明缺口。
输出
- AI 暴露评级：强 / 中 / 弱 / 无（以
ai_chokepoint_score为主锚） - 三道 Gate：逐条 pass/fail + 依据
- Top 2-3 AI 杠杆点：算力 / 光互连 / 存储 / 供电散热 / AI 应用 / AI 赋能传统业务
- 裁决：Go · 强就绪 / Wait · 观察（含缺口）
- 一句话结论
- 组合相关项（跨公司排序 / replays / 组合 EBITDA）→ 单票 N/A
展示示例
AI 就绪度 · AXT科技 (AXTI.US)
评级「强」(卡位强度 88/100) · 通过 3/3 Gate → Go · 强就绪
① 真在 AI 链上 ✅ 命中 ['inp','磷化铟','光模块','cpo']
② 真实收入/订单 ✅ 证据词 ['订单','长协','缺货','扩产']
③ 不可替代可持续 ✅ 切换+规模壁垒达标 (moat 26/40)
Top AI 杠杆点：光互连 / 光模块 · 存储 / HBM
结论：AXT 在 AI 光互连上游卡位硬，AI 暴露可作为核心论点之一。
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 · 53 lines · 31 tokens per session scan A 665a4ffdf3c9
ai-readiness is a command published in the GitHub repository wbh604/UZI-Skill (6,820 stars, last pushed 4d ago), licensed MIT. It adds 31 tokens to every session and 663 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 commands, from other repositories
valuation-methods
Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction.
merit-reconcile
Preview or check the status of Stripe → Merit payout reconciliation (read-only).
audit-checklist
Perform an internal audit, review controls, or prepare for an external financial audit.
scan
Scan AWS account for cost optimization.
finops-status
Orientation — say where an opportunity or assignment sits in the five-step FinOps lifecycle and what unlocks next. Useful when a record has no active stage: an opportunity while its assignments do the work, an assignment whose plan has not been approved yet, or a rejected or archived assignment. Read-only; mutates…
audit
Scan for non-kernel money math. Rebuild critical flows as JournalEntrys. Replay and prove. Complements /ledger-verify.