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 adambbhe/TDX-finance-mcp-plugin-v3 --skill tdx-obv-ma-strategygit clone --depth 1 https://github.com/adambbhe/TDX-finance-mcp-plugin-v3Wrote 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/adambbhe/tdx-finance-mcp-plugin-v3/tdx-obv-ma-strategy)<a href="https://agentmods.dev/skills/adambbhe/tdx-finance-mcp-plugin-v3/tdx-obv-ma-strategy"><img src="https://agentmods.dev/badge/skills/adambbhe/tdx-finance-mcp-plugin-v3/tdx-obv-ma-strategy/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/adambbhe/tdx-finance-mcp-plugin-v3/tdx-obv-ma-strategy"><img src="https://agentmods.dev/badge/skills/adambbhe/tdx-finance-mcp-plugin-v3/tdx-obv-ma-strategy.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 | $0.00145 | $0.02515 |
| Opus 5.5 | $0.00058 | $0.01006 |
| Sonnet 5.5 | $0.00029 | $0.00503 |
| Haiku 4.5 | $0.00015 | $0.00251 |
Grade A, and why
tdx-obv-ma-strategy 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 2d 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 — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OBV + MA 量价共振策略
价格会骗人，成交量不会。这套模板把价格趋势（MA）和资金进出（OBV）拆成两个独立维度各 50 分， 用两者的共振与背离做判断——只有价涨且 OBV 同步推高，上涨才有资金背书。
先决条件
通达信终端必须在跑。不确定就先 tdx_status → 必要时 tdx_connect。
扫描脚本直连 tqcenter（不走 MCP），所以全市场扫描不占对话上下文。
云端通道（无需本地终端，供任意 agent 使用）
--channel cloud 走 tdxhub TQLEX 后端（与 tdx-finance-mcp 插件同源），只需 token
（环境变量 TDX_API_TOKEN/TDX_API_KEY，或向上查找 .mcp.json 自动读取）。
OBV/MAOBV/MA 由 PBFXT 日K本地复算（与 Volume 同源自算，量纲自动对齐，无需 --sector）。
# 个股诊断（其他 agent 只要有 tdx-finance-mcp token 即可运行）
py -3.13 scripts/obv_ma_scan.py --channel cloud --codes 600519.SH --json
# 批量（清单外置：每行一个代码）
py -3.13 scripts/obv_ma_scan.py --channel cloud --codes-file list.txt --min-score 85 --out out.csv
限制：cloud 不支持 --universe（云端无股票池接口）——全市场扫描仍需 local；
--count 默认 250，两通道口径一致可直接对拍。
三种模式（按用户意图选一个）
| 用户想要 | 模式 | 怎么做 |
|---|---|---|
| 「帮我看看 600674 的量价」 | 个股诊断 | 脚本 --codes + --json，按 references/scoring.md 的模板出诊断 |
| 「全A/沪深300 里选量价共振的票」 | 全市场选股 | 脚本 --universe + --min-score，出排序表 |
| 「我持仓的 XXX 还能拿吗」 | 择时信号 | 先跑个股诊断，再按下面的操作映射给动作+仓位+止损 |
模式一：个股诊断
py -3.13 <skill>/scripts/obv_ma_scan.py --codes 600674.SH,000001.SZ --json
拿到 JSON 后按 references/scoring.md 的输出模板写诊断。必须解释分数，不能只报数：
低分要说清是价格拖累还是量能拖累，命中背离要单独点出来。
模式二：全市场选股
# 沪深300 里的强共振
py -3.13 <skill>/scripts/obv_ma_scan.py --universe 23 --min-score 90 --no-topdiv --top 20
# 全A，只要双多共振，结果落盘
py -3.13 <skill>/scripts/obv_ma_scan.py --universe 5 --min-score 85 --quadrant 双多共振 \
--no-topdiv --min-amount 1 --out D:\cc-workspace\tdx-local\obv_ma_scan.csv --top 30
# 潜伏底背离（价弱量强，左侧）
py -3.13 <skill>/scripts/obv_ma_scan.py --universe 5 --quadrant 价弱量强 --min-amount 1 --top 30
选股模式一律带 --min-amount 1（20 日均额下限，亿元）。薄票的 OBV 回撤天数噪音大，
一笔大单就能动好几天的量；全A 均额中位数才 1.19 亿，不滤会有大半是流动性陷阱。
池子编码：5 全A · 50 沪深A · 23 沪深300 · 24 中证500 · 51 创业板 · 52 科创板 · 0 自选股 · 1 持仓股。
完整清单 tdx_list_enums(group='stock_list_category')。
阈值定标：--min-score 不是绝对标准，取决于行情冷暖。牛市里全A 能有上千只 ≥80，
这时要提到 90 才够挑。先不带阈值跑一遍看分布，再定线——别把宽市场的 80 分当成强信号报给用户。
What ships with it
4 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.
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.
- 2d ago First seen · 139 lines · 145 tokens per session scan A 30766ebc1e37
tdx-obv-ma-strategy is a skill published in the GitHub repository adambbhe/TDX-finance-mcp-plugin-v3 (42 stars, last pushed 3d ago), licensed MIT. It adds 145 tokens to every session and 2,515 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-10-25.
Other skills, from other repositories
gendangzou-skill
A Chinese-language research tool for analyzing sectors in China's stock market using platform, API, and independently installable application data.
china-dcf-model
A documented discounted-cash-flow model for valuing non-financial A-share companies. A discounted-cash-flow model estimates today’s value from expected future cash flows and makes its assumptions, valuation date, and adjustments explicit.
industry-etf-research
A research workflow for studying Chinese A-share industry ETFs and the indexes they track. It compares index rules, holdings, company fundamentals, industry conditions, valuations and market evidence.
pptx-author
Produce a .pptx file on disk for headless Chinese A-share market research, using python-pptx with Chinese font support. Use when no controllable Office session is available.
china-market-data
A routing and quality-check process for obtaining Chinese stock-market and company-financial data from sources such as Tushare Pro and AKShare.
a-share-factor-validation
A research checklist and toolkit for testing stock-selection factors and backtests in China's A-share market, the mainland Chinese stock market. It checks whether results avoid future-data leakage and account for trading rules, costs, and statistical uncertainty.