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 duolongworld/AI_Renaissance --skill traditional_model_fusiongit clone --depth 1 https://github.com/duolongworld/AI_RenaissanceWrote 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/duolongworld/ai_renaissance/traditional_model_fusion)<a href="https://agentmods.dev/skills/duolongworld/ai_renaissance/traditional_model_fusion"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/traditional_model_fusion/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/duolongworld/ai_renaissance/traditional_model_fusion"><img src="https://agentmods.dev/badge/skills/duolongworld/ai_renaissance/traditional_model_fusion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00098 | $0.02844 |
| Opus 5 | $0.00049 | $0.01422 |
| Sonnet 5 | $0.00020 | $0.00569 |
| Haiku 4.5 | $0.00010 | $0.00284 |
Grade A, and why
traditional-model-fusion 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.
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
传统模型融合 Skill
一、定位
本 Skill 是“工程代码 + 大模型解读”的组合流程：
- Python 工程负责确定性计算：抓取或读取行情、运行四个模型、生成融合 JSON。
- 大模型负责解释性写作：读取 JSON，理解总信号、子模型投票、风险、冲突和证据，按本 Skill 的模板写成中文评估解读报告。
- 不要让大模型凭空判断股票，也不要只按文字规则输出结论；必须先得到工程程序生成的 JSON。
- 工程不再生成固定 Python Markdown 报告；所有
.md解读报告都应由大模型基于 JSON 进行评估、归纳和表达。
适用边界：
- 只分析行情数据和技术指标，不直接分析基本面、财报、新闻、估值或行业景气。
direction表示技术面方向倾向，不等于买卖指令。- 如果数据不足、数据源异常、复权口径不明、模型冲突或风险偏高，必须提示人工复核。
二、输入要求
用户至少提供以下一种输入：
- 股票代码模式：股票代码、开始日期、结束日期，最好补充标的名称。
- CSV 模式：本地 OHLCV 文件路径，字段建议包含
date, open, high, low, close, volume；open可缺，但不建议缺。
默认口径：
- 频率默认日线。
- 复权默认
none，即不复权。 - 如果复权口径没有确认，在解读报告中写入“不确定性”。
- 样本建议不少于 60 个交易日；趋势模型更建议 80 至 120 个交易日。
三、执行流程
按顺序执行：
- 明确标的名称、代码、日期范围、数据来源、复权口径。
- 进入包含
fusion_traditional_models/的工程根目录。 - 确认依赖已安装；如缺少依赖，运行：
python -m pip install -r requirements.txt
- 如果使用股票代码拉取行情，确认 EastMoney
ut已配置；如果没有，可在当前 PowerShell 会话设置：
$env:EASTMONEY_UT="fa5fd1943c7b386f172d6893dbfba10b"
- 运行 Python 程序生成 JSON。股票代码模式：
python -m fusion_traditional_models.cli --code <股票代码> --start <YYYY-MM-DD> --end <YYYY-MM-DD> --pretty --json-output "reports\<标的名>_<股票代码>_融合测试结果.json"
CSV 模式：
python -m fusion_traditional_models.cli --csv "<OHLCV文件路径.csv>" --pretty --json-output "reports\<标的名>_融合测试结果.json"
- 读取生成的 JSON 文件，重点读取：
fused_signalmodel_signalsvalidation_report
- 由大模型根据 JSON 生成中文评估解读报告，并写入：
reports\<标的名>_<股票代码>_融合结果解读.md
- 不要使用
--markdown-output或固定 Python Markdown 报告；本工程 CLI 只负责输出 JSON，.md解读报告由大模型按本模板写入文件。
四、四模型调用关系
Python 程序会把同一份 OHLCV 数据送入四个模型：
| 模型 | 工程实现 | 主要作用 |
|---|---|---|
| 量价模型 | fusion_traditional_models.models.volume_price |
判断量价配合、资金推动、VWAP 偏离和 CMF |
| 趋势模型 | fusion_traditional_models.models.trend_tracking |
判断 ADX 趋势环境、MA 与 MACD 方向 |
| 震荡模型 | fusion_traditional_models.models.oscillator |
判断 RSI、KDJ、BOLL、MACD、ROC 的动能和超买超卖 |
| 钝化/背离模型 | fusion_traditional_models.models.trend_application |
判断 KDJ/RSI 钝化、顶背离、底背离等风险提示 |
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.
- fusion_traditional_models/__init__.py 53 B runs code
- fusion_traditional_models/cli.py 2.7 KB runs code
- fusion_traditional_models/data_adapters.py 1.0 KB runs code
- fusion_traditional_models/fusion.py 8.8 KB runs code
- fusion_traditional_models/models/__init__.py 45 B runs code
- fusion_traditional_models/models/oscillator.py 9.3 KB runs code
- fusion_traditional_models/models/trend_application.py 6.5 KB runs code
- fusion_traditional_models/models/trend_tracking.py 8.8 KB runs code
- fusion_traditional_models/models/volume_price.py 11 KB runs code
- fusion_traditional_models/runner.py 1.9 KB runs code
- fusion_traditional_models/types.py 1.3 KB runs code
- fusion_traditional_models/utils.py 7.4 KB runs code
- requirements.txt 27 B
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 · 268 lines · 98 tokens per session scan A 502c2663dd96
traditional-model-fusion is a skill published in the GitHub repository duolongworld/AI_Renaissance (59 stars, last pushed 14d ago), licensed Apache-2.0. It adds 98 tokens to every session and 2,844 once invoked, about $0.0005 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.
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