Skill Quant Factor Skill Factory logo

Skill Quant Factor Skill Factory

Organization
quantskills
skill-quant-factor-skill-factory

Use when converting OHLCV alpha ideas into QuantSkills organization factor Skills, batch-generating non-duplicate framework-neutral quant factor Skill folders, validating them on cached real market data such as AkShare A-share and Yahoo US data, and writing factor evaluation reports.

Overview

Publisherquantskills
Repositoryskill-quant-factor-skill-factory
Skill nameskill-quant-factor-skill-factory
Stars
73
Forks
9
Bundled files
6
LicenseGPL-3.0-only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by quantskills on GitHub. Read the source before you install it.

Installation

Install the Skill Quant Factor Skill Factory AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/quantskills/skill-quant-factor-skill-factory/tree/main
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/quantskills/skill-quant-factor-skill-factory.git \
  .claude/skills/skill-quant-factor-skill-factory
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Skill Quant Factor Skill Factory in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Skill Quant Factor Skill Factory on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Skill Quant Factor Skill Factory is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

json
{
  "version": 1,
  "task": {
    "placeholder": "请说明要生成或维护的 OHLCV 因子技能主题、现有索引与输出要求",
    "required": true
  },
  "fields": [
    {
      "key": "count",
      "label": "生成数量",
      "type": "number",
      "default": "10",
      "required": true
    },
    {
      "key": "start_id",
      "label": "起始因子 ID",
      "type": "number",
      "placeholder": "例如:1001"
    },
    {
      "key": "market",
      "label": "验证市场",
      "type": "select",
      "default": "both",
      "options": [
        { "value": "both", "label": "A 股与美股" },
        { "value": "cn", "label": "A 股" },
        { "value": "us", "label": "美股" }
      ]
    }
  ],
  "prompt_template": "{{#task}}任务与材料:\n{{task}}\n\n{{/task}}{{#attachments}}用户上传的材料(已放入工作区):\n{{attachments}}\n\n{{/attachments}}使用量化因子技能工厂生成 {{count}} 个框架无关的 OHLCV 因子 Skill。{{#start_id}}从起始 ID {{start_id}} 起生成;{{/start_id}}未指定起始 ID 时按既有索引推断下一个可用编号。并在 {{market}} 真实缓存行情上验证;生成前检查既有索引避免重复,确保每个目录具备规定的技能说明、双语文档、因子与验证脚本、真实验证结果、公式参考和适配文件,核验通过数、数据供应商、市场覆盖、合并索引重复数及抽样验证结果,输出中文报告。"
}

Skill Quant Factor Skill Factory

Use this QuantSkills organization Skill when the user wants to create, extend, or maintain a library of framework-neutral quant factor Skills from OHLCV data.

The standard contract is:

  • Factors are framework-neutral Python Skills for the QuantSkills organization.
  • Users bring their own market data; generated factor code only requires open, high, low, close, volume, plus optional date, symbol, and market.
  • Validation must use cached real OHLCV data when available. Do not describe synthetic validation as real validation.
  • For China data, prefer AkShare A-share cache. For US data, prefer Yahoo Finance cache.
  • Every generated factor folder must include SKILL.md, bilingual README.md, scripts/factor.py, scripts/validate.py, validation_real/result.json, validation_real/report.md, references/formula.md, and agents/openai.yaml.
  • Before delivery, verify uniqueness across all previous factor indexes and confirm the generated package is complete.

Workflow

  1. Inspect the current project state:

    powershell
    Get-ChildItem
    Get-Content .\real_market_data\panels\panel_manifest.json
  2. Confirm the real data panel contains market and vendor evidence:

    json
    {
      "markets": {"cn": 98, "us": 50},
      "sources": ["akshare", "yahoo"]
    }

    Exact counts can vary by project, but the report must state the actual counts.

  3. Generate the next batch with the reusable script.

    If the project already has tools/real_data_factor_pipeline.py, copy only scripts/generate_factor_skill_batch.py from this skill into the project's tools/ folder. If not, copy the validated pipeline scripts from a previous project first, then adapt data download paths.

    Example:

    powershell
    $env:PYTHONUTF8='1'
    python .\tools\generate_factor_skill_batch.py `
      --count 200 `
      --start-id 1001 `
      --existing-index .\real_data_factor_skills_all_1000_index.json `
      --output-root .\real_data_factor_skills_extra_200_next `
      --combined-index .\real_data_factor_skills_all_1200_index.json `
      --report-name extra_200_next_factor_evaluation_report.md
  4. Run validation and acceptance checks:

    powershell
    python -m py_compile .\tools\generate_factor_skill_batch.py
    python .\tools\generate_factor_skill_batch.py --count <N> --start-id <ID> --existing-index <index.json> --output-root <folder> --combined-index <index.json> --report-name <report.md>
  5. Audit the output:

    • Count generated factor directories equals the requested count.
    • validation_summary_real.json has the requested count.
    • All rows have status == "pass" unless the user explicitly accepts review rows.
    • All rows show the expected real-data market coverage.
    • Combined index has no duplicate slug.
    • Run scripts/validate.py for the first, middle, and last generated factor folders.

Output Summary

Always finish with:

  • output folder
  • factor ID range
  • pass count
  • real data panel counts and market vendors
  • combined index count and duplicate count
  • report path
  • sampled scripts/validate.py result

Important Guardrails

  • Do not claim Yahoo data is present unless real_market_data/raw/yahoo_us/*.parquet and the panel manifest prove it.
  • Do not overwrite previous batches unless the user asks for regeneration.
  • Use UTF-8 when reading or writing Chinese filenames and Markdown: set PYTHONUTF8=1 in PowerShell.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Skill Quant Factor Skill Factory AI skill do?

Use when converting OHLCV alpha ideas into QuantSkills organization factor Skills, batch-generating non-duplicate framework-neutral quant factor Skill folders, validating them on cached real market data such as AkShare A-share and Yahoo US data, and writing factor evaluation reports.

Why use Skill Quant Factor Skill Factory on TypingMind?

Because you install it once and use it with any model. Skill Quant Factor Skill Factory is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Skill Quant Factor Skill Factory in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/quantskills/skill-quant-factor-skill-factory/tree/main. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Skill Quant Factor Skill Factory?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Skill Quant Factor Skill Factory?

As many as you like. As long as a model supports skills, you can use Skill Quant Factor Skill Factory with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Skill Quant Factor Skill Factory AI skill free?

Yes. It is published on GitHub by quantskills under the GPL-3.0-only license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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