Backtesting Frameworks logo

Backtesting Frameworks

Organization
sandbaseai
backtesting-frameworks

Apply backtesting frameworks in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses backtesting frameworks or its strengths fit the task.

Overview

Publishersandbaseai
Repositorysandbase-skills
Skill namebacktesting-frameworks
Stars
200
Forks
21
Bundled files
1
LicenseApache-2.0
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Backtesting Frameworks 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/sandbaseai/sandbase-skills/tree/main/marketing/backtesting-frameworks
  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/sandbaseai/sandbase-skills.git /tmp/sandbase-skills
mkdir -p .claude/skills
cp -r /tmp/sandbase-skills/marketing/backtesting-frameworks .claude/skills/backtesting-frameworks
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Backtesting Frameworks 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 Backtesting Frameworks 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 Backtesting Frameworks 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.

Backtesting Frameworks

Use this Skill to produce a bounded, verifiable Backtesting Frameworks outcome. Preserve the user's chosen stack, source material, and authorization boundaries.

Read the SandBase API map only when the task genuinely needs an external data source or generative model.

Workflow

  1. Inspect the available files, runtime, versions, inputs, and existing conventions before deciding what to change.
  2. Restate the requested outcome, constraints, acceptance checks, and any assumption that could change the result.
  3. Produce the smallest complete implementation, analysis, or artifact that satisfies those checks.
  4. Verify the real output with appropriate tests, previews, calculations, or source comparison; do not infer success from file creation alone.
  5. Return the deliverable, evidence of validation, material assumptions, and unresolved limitations.

Quality gates

  • Inspect shapes, types, units, missing values, sampling, target leakage, and train/test boundaries before modeling or transformation.
  • Pin or record relevant library versions, random seeds, parameters, and environment assumptions for reproducibility.
  • Validate against a baseline or independent calculation and report diagnostics, uncertainty, failure modes, and resource use.
  • Use user-provided or authorized data, preserve an audit trail, verify calculations independently, and label professional-review requirements; do not present the result as investment, accounting, or audit assurance.
  • Control look-ahead bias, survivorship bias, transaction costs, benchmark choice, and time alignment; keep hypothetical results clearly labeled.

SandBase boundary

Keep the core Backtesting Frameworks work local. Use SandBase only for an explicitly requested external dataset or model inference step that is not part of the local analysis.

  1. Call sandbase_discover with a short capability query.
  2. Call sandbase_inspect for viable candidates and compare the live schema, coverage, limits, output, execution mode, and price.
  3. Prefer a dedicated tool or API the user already has. Send only the minimum necessary data.
  4. Before any paid call, show the endpoint, important arguments, current unit price, call count, and total estimate or uncertainty, then obtain confirmation.
  5. Use sandbase_account before an approved multi-call batch and call sandbase_run only with current schema-defined arguments.
  6. Poll asynchronous work with sandbase_run_get using the same run ID; never resubmit merely because it is pending.
  7. Use sandbase_runs only to recover status or reconcile observed cost.

If SandBase is unavailable, continue with local work and authorized sources when possible. Do not silently switch providers, fabricate external results, or claim a generation or retrieval succeeded.

Handoff

Provide the completed artifact or findings, concise reproduction steps, checks actually run, source or asset provenance, SandBase endpoint and run IDs when used, observed cost when available, and any follow-up that still requires user action.

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 Backtesting Frameworks AI skill do?

Apply backtesting frameworks in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses backtesting frameworks or its strengths fit the task.

Why use Backtesting Frameworks on TypingMind?

Because you install it once and use it with any model. Backtesting Frameworks 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 Backtesting Frameworks in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sandbaseai/sandbase-skills/tree/main/marketing/backtesting-frameworks. 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 Backtesting Frameworks?

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 Backtesting Frameworks?

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

Is the Backtesting Frameworks AI skill free?

Yes. It is published on GitHub by sandbaseai under the Apache-2.0 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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