Factor Benchmark

Community

minihellboy

factor-benchmark

Run FactorMiner benchmark workflows — the Table 1 Top-K freeze benchmark, memory and strategy ablations, transaction-cost pressure tests, and the full suite. Use to compare FactorMiner against baselines or to reproduce paper results. Triggers on "benchmark", "ablation", "compare to baseline", "reproduce table 1", "cost pressure", "benchmark suite".

Overview

Publisher	minihellboy
Repository	`factorminer`
Skill name	`factor-benchmark`
Stars	123
Forks	38
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by minihellboy on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/minihellboy/factorminer/tree/main/integrations/factor-researcher/plugin/skills/factor-benchmark
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/minihellboy/factorminer/tree/main/integrations/factor-researcher/plugin/skills/factor-benchmark

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/minihellboy/factorminer.git /tmp/factorminer
mkdir -p .claude/skills
cp -r /tmp/factorminer/integrations/factor-researcher/plugin/skills/factor-benchmark .claude/skills/factor-benchmark

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Factor Benchmark 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 Factor Benchmark 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 Factor Benchmark 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.

Factor Benchmark

This skill runs FactorMiner's canonical benchmark surface — the rigorous comparison layer that turns a single run into evidence.

Modes

Mode	What it answers
`table1`	Top-K freeze benchmark across configured universes vs. baselines — the headline reproduction.
`ablation-memory`	How much does experience memory contribute?
`ablation-strategy`	Effect of memory policy × dependence metric × backend.
`cost-pressure`	How does the library hold up under rising transaction costs?
`efficiency`	Operator- and factor-level runtime/compute cost.
`suite`	The full benchmark suite in one run.

Workflow

Run a benchmark

bash
factorminer -o output/bench benchmark table1 --data path/to/market_data.csv

bash
factorminer -o output/bench benchmark suite --data path/to/market_data.csv

Pass a pre-mined library to benchmark a specific run rather than mining fresh:

bash
factorminer -o output/bench benchmark table1 \
  --data market_data.csv \
  --factor-miner-library output/run1/factor_library.json

efficiency takes no data — it profiles the engine itself.

Read the result

The CLI prints a per-universe summary (library IC, ICIR, avg |ρ|) and writes JSON payloads into the output directory. Fold those JSON files into the research note with factor-report --benchmark.

Interpreting ablations

An ablation that removes a feature and barely moves the metric means that feature is not earning its compute on this dataset — report that plainly.
cost-pressure is the honesty check: a library that only wins at zero cost is not a result.

Guardrails

Benchmark numbers are comparative research evidence, not a performance guarantee.
Use the same dataset and splits across compared runs, or the comparison is meaningless.
Reproduction claims must cite the exact config and run directory.

Frequently asked questions

What does the Factor Benchmark AI skill do?

Why use Factor Benchmark on TypingMind?

Because you install it once and use it with any model. Factor Benchmark 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 Factor Benchmark in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/minihellboy/factorminer/tree/main/integrations/factor-researcher/plugin/skills/factor-benchmark. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Factor Benchmark?

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 Factor Benchmark?

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

Is the Factor Benchmark AI skill free?

Yes. It is published on GitHub by minihellboy under the MIT 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Factor Benchmark

Modes

Workflow

Run a benchmark

Read the result

Interpreting ablations

Guardrails

Frequently asked questions

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