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Small Model Regression Eval

Community
Firstp1ck
small-model-regression-eval

Plan and report a bounded comparison of fixed small-model task fixtures without claiming unrun live results or substituting a model/provider. Do not use for normal task execution.

Overview

PublisherFirstp1ck
Repositorypi-coding-agent-forge
Skill namesmall-model-regression-eval
Stars
93
Forks
22
Bundled files
2
LicenseMIT
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

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

  • Open source

    Published by Firstp1ck on GitHub. Read the source before you install it.

Installation

Install the Small Model Regression Eval 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/Firstp1ck/pi-coding-agent-forge/tree/main/pi-extension-small-modal-reliability/skills/small-model-regression-eval
  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/Firstp1ck/pi-coding-agent-forge.git /tmp/pi-coding-agent-forge
mkdir -p .claude/skills
cp -r /tmp/pi-coding-agent-forge/pi-extension-small-modal-reliability/skills/small-model-regression-eval .claude/skills/small-model-regression-eval
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Small Model Regression Eval 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 Small Model Regression Eval 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 Small Model Regression Eval 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.

Small-model regression evaluation

Use this skill to compare a model, quantization, prompt, guard profile, or skill revision against frozen independent outcomes. It separates deterministic mechanism tests from live model quality results.

When to use

Should trigger

  • “Compare this configured local model with the guarded workflow on held-out tasks.”
  • “Report whether a reliability revision regressed retrieval, agentic, or coding fixtures.”
  • “Create a baseline-versus-integrated evaluation plan without making live calls.”

Should not trigger

  • “Implement this feature.”
  • “Use a model to solve the current task.”
  • “Publish or enable a model.”

Inputs

Provide a frozen task suite, independent expected outcomes, exact provider/model ID, quantization and sampling settings when applicable, package revision, guard profile, tool configuration, timeout, sample budget, and whether assisted mode is separately authorized.

Portable workflow

  1. Freeze sanitized task inputs and independent success checks before tuning.
  2. Run deterministic guard fixtures separately from model executions.
  3. Compare baseline, current extension, isolated feature, integrated guarded, and separately authorized assisted configurations.
  4. Keep assisted results in their own column; do not combine them with small-model-only scores.
  5. Record exact model/provider identity and all execution settings for every live run.
  6. Report unavailable live evaluation truthfully instead of substituting a model, provider, or adapter.

Safety and reporting boundary

Do not use the extension completion gate as the evaluation oracle. Do not make network or provider calls merely because a model name was supplied. Never claim an improvement from fixtures, mechanism tests, or unrun configurations.

Verification

Verify that each actual task outcome is checked by an independent oracle, frozen inputs remain unchanged, configuration identity is complete, failures are retained, and unavailable live execution records zero calls with a reason.

Pi adapter

Pending ordinary/queued instructions require /reliability input confirm before an evaluation can proceed. This creates new native task authority, not proof of prior ingress; it never substitutes for the exact-model evaluation dialog. Sanitization/redaction is pattern-limited best effort: inspect the disclosed packet and do not assume it is secret-free.

Run /reliability eval --suite retrieval|agentic|coding|all [--model provider/id] [--run] [--write]. The default adapter is offline-only: it reports frozen fixtures and exactly why a requested live run is unavailable. --run needs one exact configured model, an available matching native adapter, and native confirmation of the sanitized case packet; a UI-capable RPC invocation may show that dialog, while no-UI paths make zero calls. Without --run, no provider is called. An approved live run never substitutes another provider or model.

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 Small Model Regression Eval AI skill do?

Plan and report a bounded comparison of fixed small-model task fixtures without claiming unrun live results or substituting a model/provider. Do not use for normal task execution.

Why use Small Model Regression Eval on TypingMind?

Because you install it once and use it with any model. Small Model Regression Eval 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 Small Model Regression Eval in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Firstp1ck/pi-coding-agent-forge/tree/main/pi-extension-small-modal-reliability/skills/small-model-regression-eval. 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 Small Model Regression Eval?

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 Small Model Regression Eval?

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

Is the Small Model Regression Eval AI skill free?

Yes. It is published on GitHub by Firstp1ck 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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