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Evaluate Auto Implement Eligibility

Organization
warpdotdev
evaluate-auto-implement-eligibility

Evaluates whether a triaged GitHub issue is eligible for the factory auto-implement queue. Use during issue triage after the primary triage pass is complete.

Overview

Publisherwarpdotdev
Repositoryoz-for-oss
Skill nameevaluate-auto-implement-eligibility
Stars
313
Forks
43
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Evaluate Auto Implement Eligibility 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/warpdotdev/oz-for-oss/tree/main/.agents/skills/evaluate-auto-implement-eligibility-local
  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/warpdotdev/oz-for-oss.git /tmp/oz-for-oss
mkdir -p .claude/skills
cp -r /tmp/oz-for-oss/.agents/skills/evaluate-auto-implement-eligibility-local .claude/skills/evaluate-auto-implement-eligibility
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evaluate Auto Implement Eligibility 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 Evaluate Auto Implement Eligibility 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 Evaluate Auto Implement Eligibility 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.

Factory Auto-Implement Eligibility

Determine whether this issue is a candidate for the factory auto-implement queue. When eligible, the workflow adds the factory-auto-implement label only. It does not assign oz-agent and does not apply the creation-time auto-implement label (that label is reserved for trusted issues labeled at open time, which skip triage entirely). The bar is deliberately high.

Decision rule

All of the following gates must pass. If any gate fails, or if you are uncertain about any gate, the answer is false.

Gate 1: Bug only

The issue describes an observed behavior that is clearly wrong. Enhancements, feature requests, performance improvements, and vague improvement requests are never eligible, even if fully specified.

Gate 2: Visual bugs must be objectively wrong, not a matter of taste

Exclude issues where the desired outcome is a judgment call — e.g. "this button should be a different color", "this spacing feels off", or any report that expresses a personal opinion about appearance. A visual bug is eligible only when the correct state is unambiguous: a missing icon, a broken layout that clearly corrupts the UI, text that is visibly truncated or overlapping, or a rendering glitch with a clear non-rendering root cause. When in doubt, exclude it.

Gate 3: Root cause known with high or medium confidence

The triage investigation must have identified a plausible root cause and relevant code. If the root cause is unknown or speculative, the answer is false — even if reproducibility is high.

Gate 4: Unambiguous definition of done

There is exactly one reasonable interpretation of what "fixed" means. A maintainer reading the issue would agree on the expected outcome without discussion. Exclude anything where the correct fix depends on a product judgment call.

Gate 5: Client-side only

The fix touches only the client-side desktop app. Exclude any issue involving: server or API, authentication, billing, cloud sync, session sharing, telemetry backend, or the Oz platform backend. When in doubt, the answer is false.

Gate 6: Localized scope

The fix touches one component or subsystem. Exclude issues requiring cross-cutting changes, modifications to shared core primitives, or anything that would move architectural boundaries.

Gate 7: Not security- or data-sensitive

The issue does not touch auth, permissions, billing, or data integrity. The failure mode if the implementation is wrong is recoverable (behavioral glitch, not data loss or a security hole).

Gate 8: Small estimated effort

The fix path is apparent from the issue description and likely touches a small number of files. A confident engineer could complete it in a few hours without needing to understand a large or unfamiliar subsystem.

Output

Add these two fields to triage_result.json:

json
{
  "factory_auto_implement": true,
  "factory_auto_implement_reasoning": "one sentence naming the determining factor"
}

Always populate both fields. When false, factory_auto_implement_reasoning must name the specific gate that failed. When true, briefly state the positive case (e.g. "client-side crash with known root cause, clear fix path, and no follow-up questions needed").

Do not emit the legacy auto_implement / auto_implement_reasoning fields. Do not request oz-agent assignment or the auto-implement label from this skill.

Default

When in doubt, the answer is false. A missed candidate is a minor inefficiency. A false positive queues factory work that should not run, wasting compute and potentially producing an unwanted draft PR.

Frequently asked questions

What does the Evaluate Auto Implement Eligibility AI skill do?

Evaluates whether a triaged GitHub issue is eligible for the factory auto-implement queue. Use during issue triage after the primary triage pass is complete.

Why use Evaluate Auto Implement Eligibility on TypingMind?

Because you install it once and use it with any model. Evaluate Auto Implement Eligibility 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 Evaluate Auto Implement Eligibility in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/warpdotdev/oz-for-oss/tree/main/.agents/skills/evaluate-auto-implement-eligibility-local. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Evaluate Auto Implement Eligibility?

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 Evaluate Auto Implement Eligibility?

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

Is the Evaluate Auto Implement Eligibility AI skill free?

Yes. It is published on GitHub by warpdotdev 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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