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Test Pruner

Community
hex
test-pruner

Tests in a named area duplicate stronger tests, re-assert the implementation, or keep test-only seams alive, and need pruning.

Overview

Publisherhex
Repositoryclaude-council
Skill nametest-pruner
Stars
866
Forks
106
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 hex on GitHub. Read the source before you install it.

Installation

Install the Test Pruner 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/hex/claude-council/tree/main/mods/council-pane/specialists/test-pruner
  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/hex/claude-council.git /tmp/claude-council
mkdir -p .claude/skills
cp -r /tmp/claude-council/mods/council-pane/specialists/test-pruner .claude/skills/test-pruner
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Test Pruner 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 Test Pruner 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 Test Pruner 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.

Test Pruner

Prune low-value tests in the named area. Optimize for confidence, not deletion count. Read the project's own instructions (AGENTS.md, CONTRIBUTING) first.

Junk patterns

The shared checklist for both modes: the authoring gate rejects a new test that matches one, and audits hunt for existing tests that do.

  • assertion-free coverage probes;
  • self-comparisons and identity copiers;
  • copied fixtures, inventories, manifests, or export lists;
  • exact source, import, or string greps;
  • private predicate or call-shape tests duplicated at real boundaries;
  • duplicate invocations of the same contract;
  • provider-local replays of shared helpers;
  • tests whose only purpose is preserving test-only exports, globals, or wrappers;
  • dead production code whose only callers are tests;
  • expected values produced by the helper or renderer under test;
  • mocks that implement the asserted behavior, or one identical mock standing in for different APIs;
  • fixtures that supply the receipt, admission, or callback ordering the owner should produce, or persistence asserted against a store the path never writes;
  • capability tests that restate declared flags instead of exercising the delivery or acknowledgement the flag promises;
  • negative controls that pass for an unrelated reason, such as a denial from a different guard or a rejection the production path never reaches;
  • names or fixtures that promise more than the input exercises, such as a "retires the window" test asserting the window was not cleared.

Value bar

Tests justify their maintenance cost by protecting behavior, a credible regression, or an independently meaningful contract. In an audit, an existing test that must change for behavior-preserving source reorganization is suspect, not automatically deletable; the authoring gate still rejects new ones.

Before judging a candidate, read the complete test and production owner, its entry point, callers, callees, sibling implementations, overlapping tests, CI routing, and relevant history. Read root and scoped AGENTS.md files first. When the test claims dependency-backed behavior, inspect the dependency source or types directly.

Retention bar

Keep a test when it independently enforces a public API, plugin SDK, protocol, config, migration, storage, security, platform, default, prompt-byte, generated cross-language, package, release, or architecture contract. Also keep:

  • call ordering when order is observable behavior;
  • regressions with a credible failure mode;
  • source inspection when it is the cheapest independent guard: it fails when the contract changes (the user-facing key, byte, or path) and survives an identifier-only refactor;
  • a retained test that fails on the baseline: treat it as a possible product bug, reproduce it, and repair the owner rather than deleting it.

Static or slow is not a deletion reason. A test that resembles implementation may still be the independent contract; prove otherwise before removing it.

Candidate evidence

Record every field below before editing. A missing field means the candidate is not ready for deletion:

  • exact test name and location;
  • what failure it can actually detect;
  • non-test callers of the covered production or support seam;
  • stronger remaining owner-boundary proof, or why no proof is needed;
  • relevant history and the reason the test or seam exists;
  • production or test-support deletion unlocked;
  • risk and the focused validation command.

Edit shape

Choose one coherent owner-boundary batch. Delete obsolete test-only exports, globals, wrappers, and dead production paths instead of preserving aliases. Move retained regressions to their canonical owners. Consolidate repeated package or dependency assertions into one generic contract.

Prefer net-negative production LOC. Do not add replacement tests that restate the same implementation, and do not convert uncertain candidates into cleanup to increase deletion counts.

Handoff

Report:

  • removed low-value categories;
  • production simplifications;
  • retained false positives and why they remain valuable;
  • proof actually run.

Adapted from openclaw's test-audit skill (MIT, OpenClaw Foundation); see ../LICENSE-openclaw.

Frequently asked questions

What does the Test Pruner AI skill do?

Tests in a named area duplicate stronger tests, re-assert the implementation, or keep test-only seams alive, and need pruning.

Why use Test Pruner on TypingMind?

Because you install it once and use it with any model. Test Pruner 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 Test Pruner in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hex/claude-council/tree/main/mods/council-pane/specialists/test-pruner. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Test Pruner?

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 Test Pruner?

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

Is the Test Pruner AI skill free?

Yes. It is published on GitHub by hex 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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