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Consolidate Test Suites

Organization
instructa
consolidate-test-suites

Decide where durable test coverage belongs and clean up temporary test probes. Use while testing a bug fix or architectural change, or before finishing work that added tests, fixtures, snapshots, diagnostics, or temporary assertions. Select one owning layer, reuse canonical suites, merge unique signal, and remove task-created probe residue without disturbing pre-existing work.

Overview

Publisherinstructa
Repositoryagent-skills
Skill nameconsolidate-test-suites
Stars
141
Forks
16
Bundled files
1
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 instructa on GitHub. Read the source before you install it.

Installation

Install the Consolidate Test Suites 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.
  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/instructa/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/skills/engineering/consolidate-test-suites .claude/skills/consolidate-test-suites
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Consolidate Test Suites 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 Consolidate Test Suites 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 Consolidate Test Suites 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.

Consolidate Test Suites

Purpose: place each invariant in one owning test layer only.

Definitions:

  • Invariant: the rule that must stay true.
  • Owning layer: the lowest layer that truly owns and can prove that rule.
  • Canonical suite: the normal existing suite for that owning layer.
  • Probe: temporary test code, fixture, script, snapshot, diagnostic, assertion, instrumentation, or helper created to investigate or reproduce one task.

Default: reuse an existing canonical suite. Do not create a new standalone regression test unless the exception rule below allows it.

Probes are useful working material, not permanent coverage by default. Before finishing, classify every probe created for the current task as PROMOTE, MERGE, or DROP.

Hard Rules

  • You MUST identify the invariant before adding or moving any test.
  • You MUST identify one primary owning layer: unit, integration, or end-to-end.
  • You MUST first try to place coverage in an existing canonical suite for that layer.
  • You MUST prefer editing an existing test file over creating a new test file.
  • You MUST NOT add the same invariant in multiple layers unless each layer covers a different failure mode. If you keep more than one layer, name the distinct failure mode for each.
  • You MUST NOT add tests that lock in implementation details unless that implementation unit itself owns the invariant.
  • You MUST NOT create a standalone regression test because it is faster or easier.
  • If you cannot name the invariant and the owning layer, STOP. Report that placement is not justified.
  • You MUST remove current-task probes that are not promoted or merged, including temporary code inserted into a permanent test or production file.
  • You MUST preserve tests and dirty changes that predate the current task. Never delete or rewrite them merely to reduce test count.
  • If the request is review-only, report probe dispositions and cleanup recommendations without modifying files.

Required Decision Order

Choose the first option that fits:

  1. Add to an existing test in an existing file in the owning layer.
  2. Add a new test to an existing canonical file in the owning layer.
  3. Create a new file inside the existing canonical suite in the owning layer.
  4. Create a standalone regression-style test only if the Exception Rule passes.

Owning Layer Rules

Choose unit when:

  • one module owns the rule, and
  • the bug reproduces without I/O, transport, persistence, retries, IPC, orchestration, or lifecycle coupling.

Choose integration when:

  • the rule lives at a boundary between components, or
  • the bug depends on serialization, persistence, ordering, replay, retries, IPC, process lifecycle, or multi-component coordination.

Choose end-to-end only when:

  • the user-visible contract cannot be trusted from lower-layer tests alone.

Tie-breakers:

  • If torn between unit and integration, choose integration.
  • Never choose end-to-end to compensate for uncertainty.
  • Never choose a higher layer just because it is easier to reproduce there.

Exception Rule for Standalone Regression Tests

A standalone regression-style test is allowed only if ALL are true:

  • no existing canonical suite can express the case cleanly
  • the reproduction is deterministic
  • the case has durable incident or contract value
  • adding it to the canonical suite would make that suite less clear

If any condition is false, fold the coverage into the canonical suite.

Probe Lifecycle

Use temporary probes when they materially help reproduce, diagnose, or falsify a suspected bug. Keep their lifecycle lightweight:

  1. Before editing tests, inspect the existing test-related status and diff so pre-existing work is distinguishable from task-created probes. Keep this baseline in the working context; do not create tracking files for it.
  2. Once the behavior is accepted, compare each task-created probe with the nearest canonical suite and choose exactly one disposition:
    • PROMOTE: keep the smallest version because it protects a unique durable invariant at the stable owning boundary.
    • MERGE: move only its unique signal into an existing canonical test, then remove the redundant probe.
    • DROP: remove it because it is diagnostic-only, redundant, speculative, implementation-coupled, flaky, slow, or low-value.
  3. Parameterize or extend an existing behavior test when several examples prove the same invariant. Test count and coverage percentage are not reasons to keep another test.
  4. Remove every unpromoted task-created probe before final verification. If its ownership is uncertain, preserve it and report the uncertainty instead of guessing that it is safe to delete.

Promote a probe only when it is deterministic, protects an accepted observable contract, would catch a meaningful regression, adds signal not already owned elsewhere, and has maintenance cost proportional to its risk. When cheap and safe, confirm that the proposed test fails against the faulty behavior and passes against the fix; do not build extra infrastructure merely to produce that proof.

Duplicate Cleanup

After placing coverage:

  1. Search for tests that assert the same invariant.
  2. Keep the strongest owned location.
  3. Merge any unique assertions into that location.
  4. Delete or simplify weaker duplicates.
  5. Rename tests by behavior and owner, not by ticket number or bug history.

Delete or rewrite pre-existing tests only when the task includes suite consolidation and duplicate coverage has been demonstrated. Routine bug-fix work may clean up only the probes it created.

Verification

Before finishing:

  1. Run the narrowest relevant test target first.
  2. Run required typecheck, build, or lint steps for touched code.
  3. Report exactly what was run and whether it passed.

Default Output Format

Use this format by default:

Invariant:

Owning layer: <unit | integration | end-to-end>

Target suite/file:

Action: <reuse existing test | add to existing suite | create file in canonical suite | keep standalone regression>

Why this layer owns it:

Duplicates to merge/delete: <list or "none">

Current-task probes: <PROMOTE / MERGE / DROP for each, or "none">

Probe cleanup completed: <removed paths or inline code, or "none">

Verification run:

Residual risk: <what is still not covered, if anything>

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 Consolidate Test Suites AI skill do?

Decide where durable test coverage belongs and clean up temporary test probes. Use while testing a bug fix or architectural change, or before finishing work that added tests, fixtures, snapshots, diagnostics, or temporary assertions. Select one owning layer, reuse canonical suites, merge unique signal, and remove task-created probe residue without disturbing pre-existing work.

Why use Consolidate Test Suites on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/instructa/agent-skills/tree/main/skills/engineering/consolidate-test-suites. 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 Consolidate Test Suites?

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 Consolidate Test Suites?

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

Is the Consolidate Test Suites AI skill free?

It is published on GitHub by instructa. Check the repository for licensing terms. 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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