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Official agent skill

Test Coverage Improver

by openai in openai/openai-agents-python

Measure Python SDK coverage or address measured coverage gaps.

OfficialMITAuto-check passedTesting & QA

Install Test Coverage Improver

skills CLI
$ npx skills add openai/openai-agents-python --skill test-coverage-improver -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openai/openai-agents-python test-coverage-improver --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/test-coverage-improver .claude/skills/test-coverage-improver && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
test-coverage-improver
GitHub stars
30k
Token cost
~687 tokens
SKILL.md length
346 words
Files
2
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Measure Python SDK coverage or address measured coverage gaps.

  • Works in 5 steps: Inspect current .coverage, coverage.xml,… → Identify uncovered behavior within the… → For an assessment, report evidence and… → …
  • Coverage audits and metric regressions
  • SKILL.md covers Scope and authorization, Workflow and Reporting
  • Calls make and uv

What it does

Test Coverage Improver is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Measure Python SDK coverage or address measured coverage gaps. Use for coverage audits and metric regressions, not routine test additions.

Its SKILL.md is about 690 tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Testing & QA, covering Test coverage. It works with Python and OpenAI. The repository describes itself as: A lightweight, powerful framework for multi-agent workflows. The licence is MIT.

When your agent uses it

  • Coverage audits and metric regressions
  • Not routine test additions

Example prompts

  • “/test-coverage-improver”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Inspect current .coverage, coverage.xml, and any recorded command/environment evidence. Reuse them only when they represent the relevant…
  2. Identify uncovered behavior within the requested scope. Prioritize public behavior and meaningful error, cancellation, and lifecycle paths…
  3. For an assessment, report evidence and proposed scenarios. For authorized implementation, choose tests with independent expected results…
  4. Implement tests, run affected checks, and complete $implementation-final-review. Keep iterative verification focused; do not repeatedly…
  5. After clean review, run make coverage for the final measurement and $code-change-verification for the required SDK gates. These have…

What it can do on your machine

Read from SKILL.md and the folder at commit 71c2da4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • make
    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Test Coverage Improver loads about 687 tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 346 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~687

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openai/openai-agents-python at commit 71c2da4, republished under its MIT licence (© openai). 346 words, ~687 tokens.

Download SKILL.mdSave it as .claude/skills/test-coverage-improver/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
test-coverage-improver
description
Measure Python SDK coverage or address measured coverage gaps. Use for coverage audits and metric regressions, not routine test additions.

Test Coverage Improver

Use current coverage evidence to identify missing caller-visible behavior tests. Select this workflow for coverage measurement, coverage-metric regressions, or finding gaps from coverage artifacts. When the user already specifies the behaviors to test, use the ordinary implementation/review workflow without coverage measurement unless measurement is also requested. Adding tests alone does not trigger this skill or its final make coverage step.

Scope and authorization

For assessment or proposal-only requests, report gaps and suggested tests without editing. When the user requests test improvements or has approved a plan, implement the scoped tests and complete review and verification without asking again. Ask only when a new contract decision, expanded scope, or additional authority is needed. Coverage work never authorizes live API calls or broader sandbox access.

Workflow

  1. Inspect current .coverage, coverage.xml, and any recorded command/environment evidence. Reuse them only when they represent the relevant source and test state. If missing or stale, run make coverage under the repository's verification sandbox and credential policy; this initial measurement precedes implementation review. Respect host-capacity guidance before broad measurement.
  2. Identify uncovered behavior within the requested scope. Prioritize public behavior and meaningful error, cancellation, and lifecycle paths over percentage-only targets. Use uv run coverage report -m or coverage.xml to locate gaps.
  3. For an assessment, report evidence and proposed scenarios. For authorized implementation, choose tests with independent expected results at the highest controllable caller boundary. Do not add tests that merely reproduce helper logic or enumerate unsupported permutations.
  4. Implement tests, run affected checks, and complete $implementation-final-review. Keep iterative verification focused; do not repeatedly run full coverage while review is incomplete.
  5. After clean review, run make coverage for the final measurement and $code-change-verification for the required SDK gates. These have different purposes; reuse an already valid final measurement rather than repeating it. Report coverage changes, verified behaviors, and material gaps that remain.

Reporting

State the scope and age of coverage evidence, the behavior each new test protects, validation results, and unresolved gaps. Keep comments and code in English. Do not treat coverage percentages alone as proof of correctness.

© openai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/test-coverage-improver of openai/openai-agents-python.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 71c2da4

Compare with similar skills

Test Coverage Improver next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Test Coverage Improver compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Test Coverage Improver this skillopenai/openai-agents-python30k—~687Automated safety check: PassMIT
Find Untested Sourcesdotnet/skills5.6k1 repos~3.3kAutomated safety check: PassMIT
Codex E2E Trace Validationliaohch3/claude-tap3.3k—~3kAutomated safety check: PassMIT
Test Coverage Reviewareed1192/finance-news-aggregator149—~2.6kAutomated safety check: PassMIT
OptestPyLops/pylops539—~729Automated safety check: PassLGPL-3.0
Supercovsupercorp-ai/supercov1481 repos~415Automated safety check: PassMIT

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  • Sensitive Logging Audit

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  • Code Change Verification

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  • Examples Run Analysis

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    Analyze logs and source from a completed manual examples run.

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Works with

Categories

Questions about Test Coverage Improver

What does Test Coverage Improver do?

Measure Python SDK coverage or address measured coverage gaps. Test Coverage Improver is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Measure Python SDK coverage or address measured coverage gaps.

When should I use Test Coverage Improver?

Test Coverage Improver fits situations like: coverage audits and metric regressions; not routine test additions.

How do I install Test Coverage Improver in Claude Code?

Run `npx skills add openai/openai-agents-python --skill test-coverage-improver -a claude-code`. Or copy the skill folder (.agents/skills/test-coverage-improver in openai/openai-agents-python) into .claude/skills/test-coverage-improver in your project. Claude Code loads it when a task matches its description.

How do I install Test Coverage Improver in Codex?

Run `npx skills add openai/openai-agents-python --skill test-coverage-improver -a codex`. Or copy the skill folder (.agents/skills/test-coverage-improver in openai/openai-agents-python) into .agents/skills/test-coverage-improver in your project. Codex loads it when a task matches its description.

Can I use Test Coverage Improver in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openai/openai-agents-python --skill test-coverage-improver -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/test-coverage-improver, .gemini/skills/test-coverage-improver, .github/skills/test-coverage-improver and .opencode/skills/test-coverage-improver in your project.

What does Test Coverage Improver need to run?

Going by SKILL.md and its folder, Test Coverage Improver needs the command-line tools its instructions call (make and uv). Our summary lists: Python 3.

Does Test Coverage Improver access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Test Coverage Improver safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Test Coverage Improver use?

Test Coverage Improver is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Test Coverage Improver use?

About 687 tokens (SKILL.md is roughly 2.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Test Coverage Improver?

Skills that share tags, products or a category with Test Coverage Improver: Find Untested Sources (dotnet/skills, 5.6k stars), Codex E2E Trace Validation (liaohch3/claude-tap, 3.3k stars), Test Coverage Review (areed1192/finance-news-aggregator, 149 stars) and Optest (PyLops/pylops, 539 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Test Coverage Improver?

openai (a GitHub organization, an official publisher) maintains it in openai/openai-agents-python, which has 29,873 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 7, 2026.

Source: openai/openai-agents-python on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.