Smithery Logo
MCPsSkillsDocsPricing
Login
Smithery Logo

Accelerating the Agent Economy

Resources

DocumentationPrivacy PolicySystem Status

Company

PricingAboutBlog

Connect

© 2026 Smithery. All rights reserved.

    openai

    test-coverage-improver

    openai/test-coverage-improver
    Coding
    18,848
    19 installs

    About

    SKILL.md

    Install

    Install via Skills CLI

    or add to your agent
    • Claude Code
      Claude Code
    • Codex
      Codex
    • OpenClaw
      OpenClaw
    • Cursor
      Cursor
    • Amp
      Amp
    • GitHub Copilot
      GitHub Copilot
    • Gemini CLI
      Gemini CLI
    • Kilo Code
      Kilo Code
    • Junie
      Junie
    • Replit
      Replit
    • Windsurf
      Windsurf
    • Cline
      Cline
    • Continue
      Continue
    • OpenCode
      OpenCode
    • OpenHands
      OpenHands
    • Roo Code
      Roo Code
    • Augment
      Augment
    • Goose
      Goose
    • Trae
      Trae
    • Zencoder
      Zencoder
    • Antigravity
      Antigravity
    ├─
    ├─
    └─

    About

    Improve test coverage in the OpenAI Agents Python repository: run make coverage, inspect coverage artifacts, identify low-coverage files, propose high-impact tests, and confirm with the user before...

    SKILL.md

    Test Coverage Improver

    Overview

    Use this skill whenever coverage needs assessment or improvement (coverage regressions, failing thresholds, or user requests for stronger tests). It runs the coverage suite, analyzes results, highlights the biggest gaps, and prepares test additions while confirming with the user before changing code.

    Quick Start

    1. From the repo root run make coverage to regenerate .coverage data and coverage.xml.
    2. Collect artifacts: .coverage and coverage.xml, plus the console output from coverage report -m for drill-downs.
    3. Summarize coverage: total percentages, lowest files, and uncovered lines/paths.
    4. Draft test ideas per file: scenario, behavior under test, expected outcome, and likely coverage gain.
    5. Ask the user for approval to implement the proposed tests; pause until they agree.
    6. After approval, write the tests in tests/, rerun make coverage, and then run $code-change-verification before marking work complete.

    Workflow Details

    • Run coverage: Execute make coverage at repo root. Avoid watch flags and keep prior coverage artifacts only if comparing trends.
    • Parse summaries efficiently:
      • Prefer the console output from coverage report -m for file-level totals; fallback to coverage.xml for tooling or spreadsheets.
      • Use uv run coverage html to generate htmlcov/index.html if you need an interactive drill-down.
    • Prioritize targets:
      • Public APIs or shared utilities in src/agents/ before examples or docs.
      • Files with low statement coverage or newly added code at 0%.
      • Recent bug fixes or risky code paths (error handling, retries, timeouts, concurrency).
    • Design impactful tests:
      • Hit uncovered paths: error cases, boundary inputs, optional flags, and cancellation/timeouts.
      • Cover combinational logic rather than trivial happy paths.
      • Place tests under tests/ and avoid flaky async timing.
    • Coordinate with the user: Present a numbered, concise list of proposed test additions and expected coverage gains. Ask explicitly before editing code or fixtures.
    • After implementation: Rerun coverage, report the updated summary, and note any remaining low-coverage areas.

    Notes

    • Keep any added comments or code in English.
    • Do not create scripts/, references/, or assets/ unless needed later.
    • If coverage artifacts are missing or stale, rerun pnpm test:coverage instead of guessing.
    Recommended Servers
    Postman
    Postman
    Nimble MCP Server
    Nimble MCP Server
    Vercel Grep
    Vercel Grep
    Repository
    openai/openai-agents-python
    Files