Skip to main content
configure-memory-profiling Memory profiling with pytest-memray for Python. Use when setting up memory profiling, adding CI memory regression detection, or setting memory thresholds.
Ir para a instalação Skills Marketplace Descubra e explore skills de IA criadas pela comunidade.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Mostrar detalhes do prompt Um comando direto ignora o prompt de revisão. Verifique a origem antes de executá-lo.
npx skills add https://github.com/laurigates/claude-plugins --skill configure-memory-profilingO comando permanece em uma só linha. Role horizontalmente para revisá-lo antes de copiar.
Prefere uma cópia local? Baixe os arquivos disponíveis atualmente no SkillsMP.
Baixar Zip Baixando... Mais deste repositório Install, configure and troubleshoot MCP servers. Use when adding/enabling servers, editing .mcp.json, fixing OAuth, or when a server runs stale code after an upstream fix.
FinOps snapshot — org billing, workflow stats, cache usage. Use when you want a high-level view of CI spending or workflow health before diving deeper.
GitHub Actions billing, workflow efficiency, and waste analysis at org or repo level. Use when investigating CI/CD costs, wasted runs, or optimizing triggers.
Ocupações relacionadas SOC
Baseado na classificação ocupacional SOC
created "2025-12-16T00:00:00.000Z" modified "2026-06-18T00:00:00.000Z" reviewed "2025-12-16T00:00:00.000Z" description Memory profiling with pytest-memray for Python. Use when setting up memory profiling, adding CI memory regression detection, or setting memory thresholds. allowed-tools Glob, Grep, Read, Write, Edit, Bash, AskUserQuestion, TodoWrite, WebSearch, WebFetch args [--check-only] [--fix] [--threshold <mb>] [--native] argument-hint [--check-only] [--fix] [--threshold <mb>] [--native] name configure-memory-profiling
/configure:memory-profiling
Check and configure memory profiling infrastructure for Python projects using pytest-memray.
When to Use This Skill
Use this skill when... Use another approach when... Setting up memory profiling for a Python project from scratch Project is not Python — memray/pytest-memray are Python-only Adding pytest-memray integration for CI memory regression detection Profiling CPU performance — use cProfile or py-spy instead Configuring memory leak detection in test suites Running load/stress tests — use /configure:load-tests Setting memory thresholds and allocation benchmarks for CI Quick one-off memory check — run uv run pytest --memray directly Enabling native C extension stack tracking for deep profiling Profiling production systems live — use memray standalone or Grafana
Context
Project root: !pwd
Python project: !find . -maxdepth 1 \( -name 'pyproject.toml' -o -name 'setup.py' \)
pytest-memray installed: !find . -maxdepth 1 \( -name 'pyproject.toml' -o -name 'requirements*.txt' \) -exec grep 'pytest-memray' {} +
memray installed: !find . -maxdepth 1 \( -name 'pyproject.toml' -o -name 'requirements*.txt' \) -exec grep 'memray' {} +
Conftest fixtures: !find . -path '*/tests/*' -maxdepth 2 -name 'conftest.py' -exec grep -l 'memray' {} +
Memory test files: !find . -path '*/tests/*' -maxdepth 3 -name '*memory*' -o -name '*memray*'
Benchmark tests: !find . -path '*/tests/*' -maxdepth 3 -type d -name 'benchmarks'
CI workflows: !find . -path '*/.github/workflows/*' -maxdepth 3 -name '*memory*'
Memory reports dir: !find . -maxdepth 1 -type d -name 'memory-reports'
Parameters
Parse from $ARGUMENTS:
--check-only: Report memory profiling compliance status without modifications--fix: Apply all fixes automatically without prompting--threshold <mb>: Set default memory threshold in MB (default: 100)
--native
Tool Best For pytest-memray (recommended) Test-integrated profiling, CI/CD memory limits, leak detection memray standalone Deep analysis, flame graphs, production profiling tracemalloc Quick debugging, no dependencies, lightweight
Execution Execute this memory profiling configuration check:
Step 1: Verify this is a Python project Read the context values. If no pyproject.toml or setup.py is found, report "Not a Python project" and stop.
Step 2: Check latest tool versions Use WebSearch or WebFetch to verify current versions:
pytest-memray : Check PyPI memray : Check PyPI
Step 3: Analyze current memory profiling setup Check for complete setup:
pytest-memray installed as dev dependency
memray backend installed
pytest configuration in pyproject.toml (markers, addopts)
Memory limit tests using @pytest.mark.limit_memory
Leak detection enabled (--memray-leak-detection)
Native tracking configured (if --native flag)
CI/CD integration configured
Reports directory exists
Step 4: Generate compliance report Print a compliance report covering:
Installation status (pytest-memray, memray, pytest versions)
Configuration (pytest integration, markers, leak detection, native tracking)
Test coverage (memory limit tests, allocation benchmarks)
CI/CD integration (workflow, threshold, artifact upload, trend tracking)
End with overall issue count and recommendations.
If --check-only is set, stop here.
Step 5: Install and configure pytest-memray (if --fix or user confirms)
Install pytest-memray: uv add --group dev pytest-memray
Install native support if --native: uv add --group dev pytest-memray[native]
Update pyproject.toml with pytest configuration (markers, filterwarnings)
Create memory-reports/ directory
Use configuration templates from REFERENCE.md
Step 6: Create memory profiling test files
Add memory fixtures to tests/conftest.py (reports dir setup, threshold fixture, data generator)
Create tests/test_memory_example.py with example memory limit tests
Create tests/benchmarks/test_memory_benchmarks.py for trend tracking
Use test templates from REFERENCE.md
Step 7: Add package scripts Add memory profiling commands to Makefile or pyproject.toml:
test-memory: uv run pytest --memraytest-memory-report: Run with bin output + generate flame graphtest-memory-leaks: uv run pytest --memray --memray-leak-detectiontest-memory-native: uv run pytest --memray --native
Step 8: Configure CI/CD integration Create .github/workflows/memory-profiling.yml with:
Memory profiling on PRs (detect regressions)
Scheduled weekly benchmarks for trend tracking
Flame graph generation
PR comment with results
Use workflow template from REFERENCE.md
Step 9: Update standards tracking Update .project-standards.yaml:
components:
memory_profiling: "2025.1"
memory_profiling_tool: "pytest-memray"
memory_profiling_threshold_mb: 100
memory_profiling_leak_detection: true
memory_profiling_ci: true
memory_profiling_native: false
Step 10: Print final compliance report Print a summary of packages installed, configuration applied, test files created, commands available, CI/CD configured, and next steps for the user.
For detailed test templates, CI workflows, and standalone memray commands, see REFERENCE.md .
Agentic Optimizations Context Command Quick compliance check /configure:memory-profiling --check-onlyAuto-fix all issues /configure:memory-profiling --fixRun memory tests uv run pytest --memrayDetect memory leaks uv run pytest --memray --memray-leak-detectionRun with native tracking uv run pytest --memray --nativeGenerate flamegraph uv run memray flamegraph output.bin -o flamegraph.html
Flags Flag Description --check-onlyReport status without offering fixes --fixApply all fixes automatically without prompting --threshold <mb>Set default memory threshold in MB (default: 100) --nativeEnable native stack tracking for C extensions
Examples
/configure:memory-profiling
/configure:memory-profiling --check-only
/configure:memory-profiling --fix --threshold 200
/configure:memory-profiling --fix --native
Error Handling
Not a Python project : Skip with message, suggest manual setuppytest not installed : Offer to install pytest firstmemray not supported : Note platform limitations (Linux/macOS only)Native tracking unavailable : Warn about missing debug symbolsCI workflow exists : Offer to update or skip
See Also 