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.
الانتقال إلى التثبيت سوق المهارات اكتشف واستكشف مهارات الذكاء الاصطناعي التي بناها المجتمع.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
عرض تفاصيل Prompt يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/laurigates/claude-plugins --skill configure-memory-profilingيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
تحميل Zip جاري التحميل... المزيد من هذا المستودع 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.
المهن ذات الصلة SOC
استنادا إلى تصنيف SOC المهني
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 