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Configure Memory Profiling

  • 56 installs
  • 49 repo stars
  • Updated August 4, 2026
  • laurigates/claude-plugins

Helps with ai & agent building tasks.

About

configure-memory-profiling is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • configure-memory-profiling
  • AI & Agent Building
  • AI-coding skill

Configure Memory Profiling by the numbers

  • 56 all-time installs (skills.sh)
  • Ranked #6,668 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
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Installs56
repo stars49
Last updatedAugust 4, 2026
Repositorylaurigates/claude-plugins

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

/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 scratchProject is not Python — memray/pytest-memray are Python-only
Adding pytest-memray integration for CI memory regression detectionProfiling CPU performance — use cProfile or py-spy instead
Configuring memory leak detection in test suitesRunning load/stress tests — use /configure:load-tests
Setting memory thresholds and allocation benchmarks for CIQuick one-off memory check — run uv run pytest --memray directly
Enabling native C extension stack tracking for deep profilingProfiling 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: Enable native stack tracking for C extensions

Supported tools:

ToolBest For
pytest-memray (recommended)Test-integrated profiling, CI/CD memory limits, leak detection
memray standaloneDeep analysis, flame graphs, production profiling
tracemallocQuick 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:

1. pytest-memray: Check PyPI 2. 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)

1. Install pytest-memray: uv add --group dev pytest-memray 2. Install native support if --native: uv add --group dev pytest-memray[native] 3. Update pyproject.toml with pytest configuration (markers, filterwarnings) 4. Create memory-reports/ directory 5. Use configuration templates from REFERENCE.md

Step 6: Create memory profiling test files

1. Add memory fixtures to tests/conftest.py (reports dir setup, threshold fixture, data generator) 2. Create tests/test_memory_example.py with example memory limit tests 3. Create tests/benchmarks/test_memory_benchmarks.py for trend tracking 4. 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 --memray
  • test-memory-report: Run with bin output + generate flame graph
  • test-memory-leaks: uv run pytest --memray --memray-leak-detection
  • test-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

ContextCommand
Quick compliance check/configure:memory-profiling --check-only
Auto-fix all issues/configure:memory-profiling --fix
Run memory testsuv run pytest --memray
Detect memory leaksuv run pytest --memray --memray-leak-detection
Run with native trackinguv run pytest --memray --native
Generate flamegraphuv run memray flamegraph output.bin -o flamegraph.html

Flags

FlagDescription
--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

# Check compliance and offer fixes
/configure:memory-profiling

# Check only, no modifications
/configure:memory-profiling --check-only

# Auto-fix with custom threshold
/configure:memory-profiling --fix --threshold 200

# Enable native tracking for C extensions
/configure:memory-profiling --fix --native

Error Handling

  • Not a Python project: Skip with message, suggest manual setup
  • pytest not installed: Offer to install pytest first
  • memray not supported: Note platform limitations (Linux/macOS only)
  • Native tracking unavailable: Warn about missing debug symbols
  • CI workflow exists: Offer to update or skip

See Also

  • /configure:tests - Configure testing frameworks
  • /configure:coverage - Code coverage configuration
  • /configure:load-tests - Load and performance testing
  • /configure:all - Run all compliance checks
  • pytest-memray docs: https://pytest-memray.readthedocs.io
  • memray docs: https://bloomberg.github.io/memray

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