Xpersona

Crawler Summary

py-memory-optimizer answer-first brief

Automatically analyzes Python code and suggests memory usage optimizations for improved performance --- name: py-memory-optimizer description: Automatically analyzes Python code and suggests memory usage optimizations for improved performance --- py-memory-optimizer Skill Overview This skill provides static analysis of Python code to identify memory-intensive patterns, potential memory leaks, and optimization opportunities. It generates actionable suggestions with estimated memory savings and best practice recommen Capability contract not published. No trust telemetry is available yet. Last updated 3/1/2026.

Freshness

Last checked 3/1/2026

Best For

py-memory-optimizer is best for be, for workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 89/100

py-memory-optimizer

Automatically analyzes Python code and suggests memory usage optimizations for improved performance --- name: py-memory-optimizer description: Automatically analyzes Python code and suggests memory usage optimizations for improved performance --- py-memory-optimizer Skill Overview This skill provides static analysis of Python code to identify memory-intensive patterns, potential memory leaks, and optimization opportunities. It generates actionable suggestions with estimated memory savings and best practice recommen

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Mar 1, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 3/1/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Mar 1, 2026

Vendor

Neoskillfactory

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 3/1/2026.

View Source

Setup snapshot

git clone https://github.com/NeoSkillFactory/py-memory-optimizer.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Neoskillfactory

profilemedium
Observed Mar 1, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Mar 1, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

6

Snippets

0

Languages

typescript

Parameters

Executable Examples

bash

npm install --global openclaw-skill-py-memory-optimizer

bash

# Analyze a single file
py-memory-optimizer analyze path/to/script.py

# Analyze entire directory
py-memory-optimizer analyze ./my_project --recursive

# Generate detailed report
py-memory-optimizer analyze script.py --format json --output report.json

# Show optimization suggestions with memory estimates
py-memory-optimizer analyze script.py --show-suggestions --estimate-savings

# Exclude specific patterns
py-memory-optimizer analyze . --exclude "*.test.py" "*/migrations/*"

# Version info
py-memory-optimizer --version

text

Analyze this Python code for memory optimizations: <code>

python

# Bad: Creates entire list in memory
result = [process(item) for item in huge_dataset]

text

Line 15: Medium memory issue
  Pattern: Large list comprehension
  Current memory: ~O(N) for full list
  Suggestion: Use generator expression
  Optimized: (process(item) for item in huge_dataset)
  Estimated savings: 70-90% for large datasets

python

f = open('data.txt', 'r')
data = f.read()
# Missing f.close()

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Automatically analyzes Python code and suggests memory usage optimizations for improved performance --- name: py-memory-optimizer description: Automatically analyzes Python code and suggests memory usage optimizations for improved performance --- py-memory-optimizer Skill Overview This skill provides static analysis of Python code to identify memory-intensive patterns, potential memory leaks, and optimization opportunities. It generates actionable suggestions with estimated memory savings and best practice recommen

Full README

name: py-memory-optimizer description: Automatically analyzes Python code and suggests memory usage optimizations for improved performance

py-memory-optimizer Skill

Overview

This skill provides static analysis of Python code to identify memory-intensive patterns, potential memory leaks, and optimization opportunities. It generates actionable suggestions with estimated memory savings and best practice recommendations.

Core Capabilities

  • Static Code Analysis: Parses Python source files using the AST module to analyze code structure without execution
  • Pattern Detection: Identifies common memory-intensive patterns (large list comprehensions, unnecessary object creation, improper generator usage)
  • Leak Detection: Finds potential memory leaks from circular references, unclosed resources, and global variable accumulation
  • Optimization Suggestions: Provides specific, actionable recommendations with estimated memory impact
  • Framework Support: Handles Python 3.8+ and common frameworks (Django, Flask, FastAPI patterns)
  • CLI Interface: Command-line tool for integration into development workflows
  • Report Generation: Creates detailed analysis reports in multiple formats (JSON, markdown, plain text)

Dependencies

System Requirements

  • Python 3.8+
  • pip package manager

Python Packages (installed automatically via package.json)

  • astroid (optional, for enhanced AST analysis)
  • tabulate (for formatted output)
  • rich (for colored terminal output)
  • click (for CLI interface)
  • pydantic (for data validation)
  • python-slugify (for report naming)

Installation

npm install --global openclaw-skill-py-memory-optimizer

The skill will automatically install Python dependencies on first run.

Usage

CLI Interface

# Analyze a single file
py-memory-optimizer analyze path/to/script.py

# Analyze entire directory
py-memory-optimizer analyze ./my_project --recursive

# Generate detailed report
py-memory-optimizer analyze script.py --format json --output report.json

# Show optimization suggestions with memory estimates
py-memory-optimizer analyze script.py --show-suggestions --estimate-savings

# Exclude specific patterns
py-memory-optimizer analyze . --exclude "*.test.py" "*/migrations/*"

# Version info
py-memory-optimizer --version

Integration with OpenClaw Agents

The skill can be called directly by OpenClaw agents:

Analyze this Python code for memory optimizations: <code>

The agent will invoke the analyzer and return structured suggestions.

Output Format

The analyzer produces:

  1. Memory Issue Summary: Count of issues by severity (critical, high, medium, low)
  2. Detailed Findings: For each issue:
    • File and line number
    • Issue type and description
    • Memory impact estimate
    • Specific optimization suggestion with code example
  3. Overall Statistics: Total memory potentially saved, number of objects analyzed
  4. Best Practices Checklist: Compliance with Python memory optimization guidelines

Examples

Example 1: Large List Comprehension

Input:

# Bad: Creates entire list in memory
result = [process(item) for item in huge_dataset]

Output:

Line 15: Medium memory issue
  Pattern: Large list comprehension
  Current memory: ~O(N) for full list
  Suggestion: Use generator expression
  Optimized: (process(item) for item in huge_dataset)
  Estimated savings: 70-90% for large datasets

Example 2: Unclosed File Handles

Input:

f = open('data.txt', 'r')
data = f.read()
# Missing f.close()

Output:

Line 8: High memory/resource issue
  Pattern: Unclosed file handle
  Risk: File descriptor leak, memory not freed
  Suggestion: Use context manager
  Optimized: with open('data.txt', 'r') as f:
                 data = f.read()
  Estimated savings: Prevents descriptor accumulation

Out of Scope

  • Runtime profiling (use memory_profiler for that)
  • C extension analysis
  • Database or external service optimization
  • Automatic code modification (this skill only suggests)
  • Mixed-language project optimization

Files

This skill provides:

  • SKILL.md: This documentation
  • package.json: NPM package definition
  • README.md: Quick start guide
  • scripts/main.py: CLI entry point
  • scripts/analyzer.py: Core analysis engine
  • scripts/optimizer.py: Suggestion generator
  • scripts/utils.py: AST utilities and helpers
  • references/memory_patterns.md: Pattern reference
  • references/best_practices.md: Best practices guide
  • assets/sample_code/: Example files for testing

License

MIT

Support

For issues and feature requests, visit: https://github.com/openclaw/skills/issues

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/snapshot"
curl -s "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/contract"
curl -s "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

Rank

70

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 5d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Agent hubMore from this sourceOpenClaw agents
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-16T23:28:04.725Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "be",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "for",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:be|supported|profile capability:for|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Neoskillfactory",
    "href": "https://github.com/NeoSkillFactory/py-memory-optimizer",
    "sourceUrl": "https://github.com/NeoSkillFactory/py-memory-optimizer",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-03-01T06:01:49.937Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-03-01T06:01:49.937Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/neoskillfactory-py-memory-optimizer/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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