
from opencode-skills-collection23
Profile, analyze, and optimize Python applications for CPU and memory efficiency using profiling tools and performance best practices.
This skill guides agents through profiling and improving Python application performance. It covers CPU profiling, memory analysis, I/O and database optimization, and practical implementation patterns so you can identify bottlenecks and validate improvements. The skill points to a resources/implementation-playbook.md for concrete examples and playbooks.
Use this skill when you need to diagnose slow Python code, reduce latency, lower memory usage, optimize heavy data-processing pipelines, or harden production services against performance regressions. Appropriate for profiling local code, CI runs, or production traces (with caution).
Best used by developer-focused agents that can read code and suggest changes (Copilot-style/code assistants, Claude Code, Codex). It is implementation-oriented and assumes access to the codebase or profiling output.
Purely instructional skill with no bundled scripts or executable content. The SKILL.md is extremely thin — generic boilerplate instructions like 'clarify goals, apply best practices' with no concrete profiling commands, tool recommendations, or code examples. All substantive content is deferred to an unbundled resources/implementation-playbook.md. Effectively unusable as-is.
This skill appears to be a stub or template — it has the right topic but delivers almost no actionable content. The frontmatter is valid and the structure follows the spec, but the body is so generic it provides no real value to an agent trying to optimize Python performance.
API Security Testing Workflow
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Azure AI Projects SDK (TypeScript)
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Memory Forensics Playbook
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Reverse Engineer
Guided methodology and best practices for binary reverse engineering, covering static and dynamic analysis workflows and common tooling.