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Performance Tuning
by
mikeclaw007
· GitHub ↗
· v1.0.0
· MIT-0
251
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0
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0
Active Installs
1
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Install in OpenClaw
/install performance-tuning
Description
Deep performance tuning workflow—goals and measurement, profiling, hotspots, caching and concurrency trade-offs, system-specific tuning (DB, GC, network), an...
Usage Guidance
This skill is a benign, high-level checklist for performance tuning and does not request credentials or install anything. Consider these practical points before enabling it: (1) it provides advice only — it will not run profilers or change systems unless paired with other skills or human-invoked actions; (2) because the source is unknown, avoid pasting sensitive production traces or secrets into prompts when seeking guidance; (3) if you later augment the skill with automation (scripts, installs, or profiling commands), re-evaluate for permissions and network/download risks; (4) test recommendations in a safe environment (staging/canary) before applying to production.
Capability Analysis
Type: OpenClaw Skill
Name: performance-tuning
Version: 1.0.0
The skill bundle contains a high-level procedural workflow for performance tuning and optimization. It consists entirely of documentation and instructions for an AI agent to follow (SKILL.md) and standard metadata (_meta.json), with no executable code, suspicious network calls, or attempts to access sensitive system resources.
Capability Assessment
Purpose & Capability
Name and description match the provided content. The skill is an advisory workflow and requests no credentials, binaries, or config access — which is appropriate for a guidance-only performance-tuning skill.
Instruction Scope
SKILL.md contains step-by-step, measurement-driven guidance and tool suggestions but does not instruct the agent to read local files, access environment variables, call external endpoints, download or execute code, or collect/transmit sensitive data. It stays within the stated advisory scope.
Install Mechanism
No install spec and no code files — instruction-only. This minimizes disk writes and runtime installation risk.
Credentials
No environment variables, credentials, or config paths are required or referenced. The lack of requested secrets is proportionate to an advisory workflow.
Persistence & Privilege
always is false and the skill does not request persistent system presence or modifications to other skills. Autonomous invocation is allowed by default but the skill's content is advisory and does not perform privileged operations.
How to Use
- Make sure OpenClaw is installed (local or Docker)
- Run the install command in chat:
/install performance-tuning - After installation, invoke the skill by name or use
/performance-tuning - Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
Initial release of the performance-tuning skill.
- Introduces a six-stage, measurement-driven workflow for deep performance tuning.
- Covers goal setting, baseline measurement, profiling, implementation, verification, and regression prevention.
- Includes supporting checklists, profiling tool examples, and tips for various system types (e.g., DB, GC, network).
- Designed for addressing latency, throughput, resource saturation, or efficiency/cost concerns.
Metadata
Frequently Asked Questions
What is Performance Tuning?
Deep performance tuning workflow—goals and measurement, profiling, hotspots, caching and concurrency trade-offs, system-specific tuning (DB, GC, network), an... It is an AI Agent Skill for Claude Code / OpenClaw, with 251 downloads so far.
How do I install Performance Tuning?
Run "/install performance-tuning" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.
Is Performance Tuning free?
Yes, Performance Tuning is completely free, licensed under MIT-0. You can download, install and use it at no cost.
Which platforms does Performance Tuning support?
Performance Tuning is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).
Who created Performance Tuning?
It is built and maintained by mikeclaw007 (@mikeclaw007); the current version is v1.0.0.
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