Ai Code Quality Economics
v1.0.0Analyze and improve AI-generated code quality by leveraging economic incentives such as token efficiency, maintainability, and competitive market forces.
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
The name and description match the SKILL.md content: the skill explains economic incentives and gives example code and metrics for assessing AI-generated code quality. One minor mismatch: the SKILL.md lists 'Git CLI' as a dependency for repository analysis, but the skill metadata lists no required binaries—this is a small documentation inconsistency rather than a capability mismatch.
Instruction Scope
The instructions and examples stay on-topic. Examples include calls to llm.generate and subprocess.run(['git', ...]) for repo analysis; those imply the agent (or user code) may run git against local repositories and call an LLM API. The SKILL.md does not instruct the agent to read unrelated system files or to exfiltrate data, but runtime use of the examples will require providing repository paths and (practically) LLM credentials.
Install Mechanism
No install spec and no code files (beyond SKILL.md and a simple package.json). Instruction-only skills are the lowest install risk; nothing is downloaded or executed by default.
Credentials
The skill declares no required environment variables or credentials, which is reasonable for an instructional document. However, the examples assume an LLM interface (llm.generate) and Git CLI usage; in practice an implementation would need LLM API credentials and local repository access. The skill does not request those explicitly, so users should be aware credentials would be needed to run the examples.
Persistence & Privilege
The skill is not marked always:true and is user-invocable; autonomous model invocation is allowed (the platform default). There is no indication the skill modifies other skills or system-wide settings.
Assessment
This is an instruction-only guidance skill about code-quality economics and appears coherent. Before installing or running it, consider: (1) examples call git via subprocess and expect a repository path—only run those on repos you trust and on systems where you permit git operations; (2) examples call an LLM (llm.generate) but the skill does not declare how or where to store API keys—do not supply secrets unless you trust the runtime; (3) source and homepage are unknown—if you need higher assurance, ask the publisher for provenance or prefer a skill with a verifiable repo; (4) because the skill is instruction-only, it won't install software itself, but code you or the agent runs based on these instructions can execute shell commands, so apply the usual caution when allowing autonomous execution.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.