Data Generator
v2.2.0训练数据生成技能。根据传入的工具名和用户指令列表，生成多轮对话格式的 JSONL 训练数据。触发场景：(1) 传入工具名和用户指令列表，生成完整训练数据；(2) 批量生成指定工具的标注数据；(3) 给定指令列表，输出 JSONL 对话样本。
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Benign
high confidencePurpose & Capability
Name/description (生成训练数据) match the included artifacts: prompt templates, per-tool rule files under references/tools/, and two generator scripts. All required files and behavior (creating JSONL from user instructions) are coherent with the stated purpose.
Instruction Scope
SKILL.md and scripts constrain behavior to assembling prompts and producing JSONL records. The generator reads local reference files and, if invoked with --file, will read a user-supplied file of instructions — this is expected for the task but means you should not point it at sensitive system files because their contents would be embedded into generated data.
Install Mechanism
No install spec; instruction-only skill with bundled Python scripts. No downloads, no external package installs, and no code executed from remote URLs.
Credentials
The skill requests no environment variables, credentials, or config paths. The scripts operate on provided inputs and bundled reference files only, which is proportionate to the stated functionality.
Persistence & Privilege
Skill is not always-enabled and uses normal model invocation semantics. It does write output files (the generated .jsonl) as expected, but does not modify other skills or request elevated/system-wide privileges.
Assessment
This skill appears to do only what it claims: generate JSONL training samples from tool-specific templates and user instruction lists. Before running: (1) inspect a sample output to ensure it doesn't accidentally encode any sensitive content you might include in the input file; (2) do not pass paths to sensitive system files to the --file option (the script will embed file contents into the generated data); (3) if you plan to include real user data or production logs, sanitize or anonymize them first. Otherwise it is safe to install from a provenance perspective (no network calls, no secrets requested).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.