Decide Fit First

• Core job: 用于解释、评审、生成、修复 DQL。Works with Claude Code, Cursor, Cline and 23 more. runs entirely locally. 用于解释、评审、生成、修复 DQL。 若任务只要求解释语义、指出风险、做…
• Best fit: Use it when the task has reusable inputs, steps, and validation criteria rather than a one-off answer.
• Avoid forcing it: If the source lacks commands, platform support, or external-service evidence, keep those fields unknown instead of guessing.

Design Intent

• Structure: The skill is organized around “触发规则”, “权威依据”, “工具规则”, “生成 / 修复工作流”, showing how the author expects the agent to judge fit, collect context, and produce verifiable output.
• Trigger evidence: Prioritize the author’s wording around when to use it, what context to collect, and what output shape to produce.
• Evidence boundary: Author text states facts, repository files prove commands and paths, and Fluxly only adds fit, limits, and usage judgment.

How To Use It

• Inputs: Provide target material, scope, expected result, forbidden changes, and validation method.
• Invocation: Name dql directly; if the source includes slash commands, start with the command and then add task context.
• Validation: Start small and check whether the result follows “触发规则 / 权威依据 / 工具规则” before expanding.

Boundaries And Review

• Dependencies: It usually needs no extra API key, so start with a small validation task.
• Permissions: Declared permissions include read / shell-exec; ask the agent to state file, command, and rollback boundaries before acting.
• Quality bar: A useful result names the deliverable, evidence, and next action. Generic prose means the task needs tighter context.