Stress-test a plan, decision, or idea through structured rounds of questions. The skill maps dependent choices as a decision tree, asks all currently answerable questions together, and waits for the user's answers before exploring decisions that depend on them.
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Grill the user relentlessly about a plan, decision, or idea. Use when the user wants to stress-test their thinking, or uses any 'grill' trigger phrases.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Interview the user relentlessly until you reach a shared understanding. Map this as a design tree: every decision branches into the decisions that hang off it.
Work the tree in rounds. The frontier is every decision whose prerequisites are already settled — the questions you can ask now without guessing at answers you haven't heard yet. Ask the whole frontier in one round: number each question and give your recommended answer. Then wait for the user's answers before the next round.
Each question should be formatted like so:
❓ **Q1** - **<question title>**: <question body, might be multiple paragraphs, including multiple choices>
➡️ <your recommended answer>
Each round the user answers reshapes the tree — settled decisions push the frontier outward and unblock questions that depended on them. Recompute the frontier and ask the next round. A question whose answer depends on another question still open in this round belongs to a later round, not this one.
Finding facts is your job, never the user's. When a frontier question needs a fact from the environment (filesystem, tools, etc.), dispatch a sub-agent to find it — don't ask the user for anything you could look up yourself. Don't block on it: a running exploration is an unsettled prerequisite, so only the questions downstream of it wait for the sub-agent to report — ask the rest of the frontier now. The decisions are the user's — put each to them and wait.
The session is done when the frontier is empty: every branch of the design tree visited, nothing left silently assumed. Do not act on it until the user confirms you have reached a shared understanding.
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Plan the code changes needed to add a TriggerCrud-based trigger to Windmill. The checklist covers backend, database, API, CLI, frontend, capture, and validation work so the new trigger can be managed across the product.
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Improve Windmill’s AI chat by reviewing its tools, prompts, result shapes, and context usage. The guidance helps place shared instructions where chat and CLI can use them, reduce repeated token costs, and compare behavior before and after changes with A/B evaluations.
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Create and run black-box benchmarks for Windmill AI generation modes such as flows, apps, scripts, CLI, and global requests. Each case exercises the production path, checks hard requirements, and uses an LLM judge to assess user-facing behavior before and after changes.
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Use this reference to work with Windmill resources from the command line, including scripts, flows, datatables, and workspace settings. It also covers listing jobs, inspecting details and logs, retrieving results, and rerunning or restarting executions when investigating failures.
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Use this skill to shape modules with small interfaces and substantial behavior behind them. It helps you decide where a seam belongs, reduce complexity exposed to callers, and make code easier to test and maintain.
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Use this skill to review pending changes, select only the relevant files for staging, and create a focused Git commit. It formats the single-line message with a Conventional Commits type such as feat, fix, or docs, and checks the resulting status.
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