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ai-chat

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Guidance for improving the Windmill AI chat (copilot), especially global mode — tools, prompts, and context-window discipline. Use when editing chat tools, system prompts, or tool-result shapes under frontend/src/lib/components/copilot/chat, or when changing how the chat manages its context window.

windmill-labs/windmill스타 18,277(GitHub 18,277 + cccskills 0)

실행 점수 미검증

설치 방법 보기

이럴 때 유용합니다

  • Edit Windmill chat tools or system prompts
  • Reduce chat context window usage
  • Benchmark a chat behavior change

포함된 파일(1)

  • SKILL.md2.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Always benchmark before and after

No context or behavior change ships without an ai_evals A/B on the affected mode. Add or adjust cases for exactly what you changed — see the ai-evals skill for authoring and the full run reference.

Run the affected mode before your change and after, same model(s), same cases.

Where guidance goes

What the model should know about Windmill itself (flow shapes, groups, data tables, app access, secrets…) belongs in system_prompts/base/ or system_prompts/languages/, which also feed the CLI's skills — not in a */core.ts prompt builder, where only this chat would see it. The builders add tool plumbing and runtime values only. Scope a sentence to one consumer with <!-- chat-only --> / <!-- cli-only -->, keep chat tool names out of the shared files, and rerun python system_prompts/generate.py. system_prompts/README.md has the mechanics.

Measure the window first, and cumulative second

Optimize finalContextTokens (window occupancy — what drives overflow and compaction), then cumulative prompt tokens.

Context discipline

The dominant fixed cost is per-iteration overhead: the system prompt plus every tool schema is re-sent on every loop iteration. So:

  • Every tool and every parameter is a permanent tax. Justify each one and measure it; an extra "locate" round-trip can cost more than the reads it saves. Strip dead params rather than leaving them in the schema.
  • Tool results return the minimum. Never echo content the model already has. The canonical mistake: a write tool that returns the whole edited artifact right after the model authored it — return { success, message } instead. When you touch a shared write helper (e.g. finishAppDraftWrite in global/core.ts), re-check this invariant for all the write tools routing through it — the echo has regressed before via a shared refactor.

Prompts and tool descriptions are part of the surface

The system prompt and tool descriptions steer behavior as much as the tools themselves, and are benchmarkable the same way. A description that advertises truncation makes the model self-limit; the path-conventions block changes where drafts land. Treat prompt/description edits as real changes and A/B them — a pure-prompt change is a legitimate, measurable improvement.

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이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Add a Kafka, GCP, or Azure style trigger to Windmill
  • Find all product areas affected by a new trigger kind
  • Review implementation and validation steps before a trigger PR
windmill-labs/windmill1.8만2026년 10월 26일 업데이트

ai-evals

무료

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Adding a benchmark case for an AI mode
  • Comparing AI chat behavior before and after a change
  • Checking copilot changes with realistic requests
windmill-labs/windmill1.8만2026년 10월 26일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Inspecting a failed job and its logs
  • Reviewing recent runs and retrieving job results
  • Managing scripts and flows from the command line
windmill-labs/windmill1.8만2026년 10월 26일 업데이트

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Designing a module interface
  • Finding places to simplify a module
  • Choosing where behavior should vary
windmill-labs/windmill1.8만2026년 10월 26일 업데이트

commit

무료

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Committing a new feature
  • Recording a bug fix
  • Committing documentation changes
windmill-labs/windmill1.8만2026년 10월 26일 업데이트

Manage schema changes to Windmill data tables through versioned SQL migrations, including applying and rolling them back. The guide also explains role-based access, migration safety, and how changes affect workspaces, apps, and scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Changing a data table schema safely
  • Writing or applying SQL migrations
  • Rolling back a recent migration
windmill-labs/windmill1.8만2026년 10월 26일 업데이트

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