본문으로 건너뛰기
cccskills
무료GitHub에 공개

agentic-workflow-guide

Design, review, and debug agent workflows, and decide when a request should use a prompt, instruction, skill, agent, or hook before escalating to multi-agent design. Use for .agent.md / .instructions.md / .prompt.md / AGENTS.md work, workflow architecture, orchestration planning, scheduled automation model allocation, or when agent workflows may be overkill. Triggers on 'agent workflow', 'create agent', 'automation models', 'ワークフロー設計', 'orchestrator'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(29)

  • SKILL.md7.9 KB
  • LICENSE.txt5.2 KB
  • references/agent-evaluation.md13.5 KB
  • references/agent-guide.md17.5 KB
  • references/agent-template.md20.2 KB
  • references/builtin-customization-patterns.md3.8 KB
  • references/context-engineering.md14.9 KB
  • references/copilot-cloud-agent-ci.md8.8 KB
  • references/customization-decision.md5.4 KB
  • references/deep-agent-patterns.md8.6 KB
  • references/design-principles.md14.2 KB
  • references/external-resources.md7.5 KB
  • references/handoffs-guide.md12.8 KB
  • references/hooks-guide.md6.7 KB
  • references/prompt-template.md7.7 KB
  • references/review-checklist.md19.9 KB
  • references/scheduled-runtime-bindings.md8.2 KB
  • references/splitting-criteria.md11.0 KB
  • references/vscode-agent-placement.md6.5 KB
  • references/workflow-patterns/0-plan-first.md1.5 KB
  • references/workflow-patterns/1-prompt-chaining.md1.0 KB
  • references/workflow-patterns/2-routing.md1.5 KB
  • references/workflow-patterns/3-parallelization.md3.2 KB
  • references/workflow-patterns/4-orchestrator-workers.md5.0 KB
  • references/workflow-patterns/5-evaluator-optimizer.md1.8 KB
  • references/workflow-patterns/6-connected-agents.md3.3 KB
  • references/workflow-patterns/combining-patterns.md2.0 KB
  • references/workflow-patterns/ir-architecture.md2.7 KB
  • references/workflow-patterns/overview.md2.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Agentic Workflow Guide

Design, review, and improve agent workflows based on proven principles.

この SKILL の基本姿勢は、agent を増やすことではなく、必要最小の primitive で解くこと。 context が膨らんだときも、まずは split / compact / reference 化を考え、いきなり multi-agent にしない。

Primitive First

Do not start with multi-agent by default.

  • Single focused slash task -> Prompt (Local harness only; prompt files are deprecated for Agent Host sessions, so prefer a Skill for new slash workflows)
  • Always-on or file-scoped guidance -> Instruction
  • Reusable workflow with bundled assets -> Skill
  • Persona, tool restrictions, delegation, or handoffs -> Agent
  • Deterministic enforcement -> Hook (Preview; events and config differ by harness)

If the ask does not require an Agent, stop and use the simpler primitive.

Selection details: references/customization-decision.md

When to Use

ActionTriggers
CreateNew .agent.md, .instructions.md, .prompt.md, AGENTS.md, or workflow architecture
ReviewOrchestrator not delegating, design principle check, context overflow
UpdateAdding Handoffs, improving delegation, tool configuration
DebugAgent not found, subagent not working, picker visibility, access control
DecideDetermining whether multi-agent is justified or a simpler primitive is enough

Core Principles

  • Simplicity First: より単純な primitive で解けるなら agent 化しない
  • SSOT / SRP: 情報源と責務の分割を守る
  • Fail Fast: エラーは早く止める
  • Feedback Loop: 各段で検証できるようにする
  • Context Discipline: context が膨らんだら compact / split / retrieve を検討する

Principle details: references/design-principles.md

Pattern Selection

Name the pattern explicitly when complexity rises, using the selector in references/workflow-patterns/overview.md. Every loop needs explicit stop conditions.

Design Workflow

  1. Extract repeated behavior, tool preferences, and workflow shape from the conversation.
  2. Choose primitive + scope (workspace / profile); ask only about ambiguities that change behavior.
  3. Escalate only when split / compact / reference-ization cannot solve it, then pick a pattern.
  4. Implement the smallest version and tighten the weakest parts.

Rule Placement

  • 汎用的な workflow 設計原則は、この SKILL と references/ を SSOT にする。
  • repo local の .instructions.md には workspace 固有の差分だけを残す。差分が無い generic instruction は merge back して削除候補にする。
  • IR は原則 in-memory で扱う。validator、script、deterministic handoff が必要な場合だけ中間 file を materialize し、不要になったら片付ける。
  • scheduler / service / config など決定論的な state mutation は、AI/UI loop ではなく direct script / API で現状確認 -> 最小変更 -> live read-back まで行う。LLM は scope と整合対象の判断に限定する。
  • For model allocation reviews, use scheduled runtime bindings.

Escalation Rules

Prefer the lowest level (L0 prompt → L1 + instructions → L2 single agent → L3 multi-agent) that solves the problem cleanly. Quick signals to split:

  • Prompt > 50 lines
  • Steps > 5
  • "missed" / "overlooked" errorsが続く
  • Multiple responsibilities in one agent
  • Context > 70%

Threshold details: references/splitting-criteria.md

Review Gates

  • Primitive choice is simpler than agent if possible
  • Placement is appropriate and always-loaded entry files stay thin (entry boundary smells: review-checklist.md)
  • Delete unnecessary or self-evident text, including rationale sentences that do not change a decision, before shortening, only if normal/exception/stop decisions stay unchanged; then consider merge / split / move before additions. Preserve evidence, nontrivial procedures, scope, required examples, metadata, approvals, unverified requirements and failure/recovery conditions; age alone does not obsolete history, and unsaved notes stay protected.
  • Single responsibility per agent is preserved
  • Errors can be detected and stopped early
  • Verify actual producer-to-consumer dataflow, not only fixtures injected into a validator; cover empty results and meaningful state transitions. Repeating unchanged input proves idempotency, not the full lifecycle.
  • Deterministic parts are offloaded to scripts / IR / hooks, and state changes are confirmed by reading authoritative live state back (not LLM/UI loops)

Full checklist: references/review-checklist.md

Reference Map

Core references: primitive decision, design principles, workflow patterns, splitting criteria, review checklist, and context management.

For scheduled workflows, use scheduled runtime bindings. For delegated evidence and append-only current-state extraction, see orchestrator-workers and IR architecture.

When an orchestrator promises delegation but works directly, make the delegation requirement explicit and verifiable (agent-guide.md). Tool mapping and agent scaffold: agent-template.md.

Done Criteria

  • Primitive and scope selected intentionally
  • Workflow pattern selected and confirmed with user
  • New or updated assets have clear Role/Workflow/Done Criteria where applicable
  • Review Gates passed
  • Recommendations are classified as delete / merge / split / move / add / keep where applicable
  • Always-on instruction boundaries and DRY / SSOT risks are explicitly reviewed when copilot-instructions.md or AGENTS.md are in scope
  • New agent / workflow assets are registered in the appropriate catalog or docs when needed
  • AGENTS.md is updated only when shared guardrails or entry behavior need to change
  • Long-running or ad-hoc terminals/tasks started during the workflow are closed, or remaining terminals are explicitly reported with a reason
  • Distinguish configuration saved, code tested, live read verified, action authorized, and recurring cycle completed. Verify authoritative state before claiming completion or unattended readiness; pending safeguards and unobserved cycles retain their blockers and next check conditions.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Design, run, and review fair performance, token, and cost comparisons across GitHub Copilot CLI, Codex CLI, and direct model APIs. Use for AI CLI benchmark, Copilot vs Codex, prompt-cache diagnosis, cache read/write analysis, fresh-process latency measurement, token-cost comparison, or when a benchmark must separate provider caching from local CLI caches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aktsmm/Agent-Skills262026년 10월 18일 업데이트

Analyze historical VS Code GitHub Copilot Chat sessions by model, reasoning effort, AIU, time, reliability, workflow behavior, and external quality evidence, or safely prune workspace-scoped local chat history by age. Use for session analysis, repeated-task retrospectives, model evaluation, cost/performance analysis, and old session cleanup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aktsmm/Agent-Skills262026년 10월 18일 업데이트

Create reproducible animated infographic GIFs from structured data or measured results, with audience-language labels, explicit timing semantics, representative-frame QA, and a static fallback. Use when asked for GIF生成, animated infographic, 動的インフォグラフィック, 結果が流れる図, animated benchmark, or a blog/SNS animation whose displayed metrics must remain traceable to source data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aktsmm/Agent-Skills262026년 10월 18일 업데이트

archify

무료

Create polished, validated architecture, workflow, sequence, data-flow, and lifecycle/state diagrams as explorable standalone HTML with inline SVG, dark/light themes, optional trace motion, and PNG/JPEG/WebP/SVG/WebM export. Accept plain-language requirements or pasted Mermaid flowchart, sequenceDiagram, and stateDiagram input; inspect repository evidence when the diagram must reflect real code. Use when the user asks to visualize system architecture, infrastructure, cloud/security/network topology, technical workflows, API call sequences, request lifecycles, data pipelines, ETL/ELT, data lineage, state machines, or to convert/beautify Mermaid. Also use for Archify, interactive or animated HTML diagrams, インタラクティブ図, 動く構成図. When an editable .drawio file is required, use the draw.io workflow instead and treat Archify HTML only as a supplement.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aktsmm/Agent-Skills262026년 10월 18일 업데이트

Generate ASCII-art text banners for terminals, READMEs, and CLI startup messages. Use when a user asks for an ASCII banner, text art, バナー, アスキーアート, or a decorative header for a script/README.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aktsmm/Agent-Skills262026년 10월 18일 업데이트

Generate Azure environment monthly report (Markdown + PowerPoint) from Azure Advisor and Cost Management API. Use when creating Advisor recommendations report, cost trend analysis, or security/reliability assessment for a customer subscription. Triggers on 'Advisor report', 'Azure monthly report', 'subscription report', 'Azure環境レポート', '簡易月次レポート'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

aktsmm/Agent-Skills262026년 10월 18일 업데이트

aktsmm의 스킬 모두 보기

이 스킬의 문제 신고