/install agently-playbook
Agently Playbook
Use this skill first when the request still starts from business goals, refactor goals, product behavior, or broad model-app language.
The user does not need to say Agently, TriggerFlow, or any other framework term. Generic asks such as "build an assistant", "help me design an internal tool", or "create a validator for common problems" should still start here when the owner layer is unresolved.
Requests that also mention a UI, a web page, a desktop shell, or a local model service such as Ollama should still start here when the request is fundamentally about shaping a model-powered tool rather than only wiring one narrow capability.
Workflow
- Reduce the request into scenario and atomic goals.
- If the request is a project initialization or structure refactor, choose the owner layers, async boundary, and repo skeleton first.
- Choose the narrowest native Agently capability path.
- Name the concrete operations or primitives that should be used.
- Name the validation rule that proves the design stayed native-first.
Native-First Rules
- default to async-first guidance for service code, streaming, TriggerFlow, and any path that may overlap work or benefit from cancellation
- treat sync APIs as wrappers for scripts, REPL use, or compatibility bridges unless the host truly requires sync-only integration
- when the request is a project-shape refactor, separate settings, prompts, services, domain contracts, workflow, and tests before discussing low-level implementation details
Capability Routing
- model provider setup, settings-file-based model separation, or
${ENV.xxx}-backed settings loading ->agently-model-setup - request-side prompt design, prompt placeholder injection, or config-file prompt bridge ->
agently-prompt-management - output schema and reliability ->
agently-output-control - response reuse, metadata, or streaming consumption ->
agently-model-response - session continuity or restore ->
agently-session-memory - tools, MCP, FastAPIHelper,
auto_func, orKeyWaiter->agently-agent-extensions - embeddings, KB, or retrieval-to-answer ->
agently-knowledge-base - branching, concurrency, waiting/resume, mixed sync/async orchestration, event-driven fan-out, process-clarity refactors, runtime stream, or explicit multi-stage quality loops ->
agently-triggerflow - migration choice between LangChain and LangGraph ->
agently-migration-playbook
Anti-Patterns
- do not skip this playbook when the owner layer is unresolved
- do not invent custom output parsers, retry loops, or orchestration first
- do not let sync-first sample code dictate the service architecture when the target is clearly async-capable
- do not split project initialization into a fake standalone framework surface before the owner layers are chosen
- do not treat multi-agent, judge, or review flows as separate framework surfaces before checking native Agently capabilities
Read Next
references/capability-map.mdreferences/project-framework.md
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install agently-playbook - 安装完成后,直接呼叫该 Skill 的名称或使用
/agently-playbook触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Agently Playbook 是什么?
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow fr... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 152 次。
如何安装 Agently Playbook?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install agently-playbook」即可一键安装,无需额外配置。
Agently Playbook 是免费的吗?
是的,Agently Playbook 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Agently Playbook 支持哪些平台?
Agently Playbook 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Agently Playbook?
由 Maplemx(@maplemx)开发并维护,当前版本 v0.1.0。