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Agently Playbook

作者 Maplemx · GitHub ↗ · v0.1.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install agently-playbook
功能描述
Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow fr...
使用说明 (SKILL.md)

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

  1. Reduce the request into scenario and atomic goals.
  2. If the request is a project initialization or structure refactor, choose the owner layers, async boundary, and repo skeleton first.
  3. Choose the narrowest native Agently capability path.
  4. Name the concrete operations or primitives that should be used.
  5. 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, or KeyWaiter -> 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.md
  • references/project-framework.md
安全使用建议
This skill is a high-level design/playbook and appears coherent and low-risk by itself. Before using it in a production agent, remember: (1) it encourages using environment variables and .env patterns — do not paste secrets into chat or prompts; store provider keys in secure vaults or protected env variables; (2) follow-up work will be routed to specific agently-* leaf skills that may need provider credentials — review those leaf skills separately before granting secrets; (3) because it's instruction-only, it won't install code, but any code you scaffold based on its advice will. If you need the agent to work with live credentials, only provide them to trusted code/skills and verify those skills' install and env requirements first.
功能分析
Type: OpenClaw Skill Name: agently-playbook Version: 0.1.0 The agently-playbook skill bundle consists entirely of architectural guidance and workflow instructions for an AI agent to assist users in building applications with the Agently framework. The files (SKILL.md, references/capability-map.md, and references/project-framework.md) define project structures, prompt management rules, and routing logic to other skills without containing any executable code, data exfiltration logic, or malicious prompt injection attempts.
能力评估
Purpose & Capability
The name/description match the SKILL.md and reference files: this is a high-level playbook for shaping model-powered assistants and routing to leaf Agently capabilities. It does not request unrelated credentials, binaries, or system access.
Instruction Scope
The instructions are developer-facing guidance (project splitting, async-first rules, settings patterns) and do not direct the agent to read arbitrary user files or exfiltrate data. They do recommend using ${ENV.xxx} placeholders and validating env values at initialization — appropriate for a project playbook, but something to be aware of because downstream implementation steps may prompt for or require environment secrets.
Install Mechanism
No install spec or code files — instruction-only skill. Nothing is downloaded or written to disk by the skill itself.
Credentials
The skill declares no required environment variables or credentials. The references and SKILL.md recommend best-practice use of ${ENV.xxx} and .env patterns for implementations, which is proportionate to the stated purpose but means real implementations will later request provider keys (expected for downstream, implementation-specific skills).
Persistence & Privilege
always is false and there are no install hooks or config paths. The skill does not request permanent presence or system-level privileges.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install agently-playbook
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /agently-playbook 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v0.1.0
Initial release of agently-playbook. - Introduces a skill for building, initializing, validating, optimizing, or refactoring model-powered assistants, internal tools, automations, or workflows from high-level business scenarios. - Establishes a workflow to clarify goals, select owner layer, repo skeleton, and appropriate Agently capability path. - Defines routing rules for specialized tasks like model setup, prompt management, output control, orchestration, and migrations. - Emphasizes async-first patterns and discourages early low-level implementation or custom orchestration. - Provides anti-patterns to avoid during early solution shaping. - Lists relevant references for further guidance.
元数据
Slug agently-playbook
版本 0.1.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

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。

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