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agently-playbook

Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow from a business scenario or common problem statement, including project-structure refactors or starter skeletons that may separate model setup, prompt config, and orchestration, even if the request also mentions a UI, app shell, or local model service such as Ollama, and it is still unclear whether the solution should stay a single request, ad

作者: admin | 来源: ClawHub
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ClawHub
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V 0.1.0
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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 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`

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skill ai

通过对话安装

该技能支持在以下平台通过对话安装:

OpenClaw WorkBuddy QClaw Kimi Claude

方式一:安装 SkillHub 和技能

帮我安装 SkillHub 和 agently-playbook-1776109741 技能

方式二:设置 SkillHub 为优先技能安装源

设置 SkillHub 为我的优先技能安装源,然后帮我安装 agently-playbook-1776109741 技能

通过命令行安装

skillhub install agently-playbook-1776109741

下载 Zip 包

⬇ 下载 agently-playbook v0.1.0

文件大小: 5.49 KB | 发布时间: 2026-4-17 13:58

v0.1.0 最新 2026-4-17 13:58
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.

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