← 返回
未分类 中文

Agently Playbook

Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow fr...
用于当用户想要构建、初始化、验证、优化或重构模型驱动的助手、内部工具、自动化、评估器或工作流...
maplemx maplemx 来源
未分类 clawhub v0.1.0 1 版本 100000 Key: 无需
★ 0
Stars
📥 395
下载
💾 0
安装
1
版本
#latest

概述

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

版本历史

共 1 个版本

  • v0.1.0 当前
    2026-03-30 22:10 安全 安全

安全检测

腾讯云安全 (Keen)

安全,无风险
查看报告

腾讯云安全 (Sanbu)

安全,无风险
查看报告

🔗 相关推荐

ai-agent

Skill Vetter

spclaudehome
AI智能体技能安全预审工具。安装ClawdHub、GitHub等来源技能前,检查风险信号、权限范围及可疑模式。
★ 1,247 📥 272,371
ai-agent

Self-Improving + Proactive Agent

ivangdavila
自我反思+自我批评+自我学习+自组织记忆。智能体评估自身工作、发现错误并持续改进。
★ 1,425 📥 326,731
ai-agent

self-improving agent

pskoett
记录自身发现以实现自我改进的技能
★ 4,140 📥 913,940