ClawHub

Agently Playbook

v0.1.0

Use when the user wants to build, initialize, validate, optimize, or refactor a model-powered assistant, internal tool, automation, evaluator, or workflow fr...

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by@maplemx

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OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for maplemx/agently-playbook.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Agently Playbook" (maplemx/agently-playbook) from ClawHub.
Skill page: https://clawhub.ai/maplemx/agently-playbook
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

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openclaw skills install agently-playbook

ClawHub CLI

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npx clawhub@latest install agently-playbook
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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.
ℹ
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.
Assessment
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.

Like a lobster shell, security has layers — review code before you run it.

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149downloads
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1versions
Updated 1mo ago
v0.1.0
MIT-0
@maplemx
latest
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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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