Token导航 LogoToken导航TokenDH.com
研究检索只读clawhub未标认证来源可访问clear审计提醒

one-step-evolution一步进化

Agent Skill

one-step-evolution 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

8,421

周安装

344

GitHub Stars

公开资料未说明

下载量

2,697
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:one-step-evolution(一步进化)
来源仓库:https://github.com/staiv1/one-step-evolution
安装命令:
openclaw skills install one-step-evolution
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install one-step-evolution

简介

用于标准化 OpenClaw 火龙果架构部署,包括主节点与救援节点的分层配置。

  • 适用于新环境搭建或多实例协同管理的系统初始化场景。
  • 支持一键式分层文件结构生成,简化运维复杂度。one-step-evolution 属于研究检索类 Skill,可作为该场景下的辅助能力补充。
  • 需确保硬件资源满足高可用要求,避免单点故障影响稳定性。
  • 建议在非生产环境先行测试后再应用于正式部署流程。

SKILL.md

name
one-step-evolution
description
Use when you need to stand up or standardize a fresh OpenClaw setup as the Fire Dragon Fruit Architecture: one strong main, one isolated rescue, layered file memory, project truth docs, heartbeat, isolated cron, official Feishu integration, Ollama memory embeddings, and a durable long-running operating model. Search intents include OpenClaw architecture, OpenClaw memory, OpenClaw heartbeat, OpenClaw cron, OpenClaw Feishu, OpenClaw Ollama, OpenClaw rescue, Fire Dragon Fruit Architecture, 火龙果架构, 持续进化, 分层记忆, 调度任务, 长期记忆, 自动调度, 飞书机器人, 个人经营系统, 主脑架构, and personal operating system.

One Step Evolution

Overview

This skill installs the Fire Dragon Fruit Architecture, or 火龙果架构, onto a fresh OpenClaw setup and makes the resulting system durable for long-running work. It is optimized for a single strong main, one minimal rescue, layered memory on disk, an explicit evolution loop, project truth files, low-noise heartbeat, precise cron, and official integrations only.

Read fire-dragon-fruit-architecture.md for the positioning and distinguishing traits. Read architecture.md for the target design. Read checklist.md for the implementation runbook and acceptance criteria.

When To Use

Use this skill when the user wants any of the following:

  • Stand up a new OpenClaw directly in the Fire Dragon Fruit Architecture
  • Rebuild around main first and remove lab
  • Add durable memory that survives sessions and supports ongoing work
  • Add heartbeat and cron so the system can keep working without babysitting
  • Rework agent topology so fallback and rescue do not pollute the main brain
  • Standardize Feishu, runtime paths, and project structure using official mechanisms
  • Normalize an older OpenClaw install into the same architecture if needed

Do not use this skill for a tiny one-file tweak or a single prompt improvement. This skill is for system-level OpenClaw refactors.

Target State

The desired end state is the Fire Dragon Fruit Architecture:

  • One strong main agent as the primary brain
  • One minimal rescue agent for emergency continuity only
  • No lab in the steady-state production topology
  • One gateway per machine
  • Hosted frontier model as the main reasoning engine
  • Ollama only for memorySearch.provider = "ollama" embeddings unless the user explicitly wants more local inference
  • Layered memory on disk:

- MEMORY.md - memory/YYYY-MM-DD.md - memory/topics/*.md

  • Project truth kept in files, not chat history:

- projects/INDEX.md - projects/<project>/PRD.md - projects/<project>/PROGRESS.md - projects/<project>/EXECUTION_PLAN.md

  • Role behavior implemented as skills or explicit modes, not as many long-lived business agents
  • heartbeat for low-noise maintenance and isolated cron for scheduled work
  • Official @openclaw/feishu only
  • Fixed runtime paths instead of fragile version-manager-only service entrypoints

Core Capabilities

Every successful implementation of this skill must leave these three capabilities clearly present in files and runtime behavior.

1. Memory Capability

The system must have durable memory that survives sessions and can be searched, maintained, and promoted over time.

Required structure:

  • MEMORY.md for stable, curated truths
  • memory/YYYY-MM-DD.md for daily factual logs
  • memory/topics/*.md for evergreen knowledge
  • projects/INDEX.md plus project truth files so work context is not trapped in chat history

Required behavior:

  • daily facts go into date-stamped logs
  • reusable patterns get promoted into topic files
  • only high-signal truths stay in MEMORY.md
  • active projects are always recoverable from files without depending on chat transcripts

2. Evolution Capability

The system must be able to improve itself through file updates and workflow hardening, not by vague claims that the model "learns automatically".

Required behavior:

  • a reflect-mode or equivalent routine reviews recent daily logs, project progress, and failures
  • successful patterns are promoted into memory/topics/*.md, MEMORY.md, or private skills
  • failed instructions, dead prompts, and obsolete workflow branches are removed
  • useful repeated behaviors are upgraded into explicit skills or documented operating rules
  • improvement is judged by business or operating outcomes, not by how busy the agent looks

3. Scheduling Capability

The system must be able to keep working when the user is away.

Required behavior:

  • heartbeat handles low-noise health checks, stalled work detection, approval queues, and memory hygiene
  • regular cron handles timed visible outputs such as progress updates or morning focus
  • isolated cron handles heavier reflection, synthesis, document cleanup, and memory promotion tasks
  • every scheduled task must point at real files and a real purpose
  • side effects must stay behind approval gates or deterministic workflows

Template Kit

This skill includes a copy-ready starter workspace under assets/templates/workspace/.

Use it like this:

  • copy missing root files from assets/templates/workspace/
  • seed memory/topics/ from the provided topic templates
  • seed skills/*-mode/ from the provided role skill skeletons
  • copy projects/example-project/ and rename it to the real project name
  • adapt AUTOMATION.md into the target machine's real heartbeat and cron configuration
  • if rescue is missing, seed a separate minimal rescue workspace from assets/templates/rescue/

Important:

  • for a fresh OpenClaw, treat these templates as the default starting point
  • if the workspace already contains valuable project truth, merge carefully
  • do not overwrite real project history with template placeholders
  • use the templates to make the system explicit, then customize them to the machine and user

Workflow

1. Inspect The Current System

Before editing anything, inspect the real installation and summarize:

  • Agent list and routing
  • Current gateway mode and runtime paths
  • Model strategy
  • Memory layout
  • Existing skills
  • Feishu bindings
  • Heartbeat and cron jobs
  • Project directories that already contain useful truth

Pay special attention to duplicated agents, half-removed experiments, custom sidecars, and anything that can silently override the intended topology.

If the installation is fresh, keep this step short and use it to confirm the baseline before applying the templates.

2. Reshape The Agent Topology

Refactor toward:

  • main as the only daily operating brain
  • rescue as a separate emergency agent with minimal memory and minimal skills
  • No lab unless the user explicitly requests a research sandbox

Rules:

  • main and rescue must not depend on each other's memory files
  • rescue should not run routine business planning or learning loops
  • If a second external account exists, route it to rescue only if that protects continuity without creating cross-talk

3. Rebuild The Memory Layer

Create or repair the file-based memory system:

  • MEMORY.md for curated durable truths
  • memory/YYYY-MM-DD.md for daily factual logs
  • memory/topics/*.md for evergreen knowledge such as users, offers, workflows, channels, and operating rules

Keep MEMORY.md small and high-signal. Do not turn it into a dumping ground.

Build a promotion path, not just storage:

  • raw facts land in daily logs
  • repeated truths move into memory/topics/*.md
  • only durable operating truths move into MEMORY.md
  • archived project conclusions move into the relevant project files

If projects exist, establish a file-based source of truth:

  • projects/INDEX.md declares active projects
  • Each active project has PRD.md, PROGRESS.md, and EXECUTION_PLAN.md
  • Heartbeat and cron instructions must explicitly reference these project files

If these files are missing, seed them from assets/templates/workspace/projects/example-project/ and then replace placeholders with the real project data.

4. Convert Behavior Into Skills And Modes

Prefer a single main plus role skills such as:

  • scout-mode
  • closer-mode
  • ops-mode
  • reflect-mode

Do not split these into separate long-lived agents unless there is a hard routing or permissions reason.

When implementing, move durable operating logic into:

  • skills/
  • AGENTS.md
  • HEARTBEAT.md
  • project truth files

Do not bury core operating rules inside transient chats.

If role skills are missing, seed them from assets/templates/workspace/skills/.

5. Rebuild Automation The Stable Way

Use:

  • heartbeat for low-noise checks, stalled work detection, approval queues, and memory hygiene
  • regular cron for user-facing timed updates
  • isolated cron for higher-noise reflection, cleanup, and document maintenance

Automation rules:

  • Keep heartbeat small and cheap
  • Use project files as the source of truth
  • Prefer isolated sessions for heavy scheduled work
  • Keep external side effects behind explicit approval or deterministic workflow controls

If the current install has no useful cadence, establish a default blueprint:

  • heartbeat: every 30 minutes during active hours for health, stalled work, and pending approvals
  • main cron: timed operational updates such as hourly progress or morning focus
  • isolated cron: nightly reflection and memory maintenance

Make sure scheduled tasks reference the actual project files they should read and update.

If the install has no scheduling docs at all, start from assets/templates/workspace/AUTOMATION.md.

6. Install An Explicit Evolution Loop

Add a recurring improvement loop so the system can get better instead of just accumulate clutter.

Minimum loop:

  • read recent memory/YYYY-MM-DD.md logs
  • inspect current project truth files
  • identify repeated wins, failures, and stale instructions
  • promote stable rules into memory/topics/*.md or MEMORY.md
  • convert repeated work into skills or durable SOP files
  • delete or rewrite prompt clutter that no longer helps

The evolution loop should be visible in files. If another operator opens the workspace, they should be able to see how the system learns.

7. Standardize Integrations And Runtime

Use official pieces wherever possible:

  • official @openclaw/feishu
  • official Ollama embeddings path for memory search
  • fixed runtime paths for gateway or service scripts

Avoid:

  • unofficial Feishu forks when the official package covers the need
  • ad hoc embeddings sidecars when official Ollama embeddings are available
  • brittle service wrappers that only work under one shell or version-manager session
  • adding a small local text model by default when the hosted primary model is already stable and budget allows it

8. Validate, Clean Up, And Finish

After edits:

  • verify the topology matches the target state
  • verify the memory layout exists on disk
  • verify heartbeat and cron point at real files and real tasks
  • verify memory promotion and reflection paths are explicit
  • verify Feishu routing is intentional
  • verify model settings reflect the desired primary and embedding strategy
  • remove obsolete agents, dead directories, and misleading docs
  • run the relevant validation commands
  • commit the result if the repo exists and the user expects versioned changes

Guardrails

  • Do not reintroduce lab by default
  • Do not create many agents when skills are enough
  • Do not store project truth only in chat transcripts
  • Do not make rescue a shadow copy of main
  • Do not install extra local text models unless there is a clear reliability or cost reason
  • Do not leave heartbeat or cron without file references and success criteria
  • Do not claim "continuous learning" unless the evolution loop is visible in files and schedules

Deliverable

When this skill is used well, the final output should leave the system in a state where:

  • main can keep operating day to day
  • rescue exists but stays isolated and quiet
  • important facts survive across sessions because they live on disk
  • projects have explicit truth files
  • heartbeat and cron keep work moving
  • the setup is easy for another operator or another Codex to replicate

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

87.96%
按下载量换算2,372

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills