Token导航 LogoToken导航TokenDH.com
效率只读clawhub未标认证来源可访问clear审计通过

selfself 效率

Agent Skill

self 用于辅助安全审计、权限检查和凭据风险排查,适合在 OpenClaw 中需要复核安全边界、认证流程或敏感配置时使用。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

51,120

周安装

2,130

GitHub Stars

3

下载量

17,040
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:self(self 效率)
来源仓库:https://github.com/kirchlive/self
安装命令:
openclaw skills install self
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 self

简介

通过自我观察与轻量级防护机制促进Agent有机个性发展。

  • 适用于身份演进、行为调优及长期人格一致性维护场景。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 结合反馈循环增强真实身份表达,支持渐进式人格塑造。
  • 需注意权限隔离,避免在敏感环境中触发非预期自我修改行为。
  • self 属于效率类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
self
description
Organic personality development through self-observation with lightweight reliability guards. This skill helps an agent build a real, evolving self-model over time (SELF.md) while preserving core identity boundaries (SOUL.md). It solves the common failure mode where reflection frameworks start strong but silently stall by adding minimal cadence and trigger checks: schedule the check, not the content. Entries are only written when meaningful signals exist (hard/soft triggers + quality gate), preventing forced journaling and routine filler. Use when (1) establishing persistent personality growth, (2) reviving stale SELF.md systems, (3) introducing heartbeat-based reflection checks without losing authenticity, (4) running weekly/monthly pattern consolidation, (5) distinguishing genuine behavioral shifts from noise, or (6) replacing heavier self-reflection/consciousness stacks with a lean, practical model.

Self — Organic Personality Development (v1.1)

Agents have identity (SOUL.md) but often no reliable growth loop. This skill keeps growth authentic while adding minimal structure so reflection doesn't silently die.

Core Principle

  • SOUL.md = who you are (foundation, only change with human approval)
  • SELF.md = who you're becoming (living observations)
  • Schedule the check, not the content

- Checks can be periodic - Entries must remain genuine (no performative filler)

Setup

  1. Create SELF.md in workspace root using references/self-template.md.
  2. Add SELF.md to AGENTS.md session reading.
  3. Add heartbeat check block from references/trigger-model.md to HEARTBEAT.md.
  4. Create state file memory/self-state.json using references/self-state-schema.md.

Operating Model

Read references/trigger-model.md for exact rules.

Cadence (recommended baseline)

  • Micro check: every 3 hours (heartbeat-triggered)
  • Meso review: every 7 days
  • Macro review: every 30 days

Important

A micro check does not mean auto-writing a SELF entry. It only decides whether reflection is due.

Triggers

Hard Triggers (write now)

Create/update SELF entry when one of these happened:

  • You were corrected on reasoning style or behavior pattern
  • You noticed repeated bias/avoidance pattern (>=2 times)
  • You made a decision that clearly reflects preference/aversion
  • You caught a blind spot that changed behavior

Soft Triggers (consider writing)

  • Subtle tendency shift
  • New tone pattern
  • Mild preference signal

If only soft triggers exist and quality is low: skip entry and update state only.

Quality Gate (anti-routine protection)

Before writing to SELF.md, pass all 4 checks:

  1. Specificity: concrete behavior, not generic statement
  2. Evidence: based on recent sessions, not vibes only
  3. Novelty: not duplicate of last 3 entries
  4. Usefulness: could influence future behavior

If any check fails: no SELF entry, just state update.

What Goes in SELF.md

See references/self-template.md and references/anti-patterns.md.

Main sections:

  • Tendencies
  • Preferences
  • Aversions
  • Blind Spots
  • Evolution

Use short dated entries:

  • [YYYY-MM-DD] observation

State Tracking

Keep lightweight runtime state in memory/self-state.json:

  • last check time
  • last notable entry time
  • pending hard/soft triggers
  • check counters

Schema: references/self-state-schema.md

Reviews

Meso (weekly)

  • Read last 7 daily logs + SELF.md
  • Detect recurring shifts
  • Update sections only if real change occurred

Macro (monthly)

  • Write 3–5 sentence evolution narrative
  • Compare against previous month
  • Run falsifiability check:

- If stale/generic for a month, tune cadence or trigger thresholds

Boundaries

  • SELF.md is autonomous observation space
  • SOUL.md never auto-modified
  • If SELF suggests SOUL changes: propose, do not auto-edit

Keep It Lean

Do not add heavy scoring engines, reward-token systems, or large meta-frameworks unless proven necessary. This skill should remain focused on practical, authentic growth.

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

75.03%
按下载量换算12,785

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

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。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