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auto-improvement-executor自动改进执行器

Agent Skill

auto-improvement-executor 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

2,752

周安装

117

GitHub Stars

公开资料未说明

下载量

964
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:auto-improvement-executor(自动改进执行器)
来源仓库:https://github.com/lanyasheng/auto-improvement-executor
安装命令:
openclaw skills install auto-improvement-executor
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 auto-improvement-executor

简介

auto-improvement-executor 用于查找、检索和筛选相关信息,适合在 OpenClaw 中根据关键词快速定位候选结果时使用。

  • 支持文件变更应用、回滚与预览,适用于改进方案的实施管理。
  • 通过 clawhub 安装,命令为 openclaw skills install auto-improvement-executor,需结合来源仓库进一步确认具体用法。
  • 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操作。
  • 当前功能描述基于原始 README,实际能力以官方文档为准。

SKILL.md

name
improvement-executor
category
tool
description
当需要把已批准的改进候选应用到目标文件、回滚之前的变更、或预览变更效果时使用。支持 4 种 action(append/replace/insert_before/update_yaml),每次变更前自动备份。不用于打分(用 improvement-discriminator)或门禁验证(用 improvement-gate)。
license
MIT
triggers
version
0.1.0
author
OpenClaw Team

Improvement Executor

Applies accepted candidates with automatic backup and rollback.

When to Use

  • 把已批准的改进候选应用到目标文件
  • 回滚之前的变更(通过 receipt)
  • --dry-run 预览变更

When NOT to Use

  • 给候选打分 → use improvement-discriminator
  • 门禁验证 → use improvement-gate
  • 全流程编排 → use improvement-orchestrator

CLI

# Apply a candidate (requires ranking artifact + candidate ID)
python3 scripts/execute.py \
  --input ranking.json \           # REQUIRED: ranking artifact from discriminator
  --candidate-id cand-01-docs \    # REQUIRED: which candidate to execute
  --state-root /path/to/state \    # default: lib/state_machine.DEFAULT_STATE_ROOT
  --output result.json \           # default: {state-root}/executions/{run-id}-{candidate-id}.json
  --force                          # execute even if recommendation != accept_for_execution

# Rollback a previous change
python3 scripts/rollback.py --receipt receipt.json [--dry-run]
ParamDefaultWhen to change
--input(required)Always: path to ranking artifact JSON from discriminator
--candidate-id(required)Always: the id field of the candidate to execute
--forcefalseUse to execute candidates with recommendation=hold (bypasses critic check)
--outputautoSet for custom output location

4 Action Types

ActionTriggerBehavior
append_markdown_sectionexecution_plan.actionAppends heading + bulleted content lines at EOF. No-op if heading already exists
replace_markdown_sectionexecution_plan.actionFinds section by heading match, replaces all lines until next same-or-higher-level heading
insert_before_sectionexecution_plan.actionInserts content lines before a matched heading
update_yaml_frontmatterexecution_plan.actionMerges frontmatter_updates dict into YAML frontmatter (requires PyYAML)

Backup Mechanism

Every execution creates a backup at {state-root}/executions/backups/{run-id}/{candidate-id}-{filename} BEFORE modifying the target. The backup path is stored in result.rollback_pointer.backup_path for gate-driven rollback.

Safety Guards

  1. Recommendation check: refuses to execute if recommendation != accept_for_execution (use --force to override)
  2. Category check: only EXECUTOR_SUPPORTED_CATEGORIES (docs, reference, guardrail) are auto-executable; others return status=unsupported
  3. File existence: target file must exist, otherwise SystemExit
  4. Execution trace: every run captures a structured execution_trace dict with action, status, diff_summary for GEPA feedback loop

<example> 正确: 执行一个 docs 类候选 $ python3 scripts/execute.py --input ranking.json --candidate-id cand-01-docs --state-root ./state → Backup: ./state/executions/backups/run001/cand-01-docs-README.md → Appended "## Operator Notes" section → stdout: ./state/executions/run001-cand-01-docs.json </example>

<anti-example> 错误: 跳过 discriminator 直接执行 medium-risk candidate $ python3 scripts/execute.py --input ranking.json --candidate-id cand-04-prompt --force → category=prompt 不在 EXECUTOR_SUPPORTED_CATEGORIES → status=unsupported → medium/high risk 必须经 gate 走 pending_promote → 人工审批 </anti-example>

Output Artifact

{"stage": "executed", "status": "success", "candidate_id": "cand-01-docs",
 "result": {"status": "success", "modified": true, "diff": "--- a/...",
   "backup_path": "...", "rollback_pointer": {"method": "restore_backup_file",
     "backup_path": "...", "target_path": "..."}},
 "execution_trace": {"type": "execution_trace", "action": "append_markdown_section", ...},
 "next_step": "apply_gate", "next_owner": "gate"}

Related Skills

  • improvement-discriminator: Scores candidates → executor only runs accept_for_execution ones
  • improvement-gate: Validates execution results → may trigger rollback via rollback_pointer
  • improvement-orchestrator: Calls executor as stage 4, passes ranking artifact + candidate ID

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

82.54%
按下载量换算796

安全审计

VirusTotal

通过

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。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

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