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correction-memory修正记忆

Agent Skill

correction-memory 用于记录任务执行中的错误、用户纠正、经验和能力缺口,适合在 OpenClaw 中希望让 Agent 持续沉淀问题、修正和最佳实践时使用。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

15,771

周安装

638

GitHub Stars

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下载量

4,951
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:correction-memory(修正记忆)
来源仓库:https://github.com/donovanpankratz-del/correction-memory
安装命令:
openclaw skills install correction-memory
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 correction-memory

简介

持久化记录用户对 Agent 输出的更正与改进建议。

  • 用于优化后续响应质量与减少重复错误。适用宿主包括 OpenClaw,接入前应确认版本、权限和运行环境要求。
  • 自动分类为最佳实践或知识缺口类型。correction-memory 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 需开启记忆功能并授权写入本地数据库。
  • 建议定期清理过期条目以保持系统效率。

SKILL.md

name
correction-memory
version
1.1.0
description
Makes agent corrections persistent and reusable. When you override, reject, or correct an agent's output, this skill logs the correction and automatically injects it into future spawns of the same agent type. Solves "agent keeps making the same mistake across sessions." Installs correction-tracker lib + injection hook into agent-context-loader. Works standalone or alongside intent-engineering skill.

Correction Memory

The Problem

When you correct an agent, that correction evaporates after the session. Next time you spawn the same agent type, it makes the same mistake. There's no memory of what you've already taught it.

What This Skill Installs

  • lib/correction-tracker.js — logs corrections per agent type to memory/corrections/[AgentType].jsonl
  • Hook into agent-context-loader.js — correction preamble prepended to spawns automatically (if intent-engineering is also installed)

Installation

Step 1 — Install correction-tracker

cp references/correction-tracker-template.js $OPENCLAW_WORKSPACE/lib/correction-tracker.js

Verify it runs:

node $OPENCLAW_WORKSPACE/lib/correction-tracker.js

Step 2 — Wire agent-context-loader (if using intent-engineering)

If lib/agent-context-loader.js is installed (from intent-engineering skill), correction injection is automatic — no wiring needed. The loader checks for correction-tracker.js at startup and loads it if present.

If you are NOT using intent-engineering, add this to your spawn logic manually:

const { buildCorrectionPreamble } = require('./lib/correction-tracker');

const agentType   = 'CoderAgent'; // or whatever agent you're spawning
const corrections = buildCorrectionPreamble(agentType, workspaceRoot);
const fullTask    = corrections ? corrections + '\
\
---\
\
' + originalTask : originalTask;

Logging Corrections

Programmatic

const { logCorrection } = require('./lib/correction-tracker');

logCorrection(
  'CoderAgent',                                    // agent type
  'Used ESM import instead of require()',          // what was wrong
  'Always use require() for Node.js stdlib modules', // correct behavior
  workspaceRoot,
  { session_channel: 'discord' }                  // optional metadata
);

Via main agent (natural language)

Just tell the main agent:

"Note that [AgentType]: [what it did wrong] — [correct behavior]"

The main agent will log it programmatically.

How Corrections Are Replayed

On every subagent spawn, agent-context-loader detects the agent type from the task description and prepends:

## Corrections from Previous Sessions

The following corrections were logged for CoderAgent. Apply these behaviors:

1. **[2026-03-01] Issue:** Used ESM import instead of require()
   **Correction:** Always use require() for Node.js stdlib modules

Only corrections from the last 30 days are injected. Older corrections expire automatically — stale rules don't accumulate.

Viewing Corrections

# All corrections for an agent type
cat $OPENCLAW_WORKSPACE/memory/corrections/CoderAgent.jsonl | jq .

# List all agent types with corrections
ls $OPENCLAW_WORKSPACE/memory/corrections/

# Count corrections per agent
for f in $OPENCLAW_WORKSPACE/memory/corrections/*.jsonl; do
  echo "$(basename $f .jsonl): $(wc -l < $f) corrections"
done

Agent Type Detection

The loader auto-detects agent type from the task description. Default rules:

Task keywordsAgent type
code, coder, impl, debugCoderAgent
writ, author, novel, chapterAuthorAgent
world, buildWorldbuilderAgent
(anything else)general

To add custom agent types, edit detectAgentType() in agent-context-loader.js.

References

  • references/correction-tracker-template.js — Full implementation of correction-tracker.js

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

92.68%
按下载量换算4,589

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install correction-memory 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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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