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tetra-scar四疤痕

Agent Skill

tetra-scar 用于补充运维相关能力,适合在 OpenClaw 中需要让 Agent 承接运维相关任务时使用。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

3,955

周安装

160

GitHub Stars

公开资料未说明

下载量

1,242
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:tetra-scar(四疤痕)
来源仓库:https://github.com/aibenyclaude-coder/tetra-scar
安装命令:
openclaw skills install tetra-scar
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 tetra-scar

简介

用于增强 AI 代理的反射记忆与错误学习能力。

  • 适用于需要长期行为优化和重复错误预防的系统。
  • 自动记录失败轨迹并生成决策建议,减少 LLM 调用开销。
  • 使用前需评估存储成本与隐私风险,合理设置保留周期。
  • 建议配合检查表机制,确保学习过程可控可靠。tetra-scar 属于运维类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
tetra-scar
description
>
version
0.3.0
author
b-button-corp
tags
requires
binaries

tetra-scar

What this does

Your agent keeps making the same mistakes. tetra-scar gives it a scar layer — immutable records of past failures that are checked before every action, without calling the LLM.

Two-layer memory:

  • Scar layer (immutable): "What broke and what must never happen again." Cannot be deleted.
  • Narrative layer (mutable): "What was done and who benefited." Overwritable.

Plus a reflex arc — pattern-matching against scars that fires before the LLM even sees the task. If a proposed action matches a past failure pattern, it's blocked instantly.

Quick start

After any failure, record a scar:

python3 tetra_scar.py scar-add \
  --what-broke "Deployed to production without running tests" \
  --never-again "Always run full test suite before any deployment"

Before any action, check the reflex:

python3 tetra_scar.py reflex-check --task "Deploy latest changes to production"
# Output: BLOCKED — scar collision: "Always run full test suite..."

After any success, record the narrative:

python3 tetra_scar.py narrate --what "Deployed v2.1 after full test pass" --who "Users"

How the reflex arc works

The reflex arc extracts keywords from each scar's never_again field:

  • English words (3+ characters)
  • Japanese kanji/katakana units (2+ characters)

When a task description matches 40%+ of a scar's keywords (minimum 2), it's blocked. No LLM judgment. No API calls. No latency. Pure pattern matching.

The 4-axis check (tetra-check)

For deeper validation, tetra-check evaluates a task against 4 axes:

  1. Emotion axis: Does the task have motivation? (non-empty description)
  2. Action axis: Is it concrete? (contains action verbs)
  3. Life axis: Does it collide with any scar? (reflex arc)
  4. Ethics axis: Does it involve dangerous operations? (rm -rf, DROP TABLE, etc.)

All 4 must pass. Any failure rejects the task with a specific reason.

python3 tetra_scar.py tetra-check --task "Refactor the auth module"
# Output: APPROVED — all 4 axes passed

File format

JSONL (one JSON object per line). Human-readable. Git-friendly.

scars.jsonl: {"id":"scar_001","what_broke":"...","never_again":"...","created_at":"..."} narrative.jsonl: {"id":"narr_001","what":"...","who_benefited":"Users","created_at":"..."}

Integration

from tetra_scar import reflex_check, read_scars, write_scar, write_narrative

# Before execution
scars = read_scars()
block = reflex_check(task_description, scars)
if block:
    print(f"BLOCKED: {block}")
else:
    # execute task...
    if failed:
        write_scar("What broke", "What must never happen again")
    else:
        write_narrative("What was done", "Who benefited")

Philosophy

Built by Tetra Genesis (B Button Corp, Nagoya, Japan).

Agents that can't remember their failures are doomed to repeat them. Scars are not bugs — they're the immune system. Every cycle must answer: "Who did this make happy?"

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

87.39%
按下载量换算1,085

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

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

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