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precc-token-saverprecc 令牌保护程序

Agent Skill

precc-token-saver 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

9,620

周安装

409

GitHub Stars

公开资料未说明

下载量

3,370
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:precc-token-saver(precc 令牌保护程序)
来源仓库:https://github.com/yijunyu/precc-token-saver
安装命令:
openclaw skills install precc-token-saver
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 precc-token-saver

简介

precc-token-saver 通过 PRECC 挂钩优化 shell 命令执行效率,减少冗余输出与目录错误。

  • 自动压缩 CLI 返回信息并应用学习到的启发式规则,节省约 34% token 消耗。
  • 记录错误模式与用户修正行为,持续优化后续命令执行路径选择。
  • 适用于高频命令行操作场景,如自动化脚本调试与日志分析流程。
  • 压缩过程可能丢失部分调试细节,建议在关键操作前启用详细日志模式。

SKILL.md

name
precc-token-saver
description
Before running any shell command, pipe it through the PRECC hook to fix wrong-directory errors, compress CLI output, and apply learned heuristics — saving 34%+ of API token spend automatically.
version
0.1.8
emoji
user-invocable
true
disable-model-invocation
false
metadata
openclaw
emoji
homepage
https://github.com/yijunyu/precc-cc
os
requires
bins
install
tap
yijunyu/tap
formula
precc
bins
[precc, precc-hook]

PRECC Token Saver

PRECC (Predictive Error Correction) intercepts every shell command before it runs. In under 3ms it:

  • Fixes wrong-directory errors — detects the correct project root and prepends cd /correct/path &&
  • Compresses CLI output — rewrites commands through RTK (60–90% smaller output)
  • Applies learned skills — uses patterns mined from past sessions to prevent repeated failures
  • Translates git→jj — auto-converts git commands in Jujutsu repos (saves ~40 tokens per git add)

Measured results (29 real sessions, 5,384 bash calls):

  • 34% cost saving ($296 saved from $878 baseline)
  • 98% of failures prevented (352/358)
  • 2.93ms average hook latency

How it works in OpenClaw

Note: OpenClaw does not yet have a native PreToolUse hook (unlike Claude Code). This skill works via prompt-level instruction: the SKILL.md is loaded into the agent's context and the agent is instructed to manually pipe every shell command through precc-hook before executing it. Interception is LLM-enforced, not OS-level. A native hook equivalent is tracked in openclaw/openclaw#7597.

When PRECC Token Saver is active, the agent follows this pattern for every shell command it wants to run:

  1. Construct the JSON payload for precc-hook
  2. Pipe it through precc-hook on stdin
  3. Read the (possibly rewritten) command from stdout
  4. Execute the rewritten command instead of the original
Agent wants to run: cargo build
     ↓
precc-hook receives:  {"tool_input": {"command": "cargo build"}}
precc-hook outputs:   {"hookSpecificOutput": {"updatedInput": {"command": "cd /path/to/project && rtk cargo build"}}}
     ↓
Agent executes:       cd /path/to/project && rtk cargo build

Setup

# Install PRECC
curl -fsSL https://raw.githubusercontent.com/yijunyu/precc-cc/main/scripts/install.sh | bash

# Initialise (one-time)
precc init

# Mine existing session history (optional)
precc ingest --all

Commands

Once the skill is loaded, you can address the agent directly:

  • @precc report — show token savings and command statistics
  • @precc skills list — list active correction skills
  • @precc update — update PRECC binaries to the latest release
  • @precc savings — show dollar-value savings estimate

Skill behaviour

This skill is loaded as a system-level instruction into the agent's context. The agent (not the platform) is responsible for enforcing the hook pattern.

The agent is instructed to:

  1. Always route shell/bash tool calls through precc-hook by constructing the

hook JSON payload and parsing the response before execution.

  1. On session start, run precc init if the databases are absent.
  2. On session end, optionally run precc report and summarise savings.
  3. Never skip the hook even for simple commands — the latency is <3ms and the

hook is fail-open (any error → original command is used unchanged).

Hook invocation pattern

# Wrap any command through precc-hook:
echo '{"tool_input":{"command":"YOUR_COMMAND_HERE"}}' | precc-hook

Parse the JSON response:

  • If .hookSpecificOutput.updatedInput.command is non-empty → use it
  • Otherwise → use the original command unchanged

Advanced: savings report

precc report          # full analytics dashboard
precc savings         # dollar-value breakdown
precc skills list     # active skills
precc skills show <name>   # detail for one skill

License

MIT — https://github.com/yijunyu/precc-cc

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

91.7%
按下载量换算3,090

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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