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nm-conserve-token-conservationnm 保护代币保护

Agent Skill

nm-conserve-token-conservation 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

2,688

周安装

112

GitHub Stars

公开资料未说明

下载量

896
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:nm-conserve-token-conservation(nm 保护代币保护)
来源仓库:https://github.com/athola/nm-conserve-token-conservation
安装命令:
openclaw skills install nm-conserve-token-conservation
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 nm-conserve-token-conservation

简介

在会话初始化阶段强制执行令牌配额保护规则。

  • 适合在 OpenClaw 中预防意外超支风险时使用。
  • 核心能力是预判高成本操作并施加约束。
  • 通过 clawhub 安装,需设定合理的预算上限。
  • 注意监控实际消耗与预估偏差情况。nm-conserve-token-conservation 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
token-conservation
description
|
version
1.8.2
metadata
{"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/conserve", "emoji": "\�\�"}}
source
claude-night-market
source_plugin
conserve
Night Market Skill — ported from claude-night-market/conserve. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Token Conservation Workflow

When To Use

  • Run at the start of every session and whenever prompt sizes or tool calls begin to spike.
  • Mandatory before launching long-running analyses, wide diffs, or massive context loads.

When NOT To Use

  • Context-optimization already handles the scenario
  • Simple queries with minimal context

Required TodoWrite Items

  1. token-conservation:quota-check
  2. token-conservation:context-plan
  3. token-conservation:delegation-check
  4. token-conservation:compression-review
  5. token-conservation:logging

Step 1 – Quota Check (quota-check)

  • Record current session duration and weekly usage (from /status or notebook).

Note the 5-hour rolling cap + weekly cap highlighted in the Claude community notice.

  • Capture remaining budget and set a max token target for this task.

Step 2 – Context Plan (context-plan)

  • Set a discovery read budget BEFORE reading any files. Count each Read call

and each content-mode Grep as one read. Glob and files-with-matches Grep are free. - Implement from spec/requirements: max 8 reads - Bug fix at known location: max 5 reads - Refactor with known scope: max 1 read per file being changed - Open exploration: max 15 reads

  • Read order (most valuable first): spec/requirements, files to modify,

imports/interfaces, then stop and start writing.

  • When budget is spent: ask the user if more context is needed. Do NOT

self-authorize additional reads. Only explicit user approval overrides the budget.

  • Prefer Read with offset/limit params or Grep tool over loading whole files.

A Read targeting <50 lines counts as 0.5 reads. Avoid cat/sed/awk via Bash — Claude Code 2.1.21+ steers toward native file tools (Read, Edit, Write, Grep, Glob).

  • PDFs (Claude Code 2.1.30+): Use Read with pages: "1-5" for targeted PDF reading

instead of loading entire documents. Large PDFs (>10 pages) return a lightweight reference when @-mentioned — use the pages parameter to read specific sections. Hard limits: 100 pages max, 20MB max per PDF. Exceeding these previously locked sessions permanently (fixed in 2.1.31).

  • Convert prose instructions into bullet lists before prompting so only essential

info hits the model.

Step 3 – Delegation Check (delegation-check)

  • Evaluate whether compute-intensive tasks can go to Qwen MCP or other external

tooling (use qwen-delegation skill if needed).

  • For local work, favor deterministic scripts (formatters, analyzers) instead

of LLM reasoning when possible.

Step 4 – Compression Review (compression-review)

  • Summarize prior steps/results before adding new context.

Remove redundant history, collapse logs, and avoid reposting identical code.

  • Use prompt caching ideas: reference prior outputs instead of restating them

when the model has already processed the information (cite snippet IDs).

  • Decide whether the current thread should be compacted:

- If only recent context is stale, use "Summarize from here" (Claude Code 2.1.32+) via the message selector to partially summarize the conversation — this preserves recent context while compressing older portions - If the active workflow is finished and earlier context will not be reused, instruct the user to run /new - If progress requires the existing thread but the window is bloated, prompt them to run /compact before continuing

  • Automatic memory (Claude Code 2.1.32+): Claude now records and recalls session

memories automatically. This adds minor token overhead but improves cross-session continuity. No action needed — be aware it contributes to baseline context usage.

Step 5 – Logging (logging)

Document the conservation tactics that were applied and note the remaining token budget. If the budget is low, explicitly warn the user and propose secondary plans. Record any recommendations made regarding the use of /new or /compact, or justify why neither was necessary, to inform future context-handling decisions.

Output Expectations

  • A short explanation of token-saving steps, delegated tasks, and remaining runway.
  • Concrete next-action list that keeps the conversation lean (e.g.):

- "next turn: provide only failing test output lines 40-60"

  • Explicit reminder about /new or /compact whenever you determine it would save

tokens (otherwise state that no reset/compaction is needed yet).

Troubleshooting

Common Issues

Command not found Ensure all dependencies are installed and in PATH

Permission errors Check file permissions and run with appropriate privileges

Unexpected behavior Enable verbose logging with --verbose flag

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

70.67%
按下载量换算633

安全审计

VirusTotal

未展示

ClawScan

通过

Static analysis

通过

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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