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lobster-context-budget龙虾背景预算

Agent Skill

lobster-context-budget 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。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

2,952

周安装

123

GitHub Stars

公开资料未说明

下载量

984
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:lobster-context-budget(龙虾背景预算)
来源仓库:https://github.com/wangxiaofei860208-source/lobster-context-budget
安装命令:
openclaw skills install lobster-context-budget
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 lobster-context-budget

简介

lobster-context-budget 审核 Claude Code 上下文窗口消耗,识别冗余组件并优化优先级,适合 OpenClaw 中维护高效开发流程。

  • 适用于减少模型 token 浪费,提升多代理协作效率。
  • 通过分析跨代理、MCP 服务器和规则的上下文使用情况生成优化建议。
  • 需谨慎处理敏感信息,避免将输出直接作为最终决策依据。
  • 建议结合具体项目规模调整预算阈值与检查频率。

SKILL.md

name
lobster-context-budget
description
Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.
origin
ECC

Context Budget

Analyze token overhead across every loaded component in a Claude Code session and surface actionable optimizations to reclaim context space.

When to Use

  • Session performance feels sluggish or output quality is degrading
  • You've recently added many skills, agents, or MCP servers
  • You want to know how much context headroom you actually have
  • Planning to add more components and need to know if there's room
  • Running /context-budget command (this skill backs it)

How It Works

Phase 1: Inventory

Scan all component directories and estimate token consumption:

Agents (agents/*.md)

  • Count lines and tokens per file (words × 1.3)
  • Extract description frontmatter length
  • Flag: files >200 lines (heavy), description >30 words (bloated frontmatter)

Skills (skills/*/SKILL.md)

  • Count tokens per SKILL.md
  • Flag: files >400 lines
  • Check for duplicate copies in .agents/skills/ — skip identical copies to avoid double-counting

Rules (rules/**/*.md)

  • Count tokens per file
  • Flag: files >100 lines
  • Detect content overlap between rule files in the same language module

MCP Servers (.mcp.json or active MCP config)

  • Count configured servers and total tool count
  • Estimate schema overhead at ~500 tokens per tool
  • Flag: servers with >20 tools, servers that wrap simple CLI commands (gh, git, npm, supabase, vercel)

CLAUDE.md (project + user-level)

  • Count tokens per file in the CLAUDE.md chain
  • Flag: combined total >300 lines

Phase 2: Classify

Sort every component into a bucket:

BucketCriteriaAction
Always neededReferenced in CLAUDE.md, backs an active command, or matches current project typeKeep
Sometimes neededDomain-specific (e.g. language patterns), not referenced in CLAUDE.mdConsider on-demand activation
Rarely neededNo command reference, overlapping content, or no obvious project matchRemove or lazy-load

Phase 3: Detect Issues

Identify the following problem patterns:

  • Bloated agent descriptions — description >30 words in frontmatter loads into every Task tool invocation
  • Heavy agents — files >200 lines inflate Task tool context on every spawn
  • Redundant components — skills that duplicate agent logic, rules that duplicate CLAUDE.md
  • MCP over-subscription — >10 servers, or servers wrapping CLI tools available for free
  • CLAUDE.md bloat — verbose explanations, outdated sections, instructions that should be rules

Phase 4: Report

Produce the context budget report:

Context Budget Report
═══════════════════════════════════════

Total estimated overhead: ~XX,XXX tokens
Context model: Claude Sonnet (200K window)
Effective available context: ~XXX,XXX tokens (XX%)

Component Breakdown:
┌─────────────────┬────────┬───────────┐
│ Component       │ Count  │ Tokens    │
├─────────────────┼────────┼───────────┤
│ Agents          │ N      │ ~X,XXX    │
│ Skills          │ N      │ ~X,XXX    │
│ Rules           │ N      │ ~X,XXX    │
│ MCP tools       │ N      │ ~XX,XXX   │
│ CLAUDE.md       │ N      │ ~X,XXX    │
└─────────────────┴────────┴───────────┘

WARNING: Issues Found (N):
[ranked by token savings]

Top 3 Optimizations:
1. [action] → save ~X,XXX tokens
2. [action] → save ~X,XXX tokens
3. [action] → save ~X,XXX tokens

Potential savings: ~XX,XXX tokens (XX% of current overhead)

In verbose mode, additionally output per-file token counts, line-by-line breakdown of the heaviest files, specific redundant lines between overlapping components, and MCP tool list with per-tool schema size estimates.

Examples

Basic audit

User: /context-budget
Skill: Scans setup → 16 agents (12,400 tokens), 28 skills (6,200), 87 MCP tools (43,500), 2 CLAUDE.md (1,200)
       Flags: 3 heavy agents, 14 MCP servers (3 CLI-replaceable)
       Top saving: remove 3 MCP servers → -27,500 tokens (47% overhead reduction)

Verbose mode

User: /context-budget --verbose
Skill: Full report + per-file breakdown showing planner.md (213 lines, 1,840 tokens),
       MCP tool list with per-tool sizes, duplicated rule lines side by side

Pre-expansion check

User: I want to add 5 more MCP servers, do I have room?
Skill: Current overhead 33% → adding 5 servers (~50 tools) would add ~25,000 tokens → pushes to 45% overhead
       Recommendation: remove 2 CLI-replaceable servers first to stay under 40%

Best Practices

  • Token estimation: use words × 1.3 for prose, chars / 4 for code-heavy files
  • MCP is the biggest lever: each tool schema costs ~500 tokens; a 30-tool server costs more than all your skills combined
  • Agent descriptions are loaded always: even if the agent is never invoked, its description field is present in every Task tool context
  • Verbose mode for debugging: use when you need to pinpoint the exact files driving overhead, not for regular audits
  • Audit after changes: run after adding any agent, skill, or MCP server to catch creep early

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

90.37%
按下载量换算889

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

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 lobster-context-budget 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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