C

context-budget

by @affaan-mv
4.4(20)

分析 Claude Code 会话中各组件的 token 开销,优化上下文使用和预算分配

contextbudgetaillmagentsGitHub
安装方式
npx skills add https://github.com/affaan-m/everything-claude-code --skill context-budget
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Before / After 效果对比

1
使用前

在没有 `context-budget` 工具时,当 Claude Code 会话性能下降或输出质量变差时,开发者通常需要手动排查。这包括逐一检查代理、技能、规则和 CLAUDE.md 文件,估算其行数、描述长度和潜在的重复内容。对于 MCP 服务器,需要手动统计工具数量并评估其必要性。整个过程耗时且容易遗漏,导致上下文空间被不必要的组件占用,限制了会话的效率和可扩展性,难以准确评估实际可用的上下文余量。

使用后

使用 `context-budget` 后,只需运行 `/context-budget` 命令,即可获得会话中所有组件(代理、技能、规则、MCP 服务器、CLAUDE.md)的详细 token 开销分析。它会自动识别并标记出臃肿的代理描述、过重的代理文件、冗余的组件、MCP 过度订阅以及 CLAUDE.md 冗余内容。系统还会将组件分类为“始终需要”、“有时需要”或“很少需要”,并提供可操作的优化建议,帮助开发者快速回收上下文空间,提升会话性能,并清晰了解上下文余量。

SKILL.md

context-budget

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:

Bucket Criteria Action

Always needed Referenced in CLAUDE.md, backs an active command, or matches current project type Keep

Sometimes needed Domain-specific (e.g. language patterns), not referenced in CLAUDE.md Consider on-demand activation

Rarely needed No command reference, overlapping content, or no obvious project match Remove 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    │
└─────────────────┴────────┴───────────┘

⚠ 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

Weekly Installs265Repositoryaffaan-m/everyt…ude-codeGitHub Stars105.0KFirst Seen5 days agoSecurity AuditsGen Agent Trust HubPassSocketPassSnykPassInstalled oncodex255cursor229opencode227gemini-cli227github-copilot227amp227

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统计数据

安装量4.7K
评分4.4 / 5.0
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更新日期2026年7月17日
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4
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🔧Claude Code

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创建2026年3月26日
最后更新2026年7月17日
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