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affaan-m/context-budget

affaan-m

context-budget

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

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origin:ECC
New~1.3k
v1.2Saved Jul 14, 2026

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    │
└─────────────────┴────────┴───────────┘

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
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Overall Score

82/100

Grade

B

Good

Safety

85

Quality

80

Clarity

85

Completeness

78

Summary

This skill audits Claude Code context window consumption across agents, skills, MCP servers, and rules by scanning component directories, estimating token overhead, classifying components by necessity, and producing prioritized optimization recommendations. It helps developers identify bloat and reclaim context space through actionable guidance.

Detected Capabilities

file read (agents, skills, rules, MCP config)token estimation (prose and code)pattern detection (bloat, redundancy, overlap)report generation (formatted output with tables)frontmatter parsing (YAML metadata)

Trigger Keywords

Phrases that MCP clients use to match this skill to user intent.

context window audittoken overhead analysisMCP server optimizationagent bloat detectionreclaim context spacecomponent inventoryperformance degradation diagnosiscontext budget planning

Use Cases

  • Diagnose why session performance is degrading
  • Identify token overhead before adding new components
  • Discover redundant or unnecessary MCP servers and skills
  • Prioritize which agents or rules consume the most context
  • Plan context-aware architecture for multi-agent setups
  • Understand breakdown of context consumption across component types

Quality Notes

  • Excellent scope definition: clearly covers agents, skills, rules, MCP servers, and CLAUDE.md with specific thresholds (200 lines for agents, 400 for skills, 100 for rules, 20 tools for MCP)
  • Well-structured 4-phase process (Inventory, Classify, Detect, Report) that makes implementation clear
  • Practical classification buckets (Always needed, Sometimes needed, Rarely needed) help non-technical users decide what to optimize
  • Token estimation guidance (words × 1.3 for prose, chars / 4 for code) is concrete and reproducible
  • Verbose mode option acknowledges need for deeper debugging without overwhelming basic use case
  • Clear examples show different invocation scenarios and expected outputs
  • Best practices section educates users on what drives token consumption (MCP is biggest lever, agent descriptions loaded always)
  • Potential weakness: does not specify how to handle nested skill directories or symlinks
  • Could benefit from error handling guidance (e.g., what if a SKILL.md is malformed YAML, or .mcp.json is missing)
Model: claude-haiku-4-5-20251001Analyzed: Jul 14, 2026

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Version History

v1.2

Content updated

2026-07-14

Latest
v1.1

Content updated

2026-04-20

v1.0

No changelog

2026-04-12

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