Skill detail

context-budget

Audits Claude Code context window consumption across agents, skills, MCP servers, and rules, identifying bloat and producing prioritized token-savings recommendations.

Why we recommend it: Audits context window consumption and produces token-savings recommendations — directly on-topic.

MatchDirectRecommended for Token Optimization
Sourceaffaan-m/​eccExternal source
Reported installs9,165Popularity signal only

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SKILL.md

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---
name: 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. Use when the context window is filling up too fast and the agents, skills, MCP servers, or rules consuming it need to be identified.
metadata:
  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:

| 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 li
Read the full source on GitHub (opens external page)
Context

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