Context Budget
SkillAI & modelsFind out what is filling up your AI's context window (its working memory) and get a prioritized plan to free it. This skill audits how much space your loaded agents, skills, and rules consume, spots bloat and redundant components, and produces token-savings recommendations ranked by priority. It is meant for when your context window fills up too fast.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then ask your AI to run a context audit when you notice its window filling up. You will get a breakdown of what is consuming the most space and which savings to act on first.
Then ask your AI: use the Context Budget skill
What your AI can do with it
- Measure how much context window each loaded agent, skill, and rule consumes
- Spot bloat and redundant components that waste space
- Get token-savings recommendations ranked by priority
- See what to trim first when your context window fills up too fast
What this skill tells your AI
The instructions your AI receives, as published by affaan-m/ecc in skills/context-budget/SKILL.md and read by ahel’s review.
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-budgetcommand (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
descriptionfrontmatter 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 × 1.3for prose,chars / 4for 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
Signals
- GitHub stars
- 258k
- Forks
- 39k
- Last commit
- Sep 2026
Others that do the same job
Advanced
- Catalog kind
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- Gateway key
context-budget- Source
- github.com/affaan-m/ecc