context-budget
Skill0 starsChanged 4 months agoMITView on GitHub
Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.
We split this skill into the rules it gives an agent
4 rules. 0 are common, 2 are distinctive, and 2 are very specific to this project.
0
Common
Most of the 42 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 34
Check for duplicate copies in .agents/skills/ — skip identical copies to avoid double-counting
- Very specific: line 132
Token estimation: use words × 1.3 for prose, chars / 4 for code-heavy files
- Distinctive: line 135
Verbose mode for debugging: use when you need to pinpoint the exact files driving overhead, not for regular audits
- Distinctive: line 136
Audit after changes: run after adding any agent, skill, or MCP server to catch creep early
“Common” is measured: kept by at least 6 in 10 of the 42 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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-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 sidePre-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
Other versions49 projects use a version of this.
Word for word · 3
- RobSpct/agentic-os-rs-community-kit★ 2100% same
- SkotAlexsander/skills★ 1100% same
- Lucioway/way-stack★ 0100% same
Edited · 17
- misskey-dev/misskey★ 11k13% same
- loulanyue/awesome-claude-notes★ 27299% same
- Fmarzochi/EGC★ 6191% same
- woxiangyangzhimao/skills★ 1496% same
- gongyijie85/dsh-ecc★ 896% same
- VoVuongThanhDat/agentflow★ 899% same
- airshell/ai-optimizer★ 233% same
- get-proofpilot/claude-skills-catalog★ 199% same
- NISTALTALSON/essential-claude-skills★ 196% same
- SamuelNDCE/claude-super-skill-library★ 199% same
7 more not listed
Wrote their own version · 29
- poshan0126/dotclaude★ 871same name
- OneWave-AI/claude-skills★ 378same name
- felvieira/claude-skills-fv★ 23same name
- tellmefrankie/ai-investment-skills★ 14same name
- TheyanzXD/opencode-telegram-gateway★ 9same name
- mxrsv/agents-skills★ 8same name
- Fadi88/one-man-startup★ 7same name
- Univers42/claude-deal-with-the-devil★ 4same name
- Univers42/harness42★ 4same name
- hahuyhungdev/ai-coding-config★ 3same name
19 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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Does it work?
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Your trail
Files you open will collect here, so you can find them again.
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Now do this for your own.
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