Best for
- 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
affaan-m/ECC/skills/context-budget/SKILL.md
Audits Claude Code context window consumption across agents, skills, MCP servers, and rules. Identifies bloat, redundant components, and produces prioritized token-savings recommendations.
Decision brief
Analyze token overhead across every loaded component in a Claude Code session and surface actionable optimizations to reclaim context space.
Compatibility matrix
| Platform | Status | Evidence | What to check |
|---|---|---|---|
| Codex | Not declared | No explicit evidence | Portability before use |
| Claude Code | Declared | Source record | Install path and trigger |
| Cursor | Not declared | No explicit evidence | Portability before use |
| Gemini CLI | Not declared | No explicit evidence | Portability before use |
Installation
The source command is displayed only when detected. A safe inspection prompt is always available so your agent can explain every action before execution.
npx skills add https://github.com/affaan-m/ECC --skill "skills/context-budget"Inspect the Agent Skill "context-budget" from https://github.com/affaan-m/ECC/blob/4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38/skills/context-budget/SKILL.md at commit 4e973d3eaf92d97f8d2e2d8abb39d8bdc8711b38. List every install step, command, network request, credential, file read/write, external action, and rollback step. Explain whether it fits my task. Do not install or execute anything until I approve.
Workflow
Scan all component directories and estimate token consumption:
Sort every component into a bucket:
Identify the following problem patterns:
Produce the context budget report:
Session performance feels sluggish or output quality is degrading
Permission review
No configured static risk pattern was detected
This is not proof of safety. Runtime behavior, indirect dependencies, and hidden external systems are outside the static scan.
Evidence record
| Signal | Value | Evidence type | Meaning |
|---|---|---|---|
| Quality score | 84/100 | Computed | Documentation, specificity, maintenance, and trust rules |
| Repository stars | 234,327 | Source | Repository attention, not individual Skill quality |
| Compatibility | 1 platforms | Source | Declared in the catalog source record |
| Usage guide | automated source guide | Editorial | Generated or reviewed according to the visible evidence level |
Pinned source
Analyze token overhead across every loaded component in a Claude Code session and surface actionable optimizations to reclaim context space.
/context-budget command (this skill backs it)Scan all component directories and estimate token consumption:
Agents (agents/*.md)
description frontmatter lengthSkills (skills/*/SKILL.md)
.agents/skills/ — skip identical copies to avoid double-countingRules (rules/**/*.md)
MCP Servers (.mcp.json or active MCP config)
gh, git, npm, supabase, vercel)CLAUDE.md (project + user-level)
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 |
Identify the following problem patterns:
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.
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%
words × 1.3 for prose, chars / 4 for code-heavy filesAlternatives
affaan-m/ECC
Use it for engineering tasks; the detail page covers purpose, installation, and practical steps.
affaan-m/ECC
Use it for engineering tasks; the detail page covers purpose, installation, and practical steps.
jackchuka/skills
Audit skill SKILL.md files for compliance with the agentskills.io specification and house conventions. Checks frontmatter fields (name, description, compatibility, metadata, argument-hint), metadata sub-fields (author, scope, layer, confirms), and layer/suffix consistency. Use when adding new skills, reviewing skill quality, or ensuring all skills follow the spec. Triggers: "audit skills", "check skill spec", "skill compliance", "are my skills up to spec", "/claude-skill-spec-audit".
affaan-m/ECC
Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.