token-budget
Estimates and tracks token consumption to prevent context overflow errors during LLM operations.
Install
mkdir -p .claude/skills/token-budget && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1401" && unzip -o skill.zip -d .claude/skills/token-budget && rm skill.zipInstalls to .claude/skills/token-budget
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Manages token budget estimation and tracking to prevent context overflowKey capabilities
- →Estimate token usage based on content type
- →Track cumulative context consumption
- →Categorize files by size for loading strategy
- →Trigger alerts at budget thresholds
- →Recommend compression and summarization
How it works
It applies heuristic rules to estimate token counts based on line numbers and content types, then monitors these against defined percentage thresholds.
Inputs & outputs
When to use token-budget
- →Estimate tokens for prompt engineering
- →Monitor context usage in long tasks
- →Prevent model context window overflow
About this skill
Token Budget Skill
<role> You are a token-efficient agent. Your job is to maximize output quality while minimizing token consumption.Core principle: Every token counts. Load only what you need, when you need it. </role>
Token Estimation
Quick Estimates
| Content Type | Tokens/Line | Notes |
|---|---|---|
| Code | ~4-6 | Depends on verbosity |
| Markdown | ~3-4 | Less dense than code |
| JSON/YAML | ~5-7 | Structured, repetitive |
| Comments | ~3-4 | Natural language |
Rule of thumb: tokens ≈ lines × 4
File Size Categories
| Category | Lines | Est. Tokens | Action |
|---|---|---|---|
| Small | <50 | <200 | Load freely |
| Medium | 50-200 | 200-800 | Consider outline first |
| Large | 200-500 | 800-2000 | Use search + snippets |
| Huge | 500+ | 2000+ | Never load fully |
Budget Thresholds
Based on PROJECT_RULES.md context quality thresholds:
| Usage | Quality | Budget Status |
|---|---|---|
| 0-30% | PEAK | ✅ Proceed freely |
| 30-50% | GOOD | ⚠️ Be selective |
| 50-70% | DEGRADING | 🔶 Compress & summarize |
| 70%+ | POOR | 🛑 State dump required |
Budget Tracking Protocol
Before Each Task
-
Estimate current usage:
- Count files in context
- Estimate tokens per file
- Calculate approximate %
-
Check budget status:
Current: ~X,000 tokens (~Y%) Budget: [PEAK|GOOD|DEGRADING|POOR] -
Adjust strategy:
- PEAK: Proceed normally
- GOOD: Prefer search-first
- DEGRADING: Use outlines only
- POOR: Trigger state dump
During Execution
Track cumulative context:
## Token Tracker
| Phase | Files Loaded | Est. Tokens | Cumulative |
|-------|--------------|-------------|------------|
| Start | 0 | 0 | 0 |
| Task 1 | 2 | ~400 | ~400 |
| Task 2 | 3 | ~600 | ~1000 |
Optimization Strategies
1. Progressive Loading
Level 1: Outline only (function signatures)
Level 2: + Key functions (based on task)
Level 3: + Related code (if needed)
Level 4: Full file (only if essential)
2. Just-In-Time Loading
- Load file only when task requires it
- Unload mentally after task complete
- Don't preload "just in case"
3. Search Before Load
Always use context-fetch skill first:
- Search for relevant terms
- Identify candidate files
- Load only needed sections
4. Summarize & Compress
After understanding a file:
- Document key insights in STATE.md
- Reference summary instead of re-reading
- Use "I've analyzed X, it does Y" pattern
Budget Alerts
At 50% Budget
⚠️ TOKEN BUDGET: 50%
Switching to efficiency mode:
- Outlines only for new files
- Summarizing instead of loading
- Recommending compression
At 70% Budget
🛑 TOKEN BUDGET: 70%
Quality degradation likely. Recommend:
1. Create state snapshot
2. Run /pause
3. Continue in fresh session
Integration
This skill integrates with:
context-fetch— Search before loadingcontext-health-monitor— Quality trackingcontext-compressor— Compression strategies/pauseand/resume— Session handoff
Anti-Patterns
❌ Loading files "for context" — Search first ❌ Re-reading same file — Summarize once ❌ Full file when snippet suffices — Target load ❌ Ignoring budget warnings — Quality will degrade
Part of GSD v1.6 Token Optimization. See PROJECT_RULES.md for efficiency rules.
When not to use it
- →When working in environments without context limits
Limitations
- →Estimates are heuristic and not exact token counts
- →Relies on manual tracking during execution
- →Effectiveness depends on adherence to loading strategies
How it compares
It provides a structured protocol for proactive context management rather than reacting only after hitting a hard limit.
Compared to similar skills
token-budget side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| token-budget (this skill) | 4 | 4mo | No flags | Intermediate |
| sequential-thinking | 136 | 9mo | No flags | Intermediate |
| ai-wrapper-product | 5 | 6mo | No flags | Intermediate |
| initializing-memory | 2 | 2mo | Review | Beginner |
Try saying
Example prompts that trigger this skill in your AI assistant.
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