Token Budget Guard
Stop burning context. Manage your agent's token budget intelligently.
The Problem
AI agents waste 40-60% of tokens on:
- Repeatedly loading full schemas when summaries suffice
- Including irrelevant context from previous turns
- Not compressing before context window fills
- Loading entire files when snippets would do
The AAI Gateway showed 99% token savings are possible. This skill makes token budgeting automatic.
When to Use
- "token budget", "reduce tokens", "context too long", "running out of context"
- Before multi-tool workflows
- When hitting context limits
- Optimizing agent workflows for cost efficiency
Core Principles
1. Progressive Disclosure
Level 0: Name only (1-5 tokens) — "browser tool available"
Level 1: Summary (10-30 tokens) — "browser: open/navigate/snapshot web pages"
Level 2: Schema (50-200 tokens) — full parameter descriptions
Level 3: Examples (200-500 tokens) — sample calls with output
Default: Level 1. Escalate only when tool is being used.2. Summarize Before Including
- Previous conversation: summarize, don't replay
- File contents: extract relevant sections, don't cat entire files
- Tool outputs: compress to decisions + evidence, drop raw data
- Error logs: extract error line + 5 lines context, not full stack
3. Budget Allocation
Total context budget: 100%
├── System prompt: 15-20% (fixed)
├── Active task: 40-50% (working space)
├── Tool schemas: 10-15% (progressive)
├── Memory/History: 10-15% (summarized)
└── Reserve: 5-10% (safety margin)4. Compression Triggers
- When context > 60% full → start compressing history
- When context > 80% full → aggressive summarization
- When context > 90% full → emergency mode (drop all but current task)
Token Saving Strategies
Strategy 1: Schema Stubs
// Instead of full schema (200+ tokens):
// { "name": "web_search", "parameters": { "query": { "type": "string", ... }, ... } }
// Use stub (15 tokens):
// web_search(query) → search resultsStrategy 2: Conversation Compression
// Before compression (500 tokens of back-and-forth):
User: Can you find the latest Node.js version?
Agent: I'll search for that. [calls web_search]
Agent: The latest Node.js version is v22.22.2...
User: What about LTS?
Agent: [calls web_search] The current LTS is v22.x...
// After compression (30 tokens):
// Resolved: Node.js latest=v22.22.2, LTS=v22.x, user confirmed.Strategy 3: Selective File Reading
# Instead of: cat package.json (often 100+ lines)
# Use: jq '.dependencies | keys' package.json (just what you need)
# Or: head -5 package.json (name + version)Strategy 4: Tool Result Filtering
// Instead of returning full API response (2000 tokens)
// Return structured summary (50 tokens):
// ✅ 3 issues found: 2 bugs (P1, P2), 1 feature request
// Key assignees: @alice, @bob
// No urgent itemsBudget Monitoring
Track token usage per task:
### Token Budget Log — Task: "Build API endpoint"
| Action | Tokens | Running Total | Budget % |
|--------|--------|--------------|----------|
| System prompt | 2,000 | 2,000 | 10% |
| Tool schemas (stub) | 500 | 2,500 | 12.5% |
| Read 3 files (selective) | 1,200 | 3,700 | 18.5% |
| Write code | 800 | 4,500 | 22.5% |
| ... | ... | ... | ... |Quick Wins (Apply Immediately)
- Replace full file reads with targeted extraction —
grep,jq,awk>cat - Use tool stubs during planning — load full schemas only at execution time
- Summarize after every 5 tool calls — don't let raw output accumulate
- Set a hard limit — if a single file > 500 lines, read with offset/limit
- Drop completed subtask context — keep decision, drop process
Integration with Agent Workflows
Task received → Estimate token need → Allocate budget → Execute with monitoring
↓
Budget > 80%? → Compress
↓
Budget > 90%? → Emergency summarizeReal Impact
Based on AAI Gateway benchmarks:
- Multi-MCP workflows: 99% reduction in schema tokens
- Conversation history: 60-80% compressible
- File operations: 40-70% savings with selective reading
- Overall context efficiency: 3-5x improvement typical
License
MIT