Context Budget Optimizer

Framework: The Token Efficiency Matrix
Worth $200/hr consultant time. Yours for $19.

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What This Skill Does

Audits your agent's token usage across every context layer, identifies where you're burning budget on bloat, and produces a 3-week cost reduction roadmap with concrete implementation steps.

Problem it solves: Power users hitting $200-500/month in AI costs often have 60-70% waste baked into their context. Most of it is invisible: stale files in system prompts, redundant skill loading, oversized memory files, wrong model choices. The Token Efficiency Matrix makes the waste visible and rankable.

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The Token Efficiency Matrix

A 4-quadrant audit tool that scores every context element by cost (token weight) and ROI (value delivered per token). High cost + low ROI = cut first.

The Matrix

CODEBLOCK0

Action by quadrant:

• - KEEP: Don't touch. It's working efficiently.
• OPTIMIZE: Compress or lazy-load. Value is there, just expensive.
• AUDIT: Review quarterly. Low cost so not urgent, but ROI should be questioned.
• CUT: Kill immediately. You're paying for nothing.

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Phase 1: Context Inventory

Before scoring, map everything that's in your agent's context.

Context Layers to Audit

CODEBLOCK1

Inventory Template

For each item in your context, fill this in:

Item	Layer	Est. Tokens	Sessions/Day	Daily Cost*	Value (1-5)
SOUL.md	A
MEMORY.md

C | | | | |
| [Skill 1].md | B | | | | |
| [Skill 2].md | B | | | | |
| Daily notes | C | | | | |
| [Project file] | D | | | | |

*Daily Cost = (Est. Tokens / 1M) × modelrate × sessionsper_day

Token estimation cheatsheet:

• - 1 page of text ≈ 500-700 tokens
• 1 SKILL.md file ≈ 800-2,000 tokens
• 1 code file (100 lines) ≈ 1,200-1,800 tokens
• 1 MEMORY.md (well-maintained) ≈ 500-1,500 tokens
• 1 MEMORY.md (neglected/bloated) ≈ 3,000-8,000 tokens

Model rates (as of Q1 2026, approximate):

Model	Input Cost per 1M tokens
Claude Haiku 3.5	~$0.80
Claude Sonnet 4

~$3.00 |
| Claude Opus 4 | ~$15.00 |
| GPT-4o mini | ~$0.15 |
| GPT-4o | ~$2.50 |

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Phase 2: Scoring (Token Efficiency Matrix)

Score each context item:

Cost Score (1-5):

Score	Token Range	Description
1	< 200 tokens	Tiny — negligible
2

200-500 tokens | Light |
| 3 | 500-1,500 tokens | Medium |
| 4 | 1,500-4,000 tokens | Heavy |
| 5 | > 4,000 tokens | Very heavy |

ROI Score (1-5):

Score	Description
1	Rarely used, generic, stale
2

Occasionally useful |
| 3 | Moderately useful most sessions |
| 4 | Consistently referenced, shapes output |
| 5 | Critical — session breaks without it |

Matrix placement:

• - Cost 1-2, ROI 4-5 → KEEP
• Cost 4-5, ROI 4-5 → OPTIMIZE
• Cost 1-2, ROI 1-2 → AUDIT
• Cost 4-5, ROI 1-2 → CUT
• Cost 3, ROI 3 → AUDIT (marginal — evaluate quarterly)

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Phase 3: Reduction Playbook

CUT (implement immediately)

Items to eliminate first:
CODEBLOCK2

Cut target: 30-40% token reduction with zero quality loss.

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OPTIMIZE (implement week 1-2)

Tactic 1: Lazy Loading

Instead of loading all skills at startup, load only when triggered.

Before (eager load):
CODEBLOCK3

After (lazy load):
CODEBLOCK4

Lazy load implementation:

# SKILL-INDEX.md (500 tokens instead of full skills)

Available skills — load when needed:
- mcp-server-setup-kit: MCP connection setup
- agentic-loop-designer: Build autonomous loops  
- context-budget-optimizer: Token cost reduction
- [etc]

To use a skill: "Use the [skill-name] skill"

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Tactic 2: Memory Tiering

Not all memory is equally important. Tier it.

CODEBLOCK6

Memory tiering implementation:

1. 1. Create FOCUS.md (Tier 1) — just this week's priorities
2. Archive daily notes older than 14 days to INLINECODE1
3. Summarize MEMORY.md quarterly (remove resolved items)
4. Set system prompt to only inject FOCUS.md + recent 7 days of memory

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Tactic 3: Compression Templates

Replace verbose content with compressed references.

Before (bloated system prompt section):
CODEBLOCK7

After (compressed):

Owner: David Flynn | Austin TX | TechCorp (B2B SaaS, logistics, mid-market)
Background: 8yr founder, ex-McKinsey | Team: 6
Style: Direct, metric-first, no fluff
[40 tokens — 87% reduction]

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Tactic 4: Model Downgrade Opportunities

Most context-heavy sessions don't need the flagship model.

Downgrade decision tree:
CODEBLOCK9

Model savings calculator:

Switch	Token Cost Reduction	When Safe
Opus → Sonnet	80%	Most writing, analysis, ops
Sonnet → Haiku

75% | Simple reads, status checks, formatting |
| Opus → Haiku | 95% | Very simple tasks only |

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Tactic 5: Context Window Management

Stop re-injecting the same content in long sessions.

CODEBLOCK10

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3-Week Cost Reduction Roadmap

Week 1: Cut & Quick Wins

Target: 30-40% cost reduction

CODEBLOCK11

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Week 2: Optimize Structure

Target: Additional 20-30% reduction

CODEBLOCK12

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Week 3: Lock In & Monitor

Target: Establish monitoring + reach 50%+ total reduction

CODEBLOCK13

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Token Efficiency Scoring Rubric

After completing the 3-week roadmap, score your setup:

Metric	0	1	2
Average session tokens	> 50K	20-50K	< 20K
Skills lazy-loaded

None | Some | All |
| Memory tiered correctly | No | Partially | Yes |
| Model routing applied | No | Ad hoc | Systematic |
| Context reviewed quarterly | No | Annually | Quarterly |

Score 8-10: Token-efficient operator. You're in the top 5% of AI users by cost.
Score 5-7: Good progress. Keep tightening.
Score 0-4: High burn rate. Revisit Week 1 of the roadmap.

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Quick Reference: The 10 Highest-ROI Cuts

If you do nothing else, do these 10 things:

1. 1. Archive memory older than 30 days
2. Switch routine tasks from Opus/Sonnet to Haiku
3. Lazy-load skills instead of always-on
4. Compress system prompt (verbose → structured)
5. Stop re-reading files in the same session
6. Archive daily notes older than 14 days
7. Create FOCUS.md and limit startup context to it
8. Remove code files from context when not actively editing
9. Summarize MCP tool outputs instead of keeping raw results
10. Set model routing rules in AGENTS.md

Combined impact: 50-70% cost reduction for most users.

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Example Session

User prompt:

"My Claude usage is $400/month and I don't know why. Help me cut it."

Agent response using this skill:

1. 1. Runs Phase 1 Context Inventory (asks user to share what's in their setup)
2. Estimates tokens per item using the cheatsheet
3. Populates the Token Efficiency Matrix
4. Identifies top 3 CUT items (likely: bloated MEMORY.md, eager skill loading, Opus overuse)
5. Delivers Week 1 roadmap customized to their setup
6. Projects: "Based on this, you should reach $150-200/month in 3 weeks"

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Bundle Note

This skill is part of the AI Setup & Productivity Pack ($79 bundle):

• - MCP Server Setup Kit ($19)
• Agentic Loop Designer ($29)
• AI OS Blueprint ($39)
• Context Budget Optimizer ($19) — you are here
• Non-Technical Agent Quickstart ($9)

Save $36 with the full bundle. Built by @RemyClaw.