Context Manager

Context Manager

Manage the scarcest resource in the LLM OS: context window (RAM).

Usage Template

Prompt

Use context-manager for this task. Estimate token budget, choose what to keep, what to summarize, and what to drop.

Use Case

• Preparing a long task, large document set, or multi-step agent workflow without overrunning context.

Expected Result

• The agent produces a context budget, priority order, truncation plan, and cost-aware prompt assembly.

Output Example

• A table of keep/summarize/drop decisions with estimated tokens and retrieval priority.

Verification Case

• The final context plan names included sources, excluded sources, and the reason for each exclusion.

Verified Effect

• Long or messy context becomes a scoped prompt plan with lower token waste and clearer retrieval priorities.

Success Metrics

• Output includes an estimated token budget and a keep/summarize/drop table.
• Each excluded source has a reason, and critical context has a retrieval path.
• Final prompt plan fits the target context window with margin.

When to Use

• "Context is full", "too many tokens", "slow responses"
• Before complex multi-step tasks
• Designing prompt templates with variable-length content
• "Optimize my prompts" or "reduce token cost"
• Before ingesting large documents

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Core Principles

0. Concrete Ideas Buy Speed (Andrew Ng)

> "When you're vague, you're almost always right. When you're concrete, you may be right or wrong. Either way is fine — we can discover that much more fast."

| Vague | Concrete | Savings | |-------|----------|:-------:| | "Analyze this document" | "Extract 3 key metrics from earnings report, compare to last quarter" | ~60% | | "Help with this code" | "Fix TypeScript error line 42: 'Property id does not exist on type User'" | ~70% | | "Research this topic" | "Find 5 sources about Claude Code auto-mode security, focus on classifier" | ~50% |

1. Token Budgeting

Context Window = System Prompt + User Input + Retrieved Context + Tool Results + Agent Thoughts

Budget:
├─ System Prompt + Schema     10-15%
├─ User Request                 5-10%
├─ Retrieved Context (RAG)    30-40%
├─ Tool Results                20-30%
└─ Agent Reasoning             10-15%

Rule: Exceeds 80% → must truncate/compress.

2. Prompt Assembly

Layer 1: Immutable Core (always injected)
├─ System prompt (persona + constraints)
├─ Schema/ontology
└─ Safety rules

Layer 2: Task Context (per-request)
├─ User request
├─ Relevant wiki pages (top-k)
└─ Recent session log

Layer 3: Ephemeral (auto-managed)
├─ Tool call history (trim old)
├─ Large outputs (summarize)
└─ Extended thinking (compact)

3. Truncation Strategies

| Strategy | When | How | Saved | |----------|------|-----|:-----:| | Summarize | Output >500 tokens | summarize() | ~70% | | Trim oldest | Loop >10 turns | Remove earliest | ~40% | | Deduplicate | Repeated content | Keep one copy | ~20% | | Drop results | Action complete | Keep calls + errors only | ~50% | | Compact CoT | Thinking used | 1-sentence conclusion | ~80% | | Cache prefix | Repeated prompt | Identical prefix first | ~90% latency |

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Token Cost (Claude)

| Model | Input/MTok | Output/MTok | Window | |-------|:----------:|:-----------:|:------:| | Opus 4.6 | $15.00 | $75.00 | 200K | | Sonnet 4.6 | $3.00 | $15.00 | 200K | | Haiku 3.5 | $0.80 | $4.00 | 200K |

Quick estimates:

• 1 token ≈ 0.75 word
• 1 page ≈ 300-500 tokens
• 1 source ≈ 2000-8000 tokens
• 1 hour agentic ≈ 100K-500K tokens
• Weekly active user ≈ $9-15

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Decision Tree

Simple task (1-2 steps)?
├─ YES → Haiku, ~10K tokens
└─ NO → Analytical (research/compile)?
    ├─ YES → Opus, ~50-100K
    └─ NO → Procedural (ingest/lint)?
        ├─ YES → Sonnet, ~20-50K
        └─ NO → Sonnet default

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Long-Horizon Compaction Contract

For long tasks, compaction is not just shorter text. The summary must preserve the state needed to continue without drift:

Goal:
Current definition of done:
User constraints and denied actions:
Key files, sources, or artifacts:
Completed steps:
Failed paths and why:
Verification evidence:
Open risks:
Next action:

Use compaction before context pressure becomes an emergency. After resuming from a compacted state, run one quick continuity check: confirm the goal, constraints, current step, and evidence before taking the next action.

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Tokenmaxxing vs Efficiency

> "Token maxing is actually the coolest thing you can do now." — Gary Tan

| Strategy | When | Approach | |----------|------|----------| | Tokenmaxxing | Research, deep analysis | 20 sources, cross-reference everything | | Efficiency | Production, cost-sensitive | Concrete prompts, caching, truncation |

Thin Harness, Fat Skills:

• Simple tasks → thin prompt
• Complex tasks → fat prompt with examples

When to Tokenmax

| Scenario | Approach | Quality Gain | |----------|----------|:------------:| | Research synthesis | 20 sources, cross-reference | 3-5x | | Code review | Full repo analysis | 2-3x fewer bugs | | Creative writing | Multiple drafts, self-critique | Significantly better |

When to Be Efficient

| Scenario | Approach | Savings | |----------|----------|:-------:| | Routine linting | Haiku, minimal context | ~80% | | Simple edits | Concrete prompt, no CoT | ~60% | | Status checks | One-liner | ~90% |

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Quality Gates

• [ ] Token budget 500 tokens) summarized
• [ ] Long-horizon compactions preserve goal, constraints, evidence, risks, and next action
• [ ] Cost estimated for >100K token tasks
• [ ] Cache-friendly ordering
• [ ] Context utilization logged

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Mark393295827
• Source: Mark393295827/third-brain-v5-skills
• License: MIT
• Homepage: https://github.com/Mark393295827/third-brain-v5-skills/tree/master

Install and usage instructions live in the source repository linked above.