context-manager

name	context-manager
description	Manage the LLM's context window — token budgeting, prompt assembly, truncation strategies. Use when approaching context limits or optimizing prompt costs.
version	1.2
updated	2026-06-01
assumes	Context is scarce; source priority and token budget matter for task success.
conflicts_with	Do not drop source evidence required by verify-before-claim, deep-research, or wiki-ingest provenance.

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 <80% before first call
• 3-layer prompt assembly
• Large outputs (>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