---

name: context-window-management compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent context window management with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: context-window-management, context window management, how do i context-window-management, orchestrate context-window-management, automate context-window-management, agent context-window-management archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Context Window Management

Orchestrates intelligent skill selection and execution for context window management workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def manage_context_window(
    messages: List[Dict],
    max_tokens: int,
    tokenizer: Any,
    strategy: str = "sliding_window"
) -> Dict:
    """Manage context window by enforcing token limits and applying retention strategies.
    
    Implements Law 2 (Parse at boundary) by validating token counts immediately.
    Implements Law 3 (Atomic Predictability) by returning a new context state.
    """
    if not messages:
        raise ValueError("Context window cannot be empty")
        
    # Calculate current token usage at boundary
    current_tokens = sum(len(tokenizer.encode(msg.get("content", ""))) for msg in messages)
    
    if current_tokens <= max_tokens:
        return {"status": "within_limits", "tokens_used": current_tokens, "messages": messages}
        
    # Apply retention strategy based on configuration
    if strategy == "sliding_window":
        # Keep recent messages, drop oldest until within budget
        retained = []
        tokens_in_retained = 0
        for msg in reversed(messages):
            msg_tokens = len(tokenizer.encode(msg.get("content", "")))
            if tokens_in_retained + msg_tokens <= max_tokens:
                retained.append(msg)
                tokens_in_retained += msg_tokens
            else:
                break
        return {
            "status": "truncated",
            "strategy": strategy,
            "tokens_used": tokens_in_retained,
            "messages": list(reversed(retained))
        }
    elif strategy == "summarize_oldest":
        # Trigger summarization for oldest messages
        oldest_chunk = messages[:len(messages)//2]
        # In production, this would call an LLM summarization skill
        summary = _generate_context_summary(oldest_chunk)
        return {
            "status": "summarized",
            "strategy": strategy,
            "tokens_used": len(tokenizer.encode(summary)) + sum(len(tokenizer.encode(m.get("content", ""))) for m in messages[len(messages)//2:]),
            "messages": [{"role": "system", "content": f"[Context Summary]\n{summary}"}] + messages[len(messages)//2:]
        }
    else:
        raise ValueError(f"Unsupported strategy: {strategy}")

Pattern 2: Execution with Fallback

def handle_context_overflow(
    current_context: Dict,
    fallback_strategies: List[str],
    confidence_threshold: float = 0.8
) -> Dict:
    """Route context management when overflow occurs, applying fallback chains.
    
    Implements Law 4 (Fail Fast) by immediately halting on invalid overflow states.
    Implements adaptive routing based on historical success rates.
    """
    if not current_context.get("messages"):
        raise ValueError("Cannot route empty context")
        
    overflow_tokens = current_context.get("tokens_used", 0) - current_context.get("max_tokens", 4096)
    if overflow_tokens <= 0:
        return {"action": "none", "reason": "within_limits"}
        
    # Evaluate fallback strategies by historical success rate
    best_strategy = None
    best_confidence = 0.0
    
    for strategy in fallback_strategies:
        # Simulate confidence scoring based on past performance
        confidence = _get_strategy_confidence(strategy, overflow_tokens)
        if confidence > best_confidence and confidence >= confidence_threshold:
            best_confidence = confidence
            best_strategy = strategy
            
    if not best_strategy:
        # Fail loud - escalate to human or strict truncation
        return {
            "action": "escalate",
            "reason": "no_confident_fallback",
            "overflow_tokens": overflow_tokens,
            "fallback_tried": fallback_strategies
        }
        
    # Execute selected fallback strategy
    if best_strategy == "compress_metadata":
        return _apply_metadata_compression(current_context)
    elif best_strategy == "split_task":
        return _split_context_into_subtasks(current_context)
    else:
        return _apply_default_truncation(current_context)

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• LLM Context Window Optimization Guide
• Tokenization Algorithms (BPE, WordPiece)
• Retrieval-Augmented Generation (RAG) Paper (Facebook AI)
• KV Cache Optimization in LLMs
• Sliding Window Attention Mechanism (Wikipedia)

Related Skills

| Skill | Purpose | |