context-window-management

# 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 ```python 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 ```python 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 --- --- ## 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](<https://www.promptingguide.ai/techniques/contextwindow>) - [Tokenization Algorithms (BPE, WordPiece)](<https://arxiv.org/abs/1508.07909>) - [Retrieval-Augmented Generation (RAG) Paper (Facebook AI)](<https://arxiv.org/abs/2005.11401>) - [KV Cache Optimization in LLMs](<https://huggingface.co/docs/transformers/perf_infer_gpu_one#kv-cache>) - [Sliding Window Attention Mechanism (Wikipedia)](<https://en.wikipedia.org/wiki/Transformer_(deep_learning)#Scaled_dot-product_attention>) ## Related Skills | Skill | Purpose | |