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skills/ck-context-engineering/SKILL.md

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+---
+name: ck-context-engineering
+description: >
+  Optimizes prompts, context windows, and AI interaction patterns for better LLM outputs.
+  Activate when user says 'improve this prompt', 'optimize context', 'prompt engineering',
+  'getting bad AI responses', 'structure prompt better', or 'reduce token usage'.
+  Accepts existing prompts, system instructions, and desired output descriptions.
+---
+
+## Overview
+Applies prompt engineering and context management techniques to improve LLM response quality, reduce hallucinations, and optimize token efficiency across AI-powered workflows.
+
+## When to Use
+- Existing prompts produce inconsistent or low-quality outputs
+- System prompts need restructuring for a new AI-powered feature
+- Reducing context window usage while maintaining output quality
+- Designing multi-turn conversation flows
+- Creating reusable prompt templates for a team or application
+
+## Don't Use When
+- The underlying model needs fine-tuning (prompting won't fix model capability gaps)
+- Issue is API/infrastructure, not prompt quality
+- Task needs real-time data the model cannot access (use RAG or tool calls instead)
+
+## Steps / Instructions
+
+### 1. Diagnose the Current Prompt
+
+Identify failure modes:
+- **Hallucination**: model invents facts → add grounding, cite sources
+- **Verbosity**: too much irrelevant output → add format constraints
+- **Missed requirements**: model ignores instructions → restructure, use numbered lists
+- **Inconsistency**: different results each run → lower temperature, add examples
+- **Context overflow**: long conversation loses earlier instructions → summarize / compress
+
+### 2. Apply Prompt Structure Principles
+
+**Clear role definition:**
+```
+You are a senior TypeScript developer specializing in Next.js.
+Your responses are concise, production-ready, and include error handling.
+```
+
+**Explicit output format:**
+```
+Respond ONLY with a JSON object matching this schema:
+{
+  "summary": string,       // 1-2 sentences
+  "steps": string[],       // ordered action items
+  "confidence": "high" | "medium" | "low"
+}
+Do not include explanatory prose outside the JSON.
+```
+
+**Constraints before the ask:**
+```
+Rules:
+- Do not use any third-party libraries
+- Prefer async/await over callbacks
+- Keep functions under 20 lines
+
+Task: Write a function that...
+```
+
+### 3. Few-Shot Examples
+
+Add 2–3 examples for tasks with specific output patterns:
+```
+Input: "user clicked buy"
+Output: { "event": "purchase_initiated", "category": "commerce" }
+
+Input: "page loaded"
+Output: { "event": "page_view", "category": "navigation" }
+
+Input: "{{USER_INPUT}}"
+Output:
+```
+
+### 4. Context Window Management
+
+**Reduce noise:**
+- Remove redundant instructions
+- Trim verbose conversation history
+- Summarize long documents before including
+
+**Chunking strategy for long content:**
+```
+For files > 4000 tokens:
+1. Extract only relevant sections
+2. Add chunk position metadata: "[Chunk 2/5 of file.ts, lines 120-240]"
+3. Request only what the model needs for the current step
+```
+
+**System vs user message split:**
+- System: persistent role, rules, output format
+- User: task-specific content, inputs, context
+- Keep system prompt stable; vary user message per request
+
+### 5. Chain-of-Thought for Complex Reasoning
+
+```
+Before answering, think step by step:
+1. Identify what is being asked
+2. List any constraints or edge cases
+3. Draft your approach
+4. Verify your answer against the constraints
+Then provide your final response.
+```
+
+For simpler tasks, suppress COT to save tokens:
+```
+Answer directly without showing your reasoning process.
+```
+
+### 6. Temperature and Sampling
+
+| Task Type | Temperature | Notes |
+|-----------|-------------|-------|
+| Code generation | 0.0–0.2 | Deterministic, fewer errors |
+| Classification | 0.0 | Consistent labels |
+| Creative writing | 0.7–1.0 | More varied output |
+| Summarization | 0.3–0.5 | Balanced accuracy/fluency |
+
+### 7. Evaluate and Iterate
+
+Create an evaluation set:
+1. 10–20 representative inputs with expected outputs
+2. Run prompt variant against all inputs
+3. Score outputs on accuracy, format compliance, conciseness
+4. A/B test two prompt versions before deploying
+
+## Notes
+- Prompt changes can have non-obvious side effects; always test on a representative sample
+- Document prompt versions with rationale (treat prompts like code)
+- Use delimiters (`"""`, `---`, `<tags>`) to separate instructions from user content
+- Model context limits change — design prompts that degrade gracefully when truncated