How do I install the Context Engineering skill?

Run npx skillmds@latest add thewatcher01/context-engineering in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Engineering safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Engineering?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Engineering free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Context Engineering?

TheWatcher01 (@thewatcher01) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Engineering by thewatcher01

Name: Context Engineering
Rating: 4.5 (4 reviews)
Author: TheWatcher01

Context Engineering

Optimize the AI context window for any task. USE when: agent has poor memory or forgets context, responses are imprecise, needing to compress long conversations, building RAG pipelines, designing multi-turn agent state, managing tool results in context, or when the user says "the AI doesn't remember" or "context too long". Implements Karpathy/Willison context engineering: right info, right time, right format, right size.

TheWatcher01 fe5f519 3.6 KB Updated Sep 10, 2026

File contents

Skill: Context Engineering

"Context engineering is the art of filling the context window with just the right information, in the right format, at the right time — to reliably achieve the task." — Karpathy, 2025

Decision Tree — What to put in context

What is the agent missing?

├── Task definition unclear → Add: task description + success criteria
├── No examples → Add: 2-5 few-shot examples (input→output pairs)
├── Background knowledge needed → Add: RAG results (top-3 chunks, not 20)
├── Tool results overwhelming → Compress: summarize, keep key values only
├── Long history → Compact: summarize past turns, keep last 3-5 turns raw
├── Multi-modal inputs → Add: image/file after text instruction
├── Agent "forgets" midway → Add: persistent summary at context top
└── Agent loses task focus → Add: explicit reminder at context bottom

6 Context Layers (fill in priority order)

Layer	What	When to include
1. Instructions	System prompt, task definition	Always
2. State	Current task status, memory	When multi-step
3. Background	RAG results, docs	When domain knowledge needed
4. Examples	Few-shot in-context demos	For complex/ambiguous tasks
5. History	Recent conversation turns	Last 3-5 only
6. Tools	Available tool specs	On demand, not all at once

Context Compression Techniques

1. Summarize long history

[CONTEXT SUMMARY — previous N turns]
User asked about X. Agent found Y. Current status: Z.
[END SUMMARY]

[RECENT TURNS — last 3]
User: ...
Assistant: ...

2. RAG result trimming

Max 3 chunks, not everything retrieved
Include: source, date, key sentence
Exclude: full documents, duplicate info

3. Tool result compression

# Instead of dumping full JSON:
Tool result: Found 3 files. Relevant: config.toml (line 42: api_key), .env (line 7: SECRET)

4. Progressive context (for long tasks)

Phase 1 prompt: task + tools (no history)
Phase 2 prompt: phase 1 summary + new task + tools
Phase N prompt: compressed N-1 history + current step

Context Window Budget (8K context model)

System prompt:      500-1000 tokens  (10-12%)
Task description:   200-400 tokens   (5%)
Examples (few-shot): 300-600 tokens  (7%)
RAG/background:     1000-2000 tokens (20%)
Tool specs:         500-1000 tokens  (10%)
Conversation:       remaining        (~45%)
Output buffer:      reserve 1000     (12%)

Anti-Patterns

❌ Dump ALL retrieved docs → ✅ Top-3 most relevant chunks
❌ Keep full conversation history → ✅ Summarize after 5 turns
❌ Put examples at the END → ✅ Put examples just before the task
❌ All tools in every prompt → ✅ Inject tools relevant to current step
❌ No state management → ✅ Explicit state object in context

For ZeroClaw/Agent Systems

# config.toml — enable context compaction
[agent]
compact_context = true

# Memory: store compressed summaries, not full history
[memory]
backend = "sqlite"
auto_save = true

Trigger hint:smart route for complex context operations (uses GPT-OSS-120B-free for better context reasoning).

---name: context-engineeringdescription: Optimize the AI context window for any task. USE when: agent has poor memory or forgets context, responses are imprecise, needing to compress long conversations, building RAG pipelines, designing multi-turn agent state, managing tool results in context, or when the user says "the AI doesn't remember" or "context too long". Implements Karpathy/Willison context engineering: right info, right time, right format, right size.---# Skill: Context Engineering> "Context engineering is the art of filling the context window with just the right information,> in the right format, at the right time — to reliably achieve the task." — Karpathy, 2025## Decision Tree — What to put in context```What is the agent missing?├── Task definition unclear → Add: task description + success criteria├── No examples → Add: 2-5 few-shot examples (input→output pairs)├── Background knowledge needed → Add: RAG results (top-3 chunks, not 20)├── Tool results overwhelming → Compress: summarize, keep key values only├── Long history → Compact: summarize past turns, keep last 3-5 turns raw├── Multi-modal inputs → Add: image/file after text instruction├── Agent "forgets" midway → Add: persistent summary at context top└── Agent loses task focus → Add: explicit reminder at context bottom```## 6 Context Layers (fill in priority order)| Layer | What | When to include ||-------|------|----------------|| 1. Instructions | System prompt, task definition | Always || 2. State | Current task status, memory | When multi-step || 3. Background | RAG results, docs | When domain knowledge needed || 4. Examples | Few-shot in-context demos | For complex/ambiguous tasks || 5. History | Recent conversation turns | Last 3-5 only || 6. Tools | Available tool specs | On demand, not all at once |## Context Compression Techniques### 1. Summarize long history```[CONTEXT SUMMARY — previous N turns]User asked about X. Agent found Y. Current status: Z.[END SUMMARY][RECENT TURNS — last 3]User: ...Assistant: ...```### 2. RAG result trimming- Max 3 chunks, not everything retrieved- Include: source, date, key sentence- Exclude: full documents, duplicate info### 3. Tool result compression```# Instead of dumping full JSON:Tool result: Found 3 files. Relevant: config.toml (line 42: api_key), .env (line 7: SECRET)```### 4. Progressive context (for long tasks)```Phase 1 prompt: task + tools (no history)Phase 2 prompt: phase 1 summary + new task + toolsPhase N prompt: compressed N-1 history + current step```## Context Window Budget (8K context model)```System prompt: 500-1000 tokens (10-12%)Task description: 200-400 tokens (5%)Examples (few-shot): 300-600 tokens (7%)RAG/background: 1000-2000 tokens (20%)Tool specs: 500-1000 tokens (10%)Conversation: remaining (~45%)Output buffer: reserve 1000 (12%)```## Anti-Patterns- ❌ Dump ALL retrieved docs → ✅ Top-3 most relevant chunks- ❌ Keep full conversation history → ✅ Summarize after 5 turns- ❌ Put examples at the END → ✅ Put examples just before the task- ❌ All tools in every prompt → ✅ Inject tools relevant to current step- ❌ No state management → ✅ Explicit state object in context## For ZeroClaw/Agent Systems```toml# config.toml — enable context compaction[agent]compact_context = true# Memory: store compressed summaries, not full history[memory]backend = "sqlite"auto_save = true```Trigger `hint:smart` route for complex context operations (uses GPT-OSS-120B-free for better context reasoning).

Frequently asked questions