context-engineering
Use when context is growing large (50k+ tokens), performance is degrading, instructions are being ignored mid-conversation, or planning multi-agent workflows. Triggers on "lost context", forgotten instructions, or sessions exceeding 30 minutes.
What this skill does
# Context Engineering
## Overview
**Core principle:** Context is a finite resource with diminishing returns. Find the smallest high-signal token set, not the largest.
200K tokens is shared space: system prompt + conversation history + your processing. As context grows, performance degrades predictably.
## When to Use
- Session > 30 minutes or 50k+ tokens
- Instructions being ignored or forgotten
- Repeated clarifications needed
- Planning multi-agent workflows
- Preparing handoffs between sessions
## Quick Reference
| Problem | Symptom | Fix |
|---------|---------|-----|
| **Lost-in-middle** | Mid-conversation instructions ignored | Move critical info to start/end |
| **Context poisoning** | Errors compounding, hallucinations referenced | Summarize and reset |
| **Context distraction** | Irrelevant info degrading performance | Prune aggressively |
| **Context confusion** | Conflicting guidance causing inconsistency | Consolidate instructions |
## Degradation Patterns
### 1. Lost-in-Middle Effect
Information in context middle gets **10-40% lower recall** than edges.
```
[START - High attention]
↓
[MIDDLE - Low attention zone] ← Instructions here get ignored
↓
[END - High attention]
```
**Fix:** Strategic placement
- Critical instructions → START (system prompt, first user message)
- Recent decisions → END (last few messages)
- Reference material → MIDDLE (acceptable for lookup, not instructions)
### 2. Context Poisoning
Early hallucination gets referenced → compounds → becomes "fact".
**Symptoms:**
- Confident statements contradicting earlier facts
- "As we discussed..." referencing things never said
- Circular reasoning citing own previous errors
**Fix:** Checkpoint and summarize
```
Every 10-15 exchanges, create explicit checkpoint:
"Let me summarize what we've established:
1. [Verified fact]
2. [Verified fact]
3. [Decision made]
Continuing from here..."
```
### 3. Context Distraction
Irrelevant tokens compete for attention budget.
**Symptoms:**
- Responses reference unrelated earlier topics
- Focus drifts from current task
- Unnecessary caveats about old context
**Fix:** Aggressive pruning
- Use `/clear` + summary for fresh context
- In multi-agent: give subagents ONLY relevant context
- Remove resolved discussions from active consideration
### 4. Context Confusion
Multiple conflicting instructions create inconsistent behavior.
**Symptoms:**
- Alternating between approaches
- "On one hand... on the other hand..." hedging
- Ignoring some instructions to satisfy others
**Fix:** Consolidate
```
Before: "Use TypeScript" (message 3) + "Keep it simple" (message 12) + "Add types everywhere" (message 27)
After: "TypeScript with practical typing - types where they help, skip where obvious"
```
## Optimization Techniques
### Compaction
When approaching limits, summarize context sections:
```markdown
## Session Summary (compacted)
**Goal:** [One sentence]
**Decisions made:**
- [Decision 1]
- [Decision 2]
**Current state:** [What's done, what's next]
**Key constraints:** [Still-active requirements]
---
[Continue with fresh context]
```
### Observation Masking
Replace verbose tool outputs with compact references:
```
Before: [500 lines of file content in context]
After: "Read src/app/page.tsx - React component with Hero, About, FAQ sections"
```
### Context Partitioning (Multi-Agent)
Isolate subtasks in separate agents with clean contexts:
```
Main Agent (orchestrator):
- High-level plan
- Synthesis of results
Subagent 1 (search): Subagent 2 (implement):
- Only search context - Only implementation context
- Returns summary - Returns code
```
**Rule:** Subagents get task + minimum required context, NOT full conversation history.
## Practical Workflow
### For Long Sessions (>1 hour)
```dot
digraph context_management {
"Every 15-20 min" [shape=diamond];
"Context healthy?" [shape=diamond];
"Continue" [shape=box];
"Create checkpoint summary" [shape=box];
"Consider /clear + summary" [shape=box];
"Every 15-20 min" -> "Context healthy?";
"Context healthy?" -> "Continue" [label="yes"];
"Context healthy?" -> "Create checkpoint summary" [label="degrading"];
"Create checkpoint summary" -> "Consider /clear + summary";
}
```
**Health check questions:**
- Are recent instructions being followed?
- Is focus staying on current task?
- Are responses becoming vague or hedgy?
### For Handoffs (/save-session)
Capture for next session:
1. **Goal state** - What were we trying to achieve?
2. **Current state** - What's done, what's broken?
3. **Key decisions** - Why did we choose X over Y?
4. **Active constraints** - What rules still apply?
5. **Next steps** - Where to pick up?
### For Subagents
```markdown
## Subagent Prompt Template
**Task:** [Specific deliverable]
**Context:** [ONLY what's needed - 50-200 words max]
**Constraints:** [Hard requirements]
**Output format:** [What to return]
[Do NOT include: conversation history, resolved discussions, unrelated files]
```
## Anti-Patterns
| Anti-Pattern | Why it fails | Better approach |
|--------------|--------------|-----------------|
| "Include everything just in case" | Dilutes attention, causes distraction | Include only what's needed NOW |
| Repeating instructions every message | Wastes tokens, implies they weren't heard | Trust system prompt, reinforce only when ignored |
| Long file dumps without summary | Lost-in-middle effect | Read → summarize → reference summary |
| Keeping resolved threads active | Context confusion | Summarize resolution, move on |
## Token Budget Guidelines
| Context size | Expected quality | Action |
|--------------|------------------|--------|
| < 20k | Optimal | Continue normally |
| 20-50k | Good | Monitor for degradation |
| 50-100k | Degrading | Active management needed |
| 100-150k | Poor | Summarize and reset soon |
| > 150k | Critical | Reset with checkpoint |
## Key Insight
> "The goal isn't to use all 200K tokens. It's to use the fewest tokens that achieve your outcome."
Informativity over exhaustiveness. Include what matters for current decisions, exclude everything else, and design systems that access additional information on demand.
---
## What Claude Does vs What You Decide
| Claude handles | You provide |
|---------------|-------------|
| Monitoring context health | Decision to reset or continue |
| Creating checkpoint summaries | Validation that summary is accurate |
| Pruning irrelevant content | Judgment on what's still needed |
| Structuring subagent prompts | Strategic task decomposition |
| Detecting degradation patterns | Timing of interventions |
---
## Skill Boundaries
### This skill excels for:
- Long sessions (>30 min, >50k tokens)
- Multi-agent workflows with handoffs
- Complex projects spanning multiple sessions
- Debugging "forgotten instruction" issues
### This skill is NOT ideal for:
- Short, focused interactions → Not needed
- Single-turn queries → Overhead unnecessary
- Tasks with naturally bounded context → Already constrained
---
## Skill Metadata
```yaml
name: context-engineering
category: meta
version: 2.0
author: GUIA
source_expert: Anthropic research, NeoLabHQ context-engineering-kit
difficulty: advanced
mode: centaur
tags: [context, tokens, memory, multi-agent, handoff, optimization]
created: 2026-02-03
updated: 2026-02-03
```
Related in AI Agents
skill-development
IncludedComprehensive meta-skill for creating, managing, validating, auditing, and distributing Claude Code skills and slash commands (unified in v2.1.3+). Provides skill templates, creation workflows, validation patterns, audit checklists, naming conventions, YAML frontmatter guidance, progressive disclosure examples, and best practices lookup. Use when creating new skills, validating existing skills, auditing skill quality, understanding skill architecture, needing skill templates, learning about YAML frontmatter requirements, progressive disclosure patterns, tool restrictions (allowed-tools), skill composition, skill naming conventions, troubleshooting skill activation issues, creating custom slash commands, configuring command frontmatter, using command arguments ($ARGUMENTS, $1, $2), bash execution in commands, file references in commands, command namespacing, plugin commands, MCP slash commands, Skill tool configuration, or deciding between skills vs slash commands. Delegates to docs-management skill for official documentation.
reprompter
IncludedTransform messy prompts into well-structured, effective prompts — single or multi-agent. Use when: "reprompt", "reprompt this", "clean up this prompt", "structure my prompt", rough text needing XML tags and best practices, "reprompter teams", "repromptception", "run with quality", "smart run", "smart agents", multi-agent tasks, audits, parallel work, anything going to agent teams. Don't use when: simple Q&A, pure chat, immediate execution-only tasks. See "Don't Use When" section for details. Outputs: Structured XML/Markdown prompt, quality score (before/after), optional team brief + per-agent sub-prompts, agent team output files. Success criteria: Single mode quality score ≥ 7/10; Repromptception per-agent prompt quality score 8+/10; all required sections present, actionable and specific.
adaptive-compaction
IncludedAdaptive add-on policy and recovery layer that decides WHEN to compact, prune, snapshot, or fork -- replacing fixed-percent auto-compaction across Claude Code, Codex, and MCP-capable hosts. Trigger on auto-compact timing or damage: "when should I compact", "is it safe to compact now or start a fresh session", "auto-compact fires too early/mid-task", "switching to an unrelated task but the window still has space", "context rot", "answers get worse the longer the session runs", "the agent forgot the plan or my decisions after it summarized", "add a layer on top that manages context without changing the agent", raising autoCompactWindow to give the policy room, or installing/tuning a cross-tool compaction policy or PreCompact hook -- even when "compaction" is never said but the problem is context-window pressure or post-summarization memory loss. Do NOT use to summarize a conversation, build RAG, write a summarization prompt (decides WHEN not HOW), or answer max-context-length trivia.
agent-skill-creator
IncludedCreate cross-platform agent skills from workflow descriptions. Activates when users ask to create an agent, automate a repetitive workflow, create a custom skill, or need advanced agent creation. Triggers on phrases like create agent for, automate workflow, create skill for, every day I have to, daily I need to, turn process into agent, need to automate, create a cross-platform skill, validate this skill, export this skill, migrate this skill. Supports single skills, multi-agent suites, transcript processing, template-based creation, interactive configuration, cross-platform export, and spec validation.
llm-wiki
IncludedUse when building or maintaining a persistent personal knowledge base (second brain) in Obsidian where an LLM incrementally ingests sources, updates entity/concept pages, maintains cross-references, and keeps a synthesis current. Triggers include "second brain", "Obsidian wiki", "personal knowledge management", "ingest this paper/article/book", "build a research wiki", "compound knowledge", "Memex", or whenever the user wants knowledge to accumulate across sessions instead of being re-derived by RAG on every query.
skill-master
IncludedAgent Skills authoring, evaluation, and optimization. Create, edit, validate, benchmark, and improve skills following the agentskills.io specification. Use when designing SKILL.md files, structuring skill folders (references, scripts, assets), ingesting external documentation into skills, running trigger evals, benchmarking skill quality, optimizing descriptions, or performing blind A/B comparisons. Keywords: agentskills.io, SKILL.md, skill authoring, eval, benchmark, trigger optimization.