avav25 Context Engineering
Context engineering is the discipline of systematically selecting, structuring, and delivering the smallest possible set of high-signal tokens that make a task plausibly solvable for an LLM — reliably, safely, and cost-effectively.
This skill provides actionable patterns, checklists, and templates for the full context pipeline — from first principles through production readiness.
When to Use
- Designing context pipelines for AI agents (what enters the LLM context window, in what order)
- Reviewing context quality and token efficiency of existing systems
- Building RAG systems (retrieval pipeline, chunking, packing, grounding)
- Implementing memory (session, working, long-term, organizational, tool-output)
- Designing agent harness (init/continuation prompts, state management, token budgets, caching)
- Multi-agent orchestration (context boundaries, payload schemas, fan-out merge)
- Production readiness assessment for AI systems (8-checklist gate)
- Privacy and compliance for AI features (multi-tenant isolation, consent, data residency)
When NOT to Use
- Prompt technique selection and template patterns → use
prompt-engineeringskill - Security audit of individual prompts → use
prompt-engineeringskill →security-checklist.md - Non-AI software engineering tasks → use
software-engineerrole + stack-specific role - Infrastructure and deployment → use
devops-engineerrole
Context Engineering vs Prompt Engineering
These are complementary disciplines:
- Prompt engineering = designing the instruction text (technique selection, template patterns, formatting, few-shot)
- Context engineering = designing the full context PIPELINE (what enters the window, in what order, how it's managed across turns, how it degrades gracefully)
Context engineering is the system; prompt engineering is one layer within it.
Key Concepts
First Principles
- Attention is finite — curate, don't stuff. "More context" is not monotonically better
- Position effects — critical info at beginning and end ("lost-in-the-middle"). Models use tokens near the start and end more effectively than those buried in the middle
- High-signal tokens > high-volume tokens — compact, discriminative context beats verbose dumps
- Separation of concerns — policy, knowledge, examples, output contract in distinct layers. Enables independent swap, A/B testing, failure analysis
- Context is a pipeline — produced by a system (retrieval, ranking, summarization, memory, policy filters, schema enforcement), not concatenated strings
The Context Stack (8-Layer Architecture)
Ordered layers, top = highest priority. Higher layers override lower on conflict.
| Layer | What Goes Here | Design Notes |
|---|---|---|
| 1. System policy & safety | Non-negotiable constraints, content policies, refusal rules | First in window — highest attention. Cacheable |
| 2. Developer instructions | Role definitions, hard rules, reasoning protocols, conventions | Maps to: Codex package rules, role overlays, and AGENTS.md guidance |
| 3. Tool contracts | Schemas, descriptions, permissions, failure modes, retry policies | Tool descriptions ARE prompts — optimize them |
| 4. Runtime state | Current project context, AGENTS.md, active file, task state, structured state blob | Maps to: Codex AGENTS.md files |
| 5. Knowledge context (RAG) | Retrieved documents, search results, file contents | Must be wrapped as untrusted data with grounding envelopes |
| 6. Memory | Session, working, long-term, organizational, tool-output | Must be scoped, filtered, and conflict-resolved |
| 7. Examples (few-shot) | Minimal, high-leverage exemplars aligned to output contract | Dynamic selection preferred; skip if schema suffices |
| 8. Output contract | JSON Schema, format instructions, constraints, error handling | End of context — recency bias helps compliance |
Rule: keep policy/instructions (Layers 1-2) separate from knowledge (Layer 5). Mixing them is a root cause of prompt injection and "obedience confusion."
→ Full reference: context-stack-model.md
Cacheable Prefix Design
Layers 1-3 rarely change → group all static content at the beginning of the prompt to enable KV cache reuse. Append dynamic content (Layer 4-8) at the end. Never interleave static and dynamic blocks.
Track cache_prefix_ratio = cached_tokens / total_input_tokens (target > 0.3).
Token Budget Allocation
Treat context window as a finite resource with explicit budget per layer:
| Allocation | Suggested % | Notes |
|---|---|---|
| System + rules + tools (L1-3) | 15-25% | Stable, cacheable prefix |
| Runtime state (L4) | 5-10% | Structured state blob |
| Knowledge / RAG (L5) | 30-40% | Dynamic, highest signal variance |
| Memory (L6) | 10-15% | Filtered, relevance-ranked |
| Examples (L7) | 0-10% | Only when needed |
| Output contract (L8) | 5-10% | Schema + error handling |
Overflow strategy: truncate lowest-priority layers first (L7 → L6 → L5).
Resource Files
| File | Contents | Guide §§ |
|---|---|---|
context-stack-model.md |
8-layer architecture, layer design patterns, position effects, mapping to Codex assets | §3 |
memory-engineering.md |
Memory taxonomy, CRUD lifecycle, conflict handling, compression strategies | §6 |
agent-harness-patterns.md |
Init/continuation, state blobs, token budgets, caching, failure modes, streaming | §7 |
rag-engineering.md |
Pipeline blueprint (7 stages), chunking, packing, degradation signals | §5 |
multi-agent-patterns.md |
Context boundaries, payload schemas, return contracts, fan-out, contamination | §7.6, §14 |
production-checklists.md |
All 8 production readiness checklists | §11 |
reference-templates.md |
Grounding envelope, untrusted wrapper, structured output, tool error envelope | §12 |
privacy-compliance-ai.md |
Multi-tenant isolation, data controls, consent, regional compliance | §9 |
Integration
- Follows rules:
prompt-engineerrole (prompt-level techniques, security, eval-first quality) - Used by workflows:
ai-skillsskill (context engineering validation),feature-devskill (AI features),planskill (AI work packages),code-reviewskill (context quality review) - Companion skill:
prompt-engineeringskill (technique selection, template patterns, eval, security checklist) - Collaborates with roles:
solution-architectrole (AI system design),product-managerrole (agent contracts),sre-engineerrole (context observability),software-engineerrole (implementation) - Primary source:
guides/context_engineering_guide.md