Context Engineering
Use when designing, reviewing, or debugging how an agent's context window gets filled...
Install
npx skills add mvschwarz/openrig --skill context-engineering -g -a claude-code -y- Repository
- mvschwarz/openrig8.5k
- Category
- Design
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Use when designing, reviewing, or debugging how an agent's context window gets filled, pruned, or shared, choosing what loads at boot versus on demand, sizing an install or an always-loaded file, fixing an agent that drifts, repeats itself, or forgets constraints mid-task, planning compaction or summarization, deciding single-agent versus subagents, engineering handoffs between agents, or picking a tool loadout. NOT for rewording a prompt's tone, choosing which model to pin, or debugging business logic, those are adjacent moments this skill does not serve. Historical 2024, 2025 snapshot; not normative for present-day frontier models, see the Status section.
Traditional Context Engineering, 2024, 2025 Snapshot
Status: provisional historical research snapshot
This skill is a provisional historical research snapshot of context-engineering practice as published in 2024, 2025. It is NOT normative. Do not apply it prescriptively to current frontier models without re-verification. On any conflict, OpenRig current skills, explicit user rulings, and directly measured OpenRig practice PREVAIL over this document. Treat these areas as particularly suspect pending re-verification: compaction/summarization guidance, minimal-upfront versus broad orientation, just-in-time lookup assumptions, fixed context ceilings, and single-agent versus multi-agent advice.
Curated distillation of the best publicly available expertise on context engineering for coding agents, drawn from primary sources at Anthropic, OpenAI, and leading practitioners (Manus, Cognition, Chroma, LangChain, Drew Breunig, and others). Load on the moments the description names; it is deliberately not part of any base walk.
1. The mental model: what context engineering is
Definition. Context engineering is "the set of strategies for curating and maintaining the optimal set of tokens (information) during LLM inference", everything that lands in the window: system instructions, tool definitions, retrieved data, message history, and tool outputs, not just the prompt text (Anthropic, Effective context engineering for AI agents). Andrej Karpathy's framing, popularized via LangChain: "the delicate art and science of filling the context window with just the right information for the next step", the LLM is a CPU and the context window is its RAM, and your job is deciding what gets loaded into RAM at each step (LangChain, Context Engineering for Agents).
