# Context Engineering > Use when managing what an LLM sees. Covers context-window budgeting, retrieval and compaction, memory across turns, tool-result pruning, and the failure modes that come from too much context rather than too little. - **Type:** Skill - **Install:** `agentstack add skill-nimadorostkar-claude-skills-collection-context-engineering` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [nimadorostkar](https://agentstack.voostack.com/s/nimadorostkar) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [nimadorostkar](https://github.com/nimadorostkar) - **Source:** https://github.com/nimadorostkar/Claude-Skills-collection/tree/main/skills/ai/context-engineering - **Website:** https://github.com/nimadorostkar/Claude-Skills-collection ## Install ```sh agentstack add skill-nimadorostkar-claude-skills-collection-context-engineering ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # Context Engineering ## Purpose Decide what an LLM sees, and what it does not. Context is a finite budget with a nonlinear cost curve: more context is not more capability, and past a point it is actively less. ## When to Use - Building an agent or assistant that runs over many turns. - A model that performs worse as the conversation gets longer. - Managing tool results, retrieved documents, or long files. - Reducing token cost in a long-running system. ## Capabilities - Context budgeting and allocation. - Retrieval: putting the right thing in, not everything. - Compaction and summarization of history. - Memory: what persists across sessions, and in what form. - Tool-result pruning and progressive disclosure. ## Inputs - The task, and what information it genuinely requires. - The context window and its cost. - The failure symptom, if the system is degrading over long runs. ## Outputs - A context budget with an allocation per component. - A compaction strategy that preserves what matters. - Measurably better performance on long tasks. ## Workflow 1. **Budget the window explicitly** — System prompt, tools, retrieved context, history, and the response all compete for the same space. Decide the allocation rather than letting history consume everything. 2. **Retrieve narrowly** — Ten highly relevant chunks outperform a hundred marginally relevant ones. Irrelevant context does not sit inertly; it distracts. 3. **Compact, do not truncate** — Dropping the oldest turns loses the decisions that explain the current state. Summarize the history into the facts and decisions that are still live. 4. **Prune tool results** — A tool returning 50 KB of JSON when the agent needs three fields is spending the budget on noise. Filter at the tool boundary. 5. **Disclose progressively** — Provide a file listing, not the files. Let the model request what it actually needs. This is how a large codebase fits in a small window. 6. **Externalize memory** — Long-lived state belongs in a file or a store the model can read and write, not in a conversation history that grows without bound. ## Best Practices - Long context degrades attention. A model given 200,000 tokens of context often performs worse than the same model given the 5,000 tokens that mattered. Adding context is not free. - Information in the middle of a long context is attended to least. Put the critical instruction at the start or the end, not buried in the middle. - Compaction is lossy by design. Preserve the decisions, the constraints, and the unresolved questions; discard the exploration that led to them. - A tool that returns raw API output is a context leak. Wrap it, select the fields, and return a summary with a way to fetch the detail. - Cache the stable prefix. A system prompt and tool definitions that do not change should be cached rather than re-sent and re-processed each turn. - Write important state to a file. A note the model writes and re-reads survives compaction; a fact buried in turn 12 does not. ## Examples **Explicit context budget:** ```text Window: 200,000 tokens. Allocation: System prompt + skill 4,000 stable, cached Tool definitions 3,000 stable, cached Working memory file 2,000 the agent's own notes, re-read each turn Retrieved context 20,000 top-k, re-retrieved per turn, not accumulated Conversation history 40,000 compacted when it exceeds this Tool results 30,000 pruned; only the most recent kept in full Reserve for response 16,000 -------- 115,000 leaving deliberate headroom Rules: - History exceeding 40k triggers compaction, not truncation. - Retrieved context is replaced each turn, never appended — otherwise it grows monotonically and crowds out everything else. - Tool results older than 3 turns are replaced by a one-line summary. ``` **Compaction that preserves what matters:** ```python COMPACTION_PROMPT = """\ Summarize the conversation so far into a working state document. This summary replaces the full history, so anything you omit is lost permanently. Preserve: - The user's goal and any constraints they stated. - Decisions made, and the reason for each. - Facts discovered (file paths, API shapes, error messages, values). - What has been tried and failed, so it is not retried. - Open questions and the current next step. Discard: - Exploration that led nowhere. - Full file contents (keep the path and what was learned from it). - Tool call mechanics and intermediate output. Write it as a factual state document, not a narrative.""" ``` **Progressive disclosure instead of loading everything:** ```python # Wrong: 400 files, 2M tokens, does not fit and would not help if it did. context = "\n".join(read(f) for f in repo.all_files()) # Right: give the model a map, and a tool to fetch what it decides it needs. context = repo.tree(max_depth=3) # ~2,000 tokens tools = [read_file, grep, list_directory] # the model pulls what it needs ``` ## Notes - The "lost in the middle" effect is well-documented: retrieval accuracy for a fact placed in the middle of a long context is substantially lower than for the same fact at either end. Placement is not neutral. - Prompt caching makes a large stable prefix cheap after the first call. It changes the economics of a long system prompt entirely — but only if the prefix is genuinely stable, byte for byte. - The instinct to give the model more context is usually wrong. The instinct to give it *better* context is usually right. ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [nimadorostkar](https://github.com/nimadorostkar) - **Source:** [nimadorostkar/Claude-Skills-collection](https://github.com/nimadorostkar/Claude-Skills-collection) - **License:** MIT - **Homepage:** https://github.com/nimadorostkar/Claude-Skills-collection Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-nimadorostkar-claude-skills-collection-context-engineering - Seller: https://agentstack.voostack.com/s/nimadorostkar - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.