Context Engineering Agents

A working toolkit for treating the context window as a managed resource rather than an afterthought. This skill helps you design agents whose prompts stay inside token budgets, pull in only the context a step actually needs, compact history as it grows, and survive long-horizon runs without silently drifting off-task. It is aimed at developers building multi-step or long-running agents on top of LLM APIs (Claude, GPT, open models) who keep hitting context-window limits, ballooning costs, or degraded reasoning after dozens of turns.

When to use it

• An agent loop runs for hundreds of turns and the transcript no longer fits the window.
• You pay per token and want to stop re-sending the entire history on every call.
• Multiple tools (CLI, IDE, CI) need to share the same project context without copy-paste.
• A long task slowly loses the original goal, repeats steps, or contradicts earlier decisions.
• You need to decide what belongs in the system prompt vs. retrieval vs. an external store.

What it does

• Token budgeting. Assigns explicit budgets to system prompt, tool defs, retrieved chunks, and history; uses a tokenizer (e.g. anthropic.count_tokens, tiktoken) to enforce them before each call.
• AGENTS.md as shared context. Treats a repo-root AGENTS.md as the single source of cross-tool instructions, conventions, and project facts that every agent reads.
• Dynamic selection & retrieval. Scores and pulls only relevant docs/snippets per step instead of stuffing everything in.
• Summarization-based compaction. Replaces old turns with rolling summaries when usage crosses a threshold, preserving decisions while dropping transcript bulk.
• Memory tiering. Separates working memory (current step), short-term (recent turns), and long-term (vector/KV store) with explicit promotion/eviction rules.
• Drift monitoring. Periodically checks current behavior against the stated objective and flags divergence.

Example

curl -fsSL --create-dirs -o ~/.claude/skills/context-engineering-agents/SKILL.md https://claudeskil.com/skills/context-engineering-agents/SKILL.md

# context.config.yaml
budget:
  total_tokens: 180000
  system: 4000
  tools: 6000
  retrieval: 40000
  history: 120000
compaction:
  trigger_ratio: 0.8        # summarize when history hits 80% of its budget
  keep_last_turns: 6
memory:
  long_term: { store: vector, top_k: 8 }
context_sources: [AGENTS.md]
drift_check:
  every_n_turns: 20

Tips & gotchas

• Budget the output too: reserve max_tokens for the reply so a full input window doesn't truncate the answer.
• Summaries are lossy — pin hard constraints (IDs, file paths, decisions) into a protected block that compaction never rewrites.
• Keep AGENTS.md short and stable; it is prepended on every call, so churn there multiplies cost.
• Retrieval relevance beats volume. A smaller, sharper top_k usually improves reasoning over a large dump.

FAQ

Does this depend on a specific model? No. It is tokenizer- and provider-agnostic; configure the count function for your model.

How is compaction different from a bigger context window? A larger window delays the problem; compaction and tiering bound cost and keep signal-to-noise high regardless of window size.

Will it edit my prompts automatically? It applies budgets and compaction at runtime; your authored system prompt and AGENTS.md stay under your control.