# Claude Code: Context Engineering Audit > Source: > Published: 2026-07-26 16:40:35+00:00 # Claude Code: Context Engineering Audit [Claude](/en/tags/claude/)Code setup against Anthropic's latest context engineering guidelines to see if my "sophisticated" automation was actually just adding noise. The goal was simple: stop front-loading every session with a wall of text and start treating context like scoped variables in a function. ## The Context Engineering Checklist If you're optimizing a Claude Code workflow, these are the five pillars to measure against: **Lightweight** It should be for "gotchas" and non-obvious patterns, not a comprehensive repo wiki.`CLAUDE.md` :**Progressive Disclosure:** Pull in skills and references only when the task demands them.**Model Trust:** Strip out redundant guardrails that newer models already handle natively.**Automatic Memory:** Stop manually maintaining preference blocks in markdown; let the system surface them.**Tool-Centric Design:** Move instructions into tool schemas and parameters rather than prose-heavy system prompts. ## The Bottleneck: Redundant Injection I traced my `SessionStart` hooks to see exactly what was hitting the context window. I found a massive overlap in how my local "memory" layer was interacting with the agent. My `.claude/settings.json` was triggering a sequence of commands that essentially shouted the same information three times: ``` gps preferences --write # Bakes a markdown block into CLAUDE.md gps preferences --markdown # Prints the same list to stdout gps prime # Emits a session primer that overlaps both ``` The result? A ~60-line list of preferences was being injected three times—once via the file read and twice via hook output—before I even typed a single prompt. This is a textbook violation of the "lightweight" and "automatic memory" rules. ## Real-World Impact When you over-engineer the system prompt or the `CLAUDE.md` file, you aren't just wasting tokens; you're increasing the probability of the model getting distracted by irrelevant constraints. The fix is to move away from "manual dumps" and toward a leaner, tool-driven AI workflow. Instead of telling the model *how* to behave in a 60-line list, define those constraints within the tools it uses or keep the `CLAUDE.md` strictly for architectural invariants that the model cannot possibly infer from the code. For anyone building a custom LLM agent deployment, the lesson is clear: if you can't justify why a piece of information needs to be in the initial context window for 100% of tasks, it doesn't belong in the session start. [Next LLM Stack 2026: The Fragmented Pricing Gap →](/en/threads/3749/)