context-engineering-review
Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.
npx skills add mohitagw15856/pm-claude-skills --skill context-engineering-review --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Context Engineering Review Skill Most agent failures aren't model failures — they're context failures: instructions buried under retrieval dumps, stale history contradicting fresh facts, twelve tool definitions the task never needed. This skill audits the *assembled window*, not just the prompt text. ## What This Skill Produces - A **context inventory**: every component in the window, its size, and who put it there - A **keep / cut / restructure verdict** per component, with the reasoning - **Ordering and cache-alignment fixes** (stable prefix first, volatile content last) - A **token budget** per component with an enforcement point ## Required Inputs Ask for (if not already provided): - **A real assembled context** — an actual logged request (system prompt + messages + tools), not the template. If only the template exists, review that but flag that dynamic bloat is invisible - **The failure or goal** — ignoring instructions? too expensive? inconsistent? slow? - **What varies per request** (retrieval, history, user data) vs. what is static - **The model and its context limit**, and current typical request size ## Review Method **1. Inventory.** List every component in window orde
- What This Skill Produces
- Required Inputs
- Review Method
- Output Format
- Context Engineering Review: [feature/agent]
- Quality Checks
- Anti-Patterns
What does the context-engineering-review skill do?
Review what an LLM feature or agent actually puts in its context window — and find what's bloating, missing, or fighting itself. Use when asked to review a system prompt and context assembly, cut token usage without losing quality, debug an agent that ignores instructions, or audit how retrieval results, history, and tool definitions are packed into the window. Produces a context inventory with a keep/cut/restructure verdict per component, ordering and caching fixes, and a token budget. For wording-level prompt tuning use prompt-optimizer.
How do I install it?
Run `npx skills add mohitagw15856/pm-claude-skills --skill context-engineering-review --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From mohitagw15856/pm-claude-skills, a repository with 1,255 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.