# Audit Cursor Context > Audit Cursor IDE context-bar consumption (rules, tools, skills, MCP, AGENTS.md) and recommend cuts. Use when the user complains rules/tools/AGENTS.md are taking too much context, opens the context bar and asks why a layer is large, asks to reclaim context budget, or asks to audit Cursor configuration for token bloat. Use when this capability is needed. - Skill: `tomevault-io/audit-cursor-context` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/audit-cursor-context` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/audit-cursor-context/raw - Safety review: pending (external: skill-scanner PASS, skillspector CAUTION) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/audit-cursor-context --- # Audit Cursor Context Investigate what is consuming the always-on portion of the Cursor context window in this workspace and recommend specific cuts. Always-on layers (system prompt + tools + rules + skills + MCP) load before the user types — every token there is a token unavailable to the conversation. ## Reference Companion research doc: `docs/research/cursor-context-budget-audit.md` (findings, recommendations, May 2026 best practices). ## Methodology Run the seven steps below in order. Use the table at the end as the deliverable. ### Step 1: Read the user's reported context-bar numbers Ask the user to share their Cursor context bar values, or read them from a screenshot if attached. The eight layers to capture: | Layer | What lives there | | ----------------------- | ------------------------------------------------------ | | System prompt | Cursor's built-in agent prompt | | Tools | Built-in editor tools + MCP tool schemas | | Rules | `AGENTS.md` + `.cursor/rules/*.mdc` with `alwaysApply` | | Skills | Frontmatter (description) of every installed skill | | MCP | MCP server-level instructions (not per-tool schemas) | | Subagents | Subagent type catalog | | Summarized conversation | Compacted older turns | | Conversation | Live message history | ### Step 2: Inventory `.cursor/rules/` ```bash for f in .cursor/rules/*.mdc; do echo "=== $f ===" head -5 "$f" wc -l "$f" done ``` Flag every rule with `alwaysApply: true`. Healthy baseline: ≤3 always-applied rules totalling <3K tokens (≈12KB). ### Step 3: Measure `AGENTS.md` overhead ```bash wc -c AGENTS.md # bytes wc -l AGENTS.md # lines echo "≈$(($(wc -c < AGENTS.md) / 4)) tokens" # 3.7 chars/token rule of thumb ``` If `AGENTS.md` exceeds **8K tokens (≈32KB)**, it is too large. The ceiling matters because `AGENTS.md` is always loaded. ### Step 4: Inventory skills ```bash ls .agent/skills/ # personal skills ls ~/.agent/skills/ 2>/dev/null ls ~/.agent/skills-cursor/ 2>/dev/null ``` Skills are progressive-disclosure — only their frontmatter (`name` + `description`) is loaded until invoked. Each costs ≈50–250 tokens. **Do not** recommend deleting skills to save context; recommend `disable-model-invocation: true` on skills that should only run when explicitly typed (`/skill-name`). ### Step 5: Inventory MCP servers ```bash cat .cursor/mcp.json 2>/dev/null ls ~/.cursor/projects/*/mcps/ 2>/dev/null ``` For each server, count tools (each MCP tool descriptor lives at `mcps//tools/*.json`). Cursor enforces a **40-tool ceiling**. If the workspace ships >40 tools, Cursor silently drops the overflow. ### Step 6: Map each context-bar number to its source Use these rough conversions to attribute bytes → tokens (English prose, ~3.7 chars/token): | Source | Approximate tokens | | ---------------------------------------------------- | ------------------------------------- | | `AGENTS.md` (bytes / 4) | Always-loaded contribution to Rules | | Sum of `alwaysApply: true` rule bodies | Always-loaded contribution to Rules | | Each MCP tool schema (`name + description + params`) | ≈50–300 tokens depending on verbosity | | Each skill frontmatter | ≈50–250 tokens | | MCP server `serverUseInstructions` | Variable; can be 1–3K each | ### Step 7: Produce the deliverable Output a single markdown table the user can act on: ```markdown | Layer | Reported | Largest contributor | Recommended action | Reclaimed (est.) | | ------ | -------- | ----------------------------------------------------------------- | -------------------------------------------------------------------- | ---------------- | | Rules | 59.3K | `AGENTS.md` (147KB ≈ 37K tokens) | Split into lean core + glob-scoped `.cursor/rules/learned-*.mdc` | ≈30K | | Tools | 22.4K | `cursor-ide-browser` MCP server (~20 tools, verbose descriptions) | Trim per-tool descriptions to ≤50 words; disable unused tools | ≈8K | | Skills | 4.4K | 30 skills × frontmatter | Add `disable-model-invocation: true` to skills only invoked manually | ≈0.5K | ``` ## Decision rules - **Rules > 20K tokens** → audit `AGENTS.md` size first. Anything over 8K tokens in a single always-loaded file is the prime suspect. - **Tools > 15K tokens** → enumerate MCP servers; suspect the one with the longest `serverUseInstructions` and most verbs. - **Skills > 10K tokens** → unusually high; skills are normally cheap. Re-check whether any skill is being inlined instead of frontmatter-only. - **MCP > 10K tokens** → trim `serverUseInstructions` blocks (move detailed playbooks into a skill). - **Always recommend** layering a lean `AGENTS.md` (architectural core, ≤8K tokens) on top of glob-scoped `.cursor/rules/*.mdc` for long-tail facts. Never recommend deleting `AGENTS.md` outright — it is portable across Claude Code / Copilot / Cursor. - **Never recommend** deleting skills, only marking them `disable-model-invocation: true`. - **Never recommend** disabling Cursor's dynamic context discovery (tool-output-to-file, summarization-with-history-search) — those are wins, not costs. ## Refactoring patterns ### Pattern A: Split a fat `AGENTS.md` paragraph into a scoped rule Move the long-tail "Learned Workspace Facts" / "Learned User Preferences" paragraphs into focused, glob-scoped `.cursor/rules/*.mdc` files: ```yaml --- description: Graphics-pipeline facts for the CAD viewer (Three.js, WebGPU, TSL) globs: - 'apps/ui/app/components/geometry/**' - 'apps/ui/app/machines/graphics.machine.ts' alwaysApply: false --- ``` The agent loads the file only when the user is touching matching paths. Token cost when not touched: zero. ### Pattern B: Convert a verbose MCP tool description to a skill If an MCP server ships paragraph-length `serverUseInstructions`, move the detailed playbook into a `.agent/skills//SKILL.md` and shrink the server-level instructions to ≤50 words pointing at the skill name. Skill bodies load only when the skill is invoked. ### Pattern C: Convert an `alwaysApply: true` rule to glob-scoped Rewrite the rule's frontmatter: ```yaml # Before alwaysApply: true # After globs: - "**/*.ts" - "**/*.tsx" alwaysApply: false ``` Audit the body to confirm every guideline is genuinely scoped to those file types. If one guideline is universal (e.g. "use pnpm"), keep that one as a separate small `alwaysApply: true` rule (≤300 tokens) and scope the rest. ## Anti-patterns - **Don't** delete `AGENTS.md` — split it instead. Other agents (Claude Code, Copilot) read it. - **Don't** delete skills to reclaim Skills-line tokens — the line is already small (frontmatter only). - **Don't** measure tokens with `wc -w`; use bytes / 3.7 (English prose) or bytes / 4 (mixed code) as the conversion. - **Don't** recommend running every audit through `ctxaudit` if the workspace already has the seven-step shell methodology above; the CLI is a convenience, not a requirement. ## External tooling (optional) [`sanjeed5/ctxaudit`](https://github.com/sanjeed5/ctxaudit) is a Python CLI that automates Steps 2–6 across user-level and project-level Cursor configuration. Use it as a sanity check after manual cuts: ```bash pipx run ctxaudit ``` ## Output checklist When this skill finishes, the user should have: - [ ] A table mapping each context-bar layer to its filesystem origin - [ ] An explicit "largest contributor" call-out per layer - [ ] A prioritized list of cuts with estimated tokens reclaimed - [ ] No recommendation to disable dynamic context discovery - [ ] No recommendation to delete `AGENTS.md` outright - [ ] No recommendation to delete skills --- > Source: [taucad/tau](https://github.com/taucad/tau) — distributed by [TomeVault](https://tomevault.io).