菜单
Analyze and report what is consuming the 200K context window. Use when the user says /context-budget, "what's eating my context", "how much context am I using", or wants to optimize token usage.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
Create premium Accenture-branded PowerPoint presentations following Tufte data visualization principles and Dieter Rams design philosophy. Use when creating slides, presentations, or decks for Accenture corporate settings that require (1) Accenture brand identity (purple A100FF chevron logo clean layouts) (2) Minimal data-focused design (Tufte less but better) (3) Executive-ready aesthetics for boardroom presentations client pitches or internal reports
Route proposed additions to the global Codex control plane by deciding whether a request belongs in `~/.codex/AGENTS.md`, a global workflow or reference doc under `~/.codex/docs`, `~/.codex/bin/workflow.py`, or an enforceable non-prose surface. Use when asked to "add this to global AGENTS", "promote this workflow to global", "add a new global trigger", or decide whether something should be a trigger, workflow, reference, hook, lint rule, or runtime guard.
Add, remove, serve, test, and operate a server-backed in-page agent feedback capability for HTML artifacts, static pages, or local app builds. Use when the user wants Browser-style annotation inside the artifact/app itself, marker-local chat, comment-triggered agent work, queued marker processing, deterministic install/remove scripts, preflight/closeout checks, browser acceptance evidence, or progressive-disclosure access to marker context.
Audits any agent harness codebase against 13 meta-principles of quality, scores each 0 to 10 with code citations, generates a self-contained HTML report for operators, and writes an agent-readable AGENTS.md improvement directive with file-level architecture recommendations to reach 9.5 or better. Use when the user wants to audit or rate an agent harness, evaluate harness quality, score an agent framework, get a roadmap for improving a harness, run agentharness-audit, audit this harness, rate Hermes, audit Codex, or audit a Claude Code harness. Not for individual skill audits or general code review.
Use when controlling ChatGPT Atlas through Codex Computer Use, especially browser tasks where context efficiency matters: fewer screenshots, fewer UI snapshots, fewer clicks, and cleaner browser state.
Audit project against golden principles, produce scorecard. Use for /audit, "check project quality". NOT for system setup health (/harness-audit) or eval scoring (/eval-score).
| name | context-budget |
| description | Analyze and report what is consuming the 200K context window. Use when the user says /context-budget, "what's eating my context", "how much context am I using", or wants to optimize token usage. |
| model | haiku |
| effort | low |
| allowed-tools | Read, Glob, Bash |
EXECUTE this skill now. Follow the workflow steps below using the provided $ARGUMENTS. Do NOT describe, summarize, or explain this skill — run it.
CONTEXT_LIMIT: 200,000 tokensCLAUDE_DIR: C:/Users/gurusharan.gupta/.claudeREGISTRY_DIR: C:/Users/gurusharan.gupta/Agents/Claude CodeThese are loaded in every session:
~/.claude/CLAUDE.md, count words × 1.3List all installed skills and their sizes:
wc -w ~/.claude/skills/*/SKILL.md 2>/dev/null
Note which have context: fork (safe — only final output enters main ctx) vs those without (full content enters main ctx on trigger).
Read ~/.claude/.mcp.json if it exists. Each active MCP server contributes tool definitions to the context. Estimate 200-500 tokens per MCP server for tool schema overhead.
Check for auto-memory files:
ls ~/.claude/projects/*/memory/*.md 2>/dev/null | head -20
Count words × 1.3 for any that exist.
List enabled plugins from ~/.claude/settings.json. Estimate 100-300 tokens per plugin for description overhead.
Output:
## Context Budget Report
**Context Limit:** 200,000 tokens
### Always-Loaded Components
| Component | Est. Tokens | Notes |
|-----------|-------------|-------|
| Global CLAUDE.md | <n> | Always loaded |
| Project CLAUDE.md | <n> / N/A | If present |
| Memory files | <n> | Auto-memory entries |
| **Subtotal** | **<n>** | |
### Skills (on-trigger)
| Skill | Est. Tokens | context:fork? | Risk |
|-------|-------------|---------------|------|
| <skill> | <n> | Yes/No | Low/High |
...
### MCP Servers (tool schema overhead)
| Server | Est. Tokens |
|--------|-------------|
| <name> | ~200-500 |
...
### Active Plugins
| Plugin | Est. Tokens |
|--------|-------------|
...
### Summary
- **Always-loaded baseline:** <n> tokens (<n>% of limit)
- **Highest-risk skills (no context:fork):** <list>
- **MCP overhead:** <n> tokens across <count> servers
### Top 3 Optimizations
1. <highest-impact action> — saves ~<n> tokens
2. <second action> — saves ~<n> tokens
3. <third action> — saves ~<n> tokens