How do I install the Context Viz skill?

Run npx skillmds@latest add modbender/context-viz in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Context Viz safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts, reads secrets. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Context Viz?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Context Viz free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Context Viz?

modbender (@modbender) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Context Viz by modbender — Agent Skill

Name: Context Viz
Rating: 4.2 (5 reviews)
Author: modbender

Context Viz

Visualize the current context window usage — token estimates per component (system prompt, tools, workspace files, messages, free space). Use when the user asks about context size, token usage, context breakdown, "how full is the context", or wants to see what's consuming their context window.

modbender Updated Sep 9, 2026 12 repo stars

File contents

Context Visualization

Estimate and display a breakdown of the current context window usage.

How It Works

Run the bundled script to estimate token counts for workspace files:

python3 scripts/estimate_tokens.py /path/to/workspace

The script counts characters in known workspace files and estimates tokens (~4 chars/token).

Then call session_status to get the actual context usage from OpenClaw.

Generating the Visualization

Run session_status to get: model, context used/total, compactions
Run scripts/estimate_tokens.py <workspace_path> to estimate file token sizes
Estimate message tokens: context_used - system_overhead - file_tokens
Present the breakdown using the format below

Output Format

Use a monospace block with bar chart. Adapt the bar lengths proportionally.

📊 Context Usage
<model> • <used>k/<total>k tokens (<pct>%)

Component                    Tokens    %     
─────────────────────────────────────────────
⚙️  System prompt + tools    ~Xk      X%    ░░
📋  AGENTS.md                ~Xk      X%    ░
👻  SOUL.md                  ~Xk      X%    
👤  USER.md                  ~Xk      X%    
🔧  TOOLS.md                 ~Xk      X%    ░
💓  HEARTBEAT.md             ~Xk      X%    
🧠  MEMORY.md                ~Xk      X%    ░
🪪  IDENTITY.md              ~Xk      X%    
💬  Messages                 ~Xk      X%    ░░░░░░░░░░░░
📭  Free space               ~Xk      X%    ░░░░░
─────────────────────────────────────────────

Use ░ blocks: 1 block per ~2% of total context. Round to nearest block.

Memory Inventory (not in context)

Below the context chart, add a Memory on Disk section showing what's stored in memory/ — grouped by category. These files are NOT loaded into context but represent the agent's total knowledge base.

💾 Memory on Disk (not in context)
Category                     Files  Tokens   Size
──────────────────────────────────────────────────
📰  chinese-ai-digests        12    ~23k     92KB
📁  other                     11    ~12k     46KB
📅  daily-notes                9    ~5k      17KB
🗃️  zettelkasten               8    ~4k      15KB
💼  linkedin                   2    ~1k       5KB
──────────────────────────────────────────────────
     Total:                   42    ~44k    177KB

The script auto-categorizes files by directory or filename pattern.

Notes

Token estimates use ~4 chars/token (rough average for English/mixed content)
System prompt + tools overhead is estimated at ~8-10k tokens for a typical OpenClaw setup
Message tokens are the remainder after subtracting files + system overhead
Memory files are informational only — they show what the agent has accumulated
For Discord/WhatsApp: skip markdown tables, use the block format above

---name: context-vizdescription: Visualize the current context window usage — token estimates per component (system prompt, tools, workspace files, messages, free space). Use when the user asks about context size, token usage, context breakdown, "how full is the context", or wants to see what's consuming their context window.--- # Context Visualization Estimate and display a breakdown of the current context window usage. ## How It Works Run the bundled script to estimate token counts for workspace files: ```bash python3 scripts/estimate_tokens.py /path/to/workspace ``` The script counts characters in known workspace files and estimates tokens (~4 chars/token). Then call `session_status` to get the actual context usage from OpenClaw. ## Generating the Visualization 1. Run `session_status` to get: model, context used/total, compactions 2. Run `scripts/estimate_tokens.py <workspace_path>` to estimate file token sizes 3. Estimate message tokens: `context_used - system_overhead - file_tokens` 4. Present the breakdown using the format below ## Output Format Use a monospace block with bar chart. Adapt the bar lengths proportionally. ``` 📊 Context Usage <model> • <used>k/<total>k tokens (<pct>%) Component Tokens % ───────────────────────────────────────────── ⚙️ System prompt + tools ~Xk X% ░░ 📋 AGENTS.md ~Xk X% ░ 👻 SOUL.md ~Xk X% 👤 USER.md ~Xk X% 🔧 TOOLS.md ~Xk X% ░ 💓 HEARTBEAT.md ~Xk X% 🧠 MEMORY.md ~Xk X% ░ 🪪 IDENTITY.md ~Xk X% 💬 Messages ~Xk X% ░░░░░░░░░░░░ 📭 Free space ~Xk X% ░░░░░ ───────────────────────────────────────────── ``` Use ░ blocks: 1 block per ~2% of total context. Round to nearest block. ## Memory Inventory (not in context) Below the context chart, add a **Memory on Disk** section showing what's stored in `memory/` — grouped by category. These files are NOT loaded into context but represent the agent's total knowledge base. ``` 💾 Memory on Disk (not in context) Category Files Tokens Size ────────────────────────────────────────────────── 📰 chinese-ai-digests 12 ~23k 92KB 📁 other 11 ~12k 46KB 📅 daily-notes 9 ~5k 17KB 🗃️ zettelkasten 8 ~4k 15KB 💼 linkedin 2 ~1k 5KB ────────────────────────────────────────────────── Total: 42 ~44k 177KB ``` The script auto-categorizes files by directory or filename pattern. ## Notes - Token estimates use ~4 chars/token (rough average for English/mixed content) - System prompt + tools overhead is estimated at ~8-10k tokens for a typical OpenClaw setup - Message tokens are the remainder after subtracting files + system overhead - Memory files are informational only — they show what the agent has accumulated - For Discord/WhatsApp: skip markdown tables, use the block format above

Frequently asked questions