Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Productivity
Context Visualization
Visualize the current context window usage — token estimates per component (system prompt, tools, workspace files, messages, free space). Use when the user a...
skill openclawclawhub Free
Visualize the current context window usage — token estimates per component (system prompt, tools, workspace files, messages, free space). Use when the user a...
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- scripts/estimate_tokens.py, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- furukama
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs1 Scripts
- SKILL.md
- scripts/estimate_tokens.py