TokenKiller is a universal throttling system designed to minimize token usage across multi-skill agent workflows through budgets, gating, and progressive disclosure.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install tokenkiller
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install tokenkiller using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is TokenKiller?
TokenKiller is a strategic framework built to systematically reduce token consumption without compromising the success rate of complex AI agent operations. It is particularly effective for workflows involving search, coding, debugging, and documentation where context window bloat often leads to higher costs and degraded performance. By integrating this within your Openclaw Skills library, you can enforce strict constraints on how much data an agent processes and outputs.
The skill operates on the principle of minimal necessary information, utilizing a tiered approach to data retrieval. It ensures that agents prioritize goals and evidence summaries over full-file dumps, making it an essential tool for developers working with large codebases or long-running debugging sessions where managing the context window is critical for efficiency.
TokenKiller Use Cases
- Managing high API costs during intensive multi-step coding or refactoring tasks.
- Preventing context window overflow when analyzing large repositories or long execution logs.
- Throttling agent verbosity in collaborative environments to keep communication concise.
- Automating budget gates for agents performing autonomous research or testing workflows.
How TokenKiller Works
- Assessment: The skill begins by categorizing the task complexity as Simple, Medium, or Complex to establish baseline tool call and output budgets.
- Layered Information: It enforces a hierarchy (L0-L3) where the agent starts with a one-sentence goal (L0) and only moves to full content (L3) when strictly necessary.
- Budget Gating: Monitors execution in real-time, applying soft warnings when tool calls or output lines approach the predefined limits.
- Diff-First Execution: Prioritizes generating patches and summaries rather than reposting entire files or logs into the context.
- Self-Check Loops: Triggers a self-audit every three tool calls to identify duplicate information or excessive output length.
TokenKiller Setup
To deploy TokenKiller within your environment, ensure your agent configuration references the skill during task initialization. It is designed to be auto-enabled when keywords like cost or context length are detected in the prompt. For manual setup in your Openclaw Skills configuration, use the following logic:
# Example of triggering the budget assessment manually
openclaw run tokenkiller --assess "Refactor the authentication module"
You can customize the default budget thresholds in your configuration file to match your specific model requirements.
TokenKiller Data Schema & Taxonomy
TokenKiller organizes data using a complexity-based taxonomy to ensure predictable token usage:
| Complexity Level | Tool Call Budget | Output Line Budget | Typical Scenario |
|---|---|---|---|
| Simple | ≤ 3 | ≤ 50 | Single file edit |
| Medium | ≤ 6 | ≤ 120 | Multi-file exploration |
| Complex | ≤ 10 | ≤ 200 | Cross-module refactoring |
Data is further structured into Evidence summaries containing file paths, line numbers, and key command outputs rather than raw data blocks.
TokenKiller Advanced Features
- Soft-Warning Mechanism: Provides progress updates and remaining work summaries when budgets are nearly exhausted without interrupting the workflow.
- Multi-Skill Constraint Layer: Functions as a global supervisor that constrains the output of other functional Openclaw Skills like search or pdf processing.
- L3 Pull Scenarios: Intelligent logic that only permits full content retrieval for high-stakes actions like code indentation matching or interdependence debugging.
- Search Prioritization: Optimizes file discovery by traversing from cheap (Glob/Grep) to expensive (Semantic Search) methods to minimize read operations.
SKILL.md
Loading
Related Openclaw Skills
AIPM News Digest
A zero-config intelligence engine that scrapes, translates, and summarizes news from 16+ curated AI labs and tech sources into a daily Markdown digest.
Serpshot Google Search API
A powerful search skill that enables AI agents to perform real-time Google web and image searches via the Serpshot API.
Daily Horoscope
A specialized AI skill providing real-time daily astrological forecasts and fortune insights for all twelve zodiac signs.
AI News Toutiao
An advanced AI news aggregator that identifies and ranks high-impact artificial intelligence developments while filtering out SEO noise.
Safe Payment Guard
A high-integrity safety controller that enforces rigorous verification and fraud prevention for all AI-driven financial transactions.
Product Description Writer
A professional-grade AI copywriting tool designed to transform raw product specs into persuasive, SEO-optimized descriptions that drive conversions.
Featured*
