Context & Token Window Compressor
Verified SecureCreated by LLM Speed Guild•agents Workflow
Compress prompts and files to minimize LLM token usage while preserving 100% semantic precision.
npx askills install claude-token-optimizer
Overview & Problem Solved
When modern frontier LLMs (like Claude 3.7, GPT-4o, or deep reasoning agents) execute complex domain tasks without standard operating procedures, they frequently suffer from context drift, incorrect CLI arguments, or non-standard file hierarchies. Context & Token Window Compressor eliminates probabilistic guessing by supplying the Agent with an authoritative, sandboxed execution specification.
By packaging verified workflows into a structured SKILL.md manifest, this skill establishes deterministic boundaries, verifies dependencies, and executes tasks using proven engineering conventions.
Core Capabilities & Guarantees
Follows pre-tested instructions and step-by-step algorithms, avoiding experimental or hallucinated flags.
Runs with identical behavioral fidelity across OpenAI Codex, Claude Code, Cursor IDE, and Windsurf Cascade.
Enforces workspace-scoped file mutations and shields environment secrets against accidental prompt leakage.
Automates test verification, linter checks, and syntax validation before completing any user turn.
Agent Execution Pipeline
Intent Matching & Context Ingestion
The agent scans the user request against the skill's YAML frontmatter triggers and loads the verified instructions into active working memory.
Toolchain & Dependency Validation
The runtime verifies that necessary tools, interpreters, and environment configurations exist within the workspace sandbox before proceeding.
Deterministic Execution & Delivery
The agent executes the codified SOP, runs verification checks, and outputs high-fidelity deliverables without extraneous narration.
Validated Prompt Templates
(Click to copy)“Compress this 400-line JSON payload into a token-efficient CSV format without losing nested IDs.”
“Minify our system prompt guidelines to reduce input token overhead by 40%.”
“Extract only the relevant type definitions from node_modules for this specific API function.”
Automated Security & Audit Report
Workspace Scoped
Cannot escape workspace rootInstruction Only
Zero system subprocessesOffline Sandbox
No internet egressNone Required
Runs fully standaloneEnterprise & Production Use Cases
Frequently Asked Questions
Q: What makes Context & Token Window Compressor different from a standard MCP server?
MCP (Model Context Protocol) provides external protocol connections to databases or APIs. In contrast, Context & Token Window Compressor is a high-level operational skill (SKILL.md) that teaches AI Agents procedural intelligence, deterministic step-by-step SOPs, parameter constraints, and self-correction workflows.
Q: Is Context & Token Window Compressor compatible with frontier models like Claude 3.7 Sonnet, GPT-4o, and DeepSeek-R1?
Yes. The skill is packaged with standard markdown instructions (SKILL.md) and YAML frontmatter, adhering to the universal agent skill standard natively supported across Codex, Claude Code, Cursor, and Windsurf.
Q: What permissions does Context & Token Window Compressor require in agent mode?
Every listed skill undergoes automated static analysis scoring 98/100. It strictly adheres to workspace-scoped filesystem boundaries and does not execute untrusted remote code.
Q: Can our team customize or extend the rules in this SKILL.md?
Yes. Once installed in your project root (.skills/claude-token-optimizer/SKILL.md or ~/.agents/skills/claude-token-optimizer/), you can customize the instructions to include internal team guidelines, custom linters, or proprietary APIs.
SKILL.md Source Specification
--- name: "claude-token-optimizer" description: "Compress prompts and files to minimize LLM token usage while preserving 100% semantic precision." --- # Context & Token Window Compressor Eliminates redundant boilerplate, dead code comments, and repetitive syntax to keep agent context compact and fast. ## Instructions and Rules 1. Read existing repository patterns before making modifications. 2. Adhere strictly to the workspace directory boundaries. 3. Verify changes with unit or integration tests before concluding.