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sqlite-agent-context

Detect agent type, capabilities, token budgets, and format responses for LLM platforms.

Updated Aug 23, 2026
Search Tags:#context#optimization#agent#LLM#detection#token management#capabilities
One-click install
npx skills add https://github.com/codetalcott/fixiplug --skill sqlite-agent-context
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sqlite-agent-context
Source: https://github.com/codetalcott/fixiplug/tree/main/.claude/skills/sqlite-agent-context
Command: npx skills add https://github.com/codetalcott/fixiplug --skill sqlite-agent-context

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill empowers AI agents to understand their own operational environment, capabilities, and limitations (like token budgets) across different LLM platforms. It ensures agents operate efficiently and format responses optimally, preventing errors and maximizing performance.

Core Features & Use Cases

  • Agent Detection: Automatically identify the current agent type (e.g., Claude, GPT-4) and its version.
  • Capability Discovery: Get detailed information on agent capabilities such as tool use, vision, and code execution.
  • Token Budget Management: Calculate remaining token budget for conversations, enabling proactive context optimization.
  • Optimal Response Formatting: Format agent outputs (text, code, data, error) to best suit the specific LLM platform.
  • Use Case: Develop an agent that dynamically adjusts its behavior and response style based on whether it's running on Claude 3.5 Sonnet (with vision) or GPT-4 (with advanced reasoning), ensuring it always leverages the platform's strengths.

Quick Start

Use the sqlite-agent-context skill to detect the current agent's type and capabilities, then retrieve its maximum token limit and recommended practices for optimal interaction.

Frequently Asked Questions about sqlite-agent-context

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect what LLM agent is running my code?▼

Agent detection automatically identifies your current agent type and version (e.g., Claude, GPT-4) by analyzing the runtime environment. The sqlite.context.detect hook returns the agent name, version, and confidence score, enabling your code to adapt behavior dynamically based on which platform it's executing on.

How can I discover what capabilities an agent supports?▼

Capability discovery retrieves detailed information about the agent's features—such as tool use, vision, code execution, and reasoning abilities—through the sqlite.context.capabilities hook. You get structured output listing each capability with support status, allowing you to conditionally enable features only when available.

How do I calculate token budget for multi-turn conversations?▼

Token budget management computes remaining tokens in the conversation context using the sqlite.context.token_budget hook. It calculates available tokens based on the agent's maximum limit and current usage, enabling proactive context optimization and preventing token exhaustion mid-conversation.

Can I format agent responses differently for each LLM platform?▼

Optimal response formatting adjusts output structure, code block syntax, and error handling based on the detected agent type. The Skill ensures responses align with each platform's strengths—for example, leveraging vision capabilities on Claude 3.5 Sonnet or advanced reasoning on GPT-4—maximizing performance and compatibility.

What information does agent context detection provide?▼

Agent context detection returns agent type, version, detected capabilities, confidence metric, and recommended token budget in structured output. These metrics enable agents to understand their operational environment, avoid unsupported features, and optimize resource usage across different LLM platforms in multi-turn workflows.

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