AgentSkillsCN

long-context-processing

在 Simmer 的 AI 价格与 Polymarket 价格出现显著背离的市场中寻找投资机会。背离幅度越大，潜在收益空间也越大。适用于用户希望挖掘 AI 与市场之间的分歧、扫描交易机会，或了解 AI 对外盘价格的看涨/看跌倾向时使用。

SKILL.md
--- frontmatter
name: long-context-processing
description: Process very large documents or codebases using Gemini's 2M token context window. Use when dealing with content that exceeds typical context limits or requires seeing everything at once.
allowed-tools: gemini-api, Read, Glob, Grep

Long Context Processing with Gemini

When to Use This Skill

Automatically invoke this skill when:

  • •User needs to analyze an entire codebase at once
  • •Document or file content exceeds ~100K tokens
  • •User explicitly requests processing multiple large files together
  • •Task requires comprehensive "see everything" context
  • •User asks to find patterns across many files
  • •Analyzing very long documents (books, research papers, extensive logs)

Examples That Trigger This Skill

  • •"Analyze the entire codebase architecture"
  • •"Summarize this 500-page document"
  • •"Find patterns across all log files"
  • •"Review all Python files in this project"
  • •"What are the common themes in these research papers?"
  • •"Analyze all API endpoints across the codebase"

How to Use

  1. •Gather content:
    • •Use Glob to find all relevant files
    • •Use Read to get file contents
    • •Concatenate into a single large text block
  2. •Estimate size: Calculate approximate tokens (chars / 4)
  3. •Call Gemini: Use the process_long_context tool from gemini-api MCP server
    • •Pass the aggregated content
    • •Include user's analysis request as the prompt
    • •Use gemini-1.5-pro for best long-context performance
  4. •Present results: Return Gemini's comprehensive analysis

Tool Parameters

javascript
{
  "content": "[very large concatenated text content]",
  "prompt": "Analyze the overall architecture and identify main patterns",
  "model": "gemini-1.5-pro"  // Pro recommended for long context
}

Capabilities

  • •Context Window: Up to 2 million tokens (~1.5 million words, ~8 million characters)
  • •Whole Codebase Analysis: See entire project structure and relationships
  • •Pattern Detection: Find recurring themes across extensive content
  • •Comprehensive Summarization: Distill key points from massive documents
  • •Cross-file Analysis: Understand how different parts relate to each other

When NOT to Use

  • •Small documents (< 50K tokens) - use Claude directly instead
  • •When detailed code editing is needed - Claude is better for precise changes
  • •Real-time or interactive tasks - prefer Claude's faster response
  • •When summarization is not the primary goal

Best Practices

  • •Always use gemini-1.5-pro model for long context tasks
  • •Structure the prompt clearly: "Given the following [codebase/document/logs], [task]"
  • •Include file paths or section markers in content for better reference
  • •Ask for structured output (headings, lists) for easier parsing
  • •For very large content, consider if you truly need ALL of it or can filter first

Content Formatting

When aggregating multiple files, use clear delimiters:

code
=== FILE: path/to/file1.py ===
[file content]

=== FILE: path/to/file2.py ===
[file content]

This helps Gemini maintain file context in its analysis.