finfocus: Skill for Claude Code

jqschema - JSON Schema Discovery

Use ./.github/skills/jqschema/jqschema.sh directly from the repository skill folder to discover complex JSON structure.

Purpose

Generate a compact structural schema (keys + types) from JSON input. Use it when:

• Analyzing tool outputs from GitHub search (search_code, search_issues, search_repositories)
• Exploring API responses with large payloads
• Understanding the structure of unfamiliar data without verbose output
• Planning queries before fetching full data

Usage

# Analyze a file
cat data.json | ./.github/skills/jqschema/jqschema.sh

# Analyze command output
echo '{"name": "test", "count": 42, "items": [{"id": 1}]}' | ./.github/skills/jqschema/jqschema.sh

# Analyze GitHub search results
gh api search/repositories?q=language:go | ./.github/skills/jqschema/jqschema.sh

How It Works

The script transforms JSON data by:

1. Replacing object values with their type names ("string", "number", "boolean", "null")
2. Reducing arrays to their first element's structure (or empty array if empty)
3. Recursively processing nested structures
4. Outputting compact (minified) JSON

Example

Input:

{
  "total_count": 1000,
  "items": [
    {"login": "user1", "id": 123, "verified": true},
    {"login": "user2", "id": 456, "verified": false}
  ]
}

Output:

{"total_count":"number","items":[{"login":"string","id":"number","verified":"boolean"}]}

Best Practices

Use this script when:

• You need to understand the structure of tool outputs before requesting full data
• GitHub search tools return large datasets (use perPage: 1 and pipe through schema minifier first)
• Exploring unfamiliar APIs or data structures
• Planning data extraction strategies

Example workflow for GitHub search tools:

# Step 1: Get schema with minimal data (fetch just 1 result)
# This helps understand the structure before requesting large datasets
echo '{}' | gh api search/repositories -f q="language:go" -f per_page=1 | ./.github/skills/jqschema/jqschema.sh

# Output shows the schema:
# {"incomplete_results":"boolean","items":[{...}],"total_count":"number"}

# Step 2: Review schema to understand available fields

# Step 3: Request full data with confidence about structure
# Now you know what fields are available and can query efficiently