jqschema

name	jqschema
description	JSON schema discovery utility that extracts structure and type information from JSON data
tools	{"bash":["jq *","/tmp/gh-aw/jqschema.sh","git"]}
steps	[{"name":"Setup jq utilities directory","run":"mkdir -p /tmp/gh-aw\ncp \"$GITHUB_WORKSPACE/.github/skills/jqschema/jqschema.sh\" /tmp/gh-aw/jqschema.sh\nchmod +x /tmp/gh-aw/jqschema.sh\n"}]

jqschema - JSON Schema Discovery

A utility script is available at /tmp/gh-aw/jqschema.sh to help you discover the structure of complex JSON responses.

Purpose

Generate a compact structural schema (keys + types) from JSON input. This is particularly useful 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 | /tmp/gh-aw/jqschema.sh

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

# Analyze GitHub search results
gh api search/repositories?q=language:go | /tmp/gh-aw/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 | /tmp/gh-aw/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

Using with GitHub MCP tools: When using tools like search_code, search_issues, or search_repositories, pipe the output through jqschema to discover available fields:

# Save a minimal search result to a file
gh api search/code -f q="jq in:file language:bash" -f per_page=1 > /tmp/sample.json

# Generate schema to understand structure
cat /tmp/sample.json | /tmp/gh-aw/jqschema.sh

# Now you know which fields exist and can use them in your analysis