canonry（canonry 搜索）

Canonry

Agent-first open-source AEO (Answer Engine Optimization) operating platform. Track how AI answer engines cite your domain across Gemini, ChatGPT, Claude, and Perplexity, then act on the signal through the content engine and integrations.

Website: ainyc.ai | Docs: github.com/AINYC/canonry

When to Use

• Tracking keyphrase citations across AI providers
• Running technical SEO audits (14‑factor scoring)
• Implementing structured data (JSON‑LD)
• Diagnosing indexing gaps via Google Search Console / Bing Webmaster Tools
• Optimizing llms.txt, sitemaps, robots.txt for AI crawlers
• Submitting URLs to Google Indexing API and Bing IndexNow
• Analyzing competitor citation patterns

Core Philosophy

• Measure outcomes — AI models are black boxes; track citations, don't assume causality
• Signal over noise — Focus on high‑intent queries; avoid granular targeting until base visibility exists
• CLI‑native — API‑driven changes over manual CMS clicks; faster, repeatable, auditable

Toolchain

canonry (AEO Operating Platform)

# List projects
canonry project list

# Run a sweep (all providers)
canonry run <project> --wait

# Check per‑phrase citation status
canonry evidence <project>

# Show latest run summary
canonry status <project>

# Add/remove keyphrases
canonry keyword add <project> "polyurea roof coating"
canonry keyword remove <project> "best roof coating for a warehouse"

# Submit URLs to Bing
canonry bing request-indexing <project> <url>

# Submit to Google Indexing API
canonry google request-indexing <project> <url>

aeo-audit (Technical SEO Analysis)

# Run audit (JSON output)
npx @ainyc/aeo-audit@latest "https://example.com" --format json

# 14‑factor scoring includes:
# - Structured Data (JSON‑LD)
# - Content Depth
# - AI‑Readable Content (llms.txt, llms‑full.txt)
# - E‑E‑A‑T Signals
# - FAQ Content
# - Citations & Authority Signals
# - Definition Blocks
# - Technical SEO (H1, alt text, meta)

Google Search Console / Bing WMT

# GSC coverage summary
canonry google coverage <project>

# Bing coverage summary  
canonry bing coverage <project>

# Force refresh cached data
canonry google refresh <project>
canonry bing refresh <project>

Workflow

1. Diagnose

# Baseline AEO visibility
canonry run <project> --wait
canonry evidence <project>

# Technical SEO audit
npx @ainyc/aeo-audit@latest "https://client.com" --format json > audit.json

2. Prioritize

Gaps sorted by impact:

1. Missing H1 → immediate content patch
2. No structured data → JSON‑LD injection
3. Thin content → definition blocks ("What is…")
4. County‑level targeting → refine after base visibility
5. E‑E‑A‑T signals → Person schema, author tags (needs client input)

3. Execute

• Schema injection: LocalBusiness + FAQPage JSON‑LD via site‑appropriate method (Elementor Custom Code, theme hooks, etc.)
• Content patches: H1, meta title/description, image alt text via REST API or CMS
• AI‑readable files: Upload llms.txt, llms‑full.txt to site root
• Indexing requests: Submit all URLs to Google Indexing API + Bing IndexNow
• Keyphrase strategy: Trim to 8‑12 high‑intent queries; remove noise

4. Monitor

• Weekly canonry sweeps to track citation changes
• Correlate visibility shifts with deployment dates
• Watch for competitor displacement in keyphrases

5. Report

Clear, data‑first summaries:

“Lost emergency dentist brooklyn on Gemini — two competitors moved in. Here’s what to fix.”

Common Patterns

New Site (0 citations)

• Focus on indexing first: submit sitemap to GSC/Bing, request indexing
• Implement base schema (LocalBusiness, Service)
• Create llms.txt with service‑area details
• Trim keyphrases to 8‑12 core queries
• Expect 4‑8 weeks for first citations

Established Site (regression)

• Compare canonry runs to identify when loss occurred
• Check for recent competitor content or site changes
• Validate schema is still present and error‑free
• Re‑submit affected URLs to indexing APIs

County‑Level Targeting

# Service areas in llms.txt / schema
Michigan:
  - Oakland County (Troy, Auburn Hills, Pontiac)
  - Macomb County (Sterling Heights, Shelby Township)
  - Wayne County (Detroit, Dearborn)
  - Lapeer County (HQ: Almont)

Florida:
  - Miami‑Dade County (Miami, Coral Gables)
  - Broward County (Fort Lauderdale, Hollywood)
  - Palm Beach County (West Palm Beach, Boca Raton)

• Reference counties in schema areaServed and llms.txt
• Do not create separate keyphrases per county until base visibility exists

WordPress/Elementor Specifics

• REST API user with Application Passwords (/wp‑json/wp/v2/)
• Elementor data patched via _elementor_data meta field
• Schema injection via Elementor Pro Custom Code (elementor_snippet CPT)
• Yoast SEO title/description fields often NOT REST‑writable → manual WP Admin edit
• wp‑login.php may be hidden (security plugin) → file uploads require manual WP File Manager

Example: Full AEO Audit + Action Plan

# 1. Audit
npx @ainyc/aeo-audit@latest "https://client.com" --format json > audit.json

# 2. Parse score
cat audit.json | jq '.overallScore, .overallGrade'

# 3. Check AEO baseline
canonry status client-project
canonry evidence client-project

# 4. Generate action list
cat audit.json | jq -r '.factors[] | select(.score < 70) | "- \(.name): \(.score)/100 (\(.grade)) - \(.recommendations[0])"'

Boundaries & Safety

• Never touch live WordPress without explicit approval
• Back up ~/.canonry/config.yaml before any config edit
• Never fabricate citation data — if a sweep hasn’t run, say so
• Client data stays private — canonry repo is public; no real domains in issues
• Respect API rate limits — batch operations, avoid tight loops

Tools: canonry v1.37+, @ainyc/aeo‑audit v1.3+ Website: ainyc.ai | Reference: AINYC AEO Methodology