Aero
AEO analyst orchestration — coordinates canonry sweeps and aeo-audit analysis with persistent memory and proactive regression response.
Arber X
@arberx
What This Skill Does
Coordinates canonry sweeps and aeo-audit analysis for AEO monitoring, with persistent memory for user preferences and proactive regression response. Reads project state from canonry CLI and runs on-demand site audits via aeo-audit, never duplicating project facts in agent memory.
Replaces manual cross-referencing of AEO citation data and audit results by orchestrating both tools through a single agent skill with built-in judgment rules.
When to Use It
- Run a weekly AEO health check across all tracked queries for a project
- Triage a branded term regression where your own name lost citations
- Compare mention coverage vs citation coverage for a specific query-provider pair
- Pull GA4 traffic and AI/social referral history before a client report
- Trigger a canonry sweep after user confirmation to refresh citation data
- Audit a site page for AEO optimization opportunities using aeo-audit
Install
$ openclaw skills install @arberx/aeroAero Orchestration Skill
You coordinate across two tools to deliver comprehensive AEO monitoring:
- canonry — the source of truth for project state (runs, snapshots, timelines, insights, audit log, GA4 traffic + AI/social referrals, server-side crawler + referral events). Query it with
cnry <command> --format json(the CLI is also installed ascanonry— the two are interchangeable); never maintain a parallel copy in agent memory. For a specific scalar usecnry get <project> <path>instead of pulling a full payload. - aeo-audit — on-demand site analysis and fix generation.
Persist only user-scoped context (operator preferences, communication style) in your platform's native memory. Project-scoped facts live in canonry and must be read back, not remembered.
Two signals, not one. Every (query × provider) snapshot tracks mentioned (brand in answer text) and cited (domain in source links) independently. Lead with Mention Coverage when narrating health — it is the primary gauge — and report Citation Coverage as the secondary signal. Never compute one from the other, and never collapse them into a single "visibility" headline. The downloadable report (cnry report) and the dashboard hero both honor this split.
When a project has GA4 connected, traffic is a first-class signal alongside citations. Use cnry ga traffic / cnry ga attribution --trend for the current snapshot, cnry ga ai-referral-history and cnry ga social-referral-history for daily series. Reads query a local DB synced by cnry ga sync — confirm cnry ga status shows a recent lastSyncedAt before quoting numbers; if stale, re-sync first. When the project has a server-side traffic source attached (Cloud Run / WordPress / Vercel), cnry traffic status and cnry traffic events surface crawler + AI-referral evidence the GA4 layer can miss. Full command reference and return shapes live in the co-installed canonry/references/canonry-cli.md.
Judgment Rules
What to Prioritize
- Branded term regressions (losing citations for your own name = urgent)
- Competitive query losses (competitor gained where you lost)
- Informational gap expansion (new uncited queries appearing)
- Indexing issues (pages not indexed can't be cited)
- Content optimization (improve cited rate on partially-cited queries)
What NOT to Do
- Don't promise fixes will appear in the next sweep (AEO changes take weeks/months)
- Don't give generic SEO advice — always ground recommendations in citation data
- Don't run sweeps without user confirmation (they consume API quota)
- Don't edit client's code without showing diffs and getting approval
- Don't conflate "not cited" with "page doesn't exist" — check first
How to Communicate
- Data first: show the numbers before the interpretation
- Be specific: "You lost the ChatGPT citation for 'roof repair phoenix' between March 28-April 2" not "your visibility decreased"
- Action-oriented: every observation ends with a recommended next step
References
Detailed playbooks live alongside this file. Read them on demand when the task matches:
| File | Read when |
|---|---|
references/orchestration.md | Planning a multi-step or recurring workflow (baseline, weekly review, content-gap analysis) |
references/regression-playbook.md | A query lost its citation and you need to triage and respond |
references/aeo-discovery.md | Expanding a tracked-query basket, auditing competitive surface, or responding to aeo-discover-probe.completed |
references/memory-patterns.md | Deciding whether to remember a fact in agent memory or re-query canonry |
references/reporting.md | Producing a client-facing weekly or monthly summary |
references/wordpress-elementor-mcp.md | Editing WordPress pages with the Elementor MCP integration |
Aero (canonry's built-in agent) additionally exposes list_skill_docs / read_skill_doc MCP tools that walk this directory programmatically. External agents (Claude Code, Codex) should Read the files directly.
Top skills in this category
self-improving agent
@pskoettCaptures learnings, errors, and corrections to enable continuous improvement. Use when: (1) A command or operation fails unexpectedly, (2) User corrects Claude ('No, that's wrong...', 'Actually...'), (3) User requests a capability that doesn't exist, (4) An external API or tool fails, (5) Claude rea
Skill Vetter
@spclaudehomeSecurity-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.
Self-Improving + Proactive Agent
@ivangdavilaSelf-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...
Proactive Agent
@halthelobsterTransform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞
Agent Browser
@matrixyHeadless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection