Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Communication & Collaboration
AEO Analytics Free
Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks me...
skill openclawclawhub Free
Track AI visibility — measure whether a brand is mentioned and cited by AI assistants (Gemini, ChatGPT, Perplexity) for target prompts. Runs scans, tracks me...
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/data-schema.md, references/gemini-grounding.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- psyduckler
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
AEO Analytics (Free)
Source: github.com/psyduckler/aeo-skills Part of: AEO Skills Suite — Prompt Research → Content → Analytics Track whether AI assistants mention and cite your brand — and how that changes over time.
Requirements
Primary: Gemini API key (free from aistudio.google.com) — enables grounding with source data Fallback: web_search only — weaker signal but zero API keys needed web_fetch — optional, for deeper analysis of cited pages
Input
Domain (required) — the brand's website (e.g., tabiji.ai) Brand names (required) — names to search for in responses (e.g., ["tabiji", "tabiji.ai"]) Prompts (required for first scan) — list of target prompts to track. Can come from aeo-prompt-research-free output. Data file path (optional) — where to store scan history. Default: aeo-analytics/<domain>.json
Commands
The skill supports three commands:
scan — Run a new visibility scan
Execute all tracked prompts against the AI model and record results.
report — Generate a visibility report
Analyze accumulated scan data and produce a formatted report.
add-prompts / remove-prompts — Manage tracked prompts
Add or remove prompts from the tracking list.
Step 1: Load or Initialize Data
Check if a data file exists for this domain. If yes, load it. If no, create a new one. See references/data-schema.md for the full JSON schema.
Step 2: Run Prompts
For each tracked prompt: Method A — Gemini API with grounding (preferred): See references/gemini-grounding.md for API details. Send prompt to Gemini API with googleSearch tool enabled From the response, extract: Response text — the AI's answer Grounding chunks — the web sources cited (URLs + titles) Web search queries — what the AI searched for Analyze the response: Mentioned? — Search response text for brand names (case-insensitive, word-boundary match) Mention excerpt — Extract the sentence(s) containing the brand name Cited? — Check if brand's domain appears in any grounding chunk URI Cited URLs — List the specific brand URLs cited Sentiment — Classify the mention context as positive/neutral/negative Competitors — Extract other brand names and domains from response + citations Method B — Web search fallback (if no Gemini API key): web_search the exact prompt text Check if brand's domain appears in search results Record as "web-proxy" method (less direct than grounding)
Step 3: Save Results
Append the scan results to the data file. Never overwrite previous scans — history is the whole point.
Step 4: Quick Summary
After scanning, output a brief summary: Prompts scanned Current mention rate and citation rate Change vs. last scan (if applicable) Any notable changes (new mentions, lost citations)
Per-Prompt Detail
For each tracked prompt, show: 1. "[prompt text]" Scans: [total] (since [first scan date]) Mentioned: [count]/[total] ([%]) — [trend arrow] [trend description] Cited: [count]/[total] ([%]) Latest: [✅/❌ Mentioned] + [✅/❌ Cited] Sentiment: [positive/neutral/negative] Competitors mentioned: [list] If mentioned in latest scan, include the mention excerpt. If not mentioned, note which sources were cited instead and rate the opportunity (HIGH/MEDIUM/LOW).
Summary Section
VISIBILITY SCORE Brand mentioned: [X]/[total] prompts ([%]) in latest scan Brand cited: [X]/[total] prompts ([%]) in latest scan TRENDS (last [N] days, [N] scans) Mention rate: [%] → [trend] Citation rate: [%] → [trend] Most improved: [prompt] ([old rate] → [new rate]) Most volatile: [prompt] (mentioned [X]/[N] scans) Consistently absent: [list of prompts never mentioned] COMPETITOR SHARE OF VOICE [Competitor 1] — mentioned in [X]/[total] prompts [Competitor 2] — mentioned in [X]/[total] prompts [Brand] — mentioned in [X]/[total] prompts NEXT ACTIONS → [Prioritized recommendations based on gaps and trends]
Recommendations Logic
High opportunity: Prompt has 0% mention rate + no strong owner in citations → create content Close to winning: Prompt has mentions but no citations → refresh content for citation-worthiness Volatile: Mention rate between 20-60% → content exists but needs strengthening Won: Mention rate >80% + citation rate >50% → maintain, monitor for decay
Data Management
Data file location: aeo-analytics/<domain>.json Schema: see references/data-schema.md Each scan appends to the scans array — never delete history Prompts can be added/removed without affecting historical data When adding new prompts, they start with 0 scans (no backfill)
Tips
Run scans at consistent intervals (weekly or biweekly) for meaningful trend data After publishing new AEO content, wait 2-4 weeks for indexing before expecting changes Gemini's grounding results can vary run-to-run — that's normal. Aggregate data over multiple scans is more reliable than any single result Track 10-20 prompts max for a focused view. Too many dilutes the signal This skill completes the AEO loop: Research (aeo-prompt-research-free) → Create/Refresh (aeo-content-free) → Measure (this skill) → repeat
Category context
Messaging, meetings, inboxes, CRM, and teammate communication surfaces.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
3 Docs
- SKILL.md
- references/data-schema.md
- references/gemini-grounding.md