GEO Pre-Sales Quick Hook#

📌 Skill Overview#

Pre-Sales Quick Hook is the first step in the GEO product sales pipeline, designed specifically for sales scenarios:

Sales rep has a target client + 1-2 target keywords → Quickly generate a competitive comparison card → Show the client how far behind they are → Create urgency → Trigger sign-up

Relationship with other tools:

• geo-quick-hook (this tool) = Pre-sales hook (create urgency, trigger sign-up intent)
• geo-brand-extractor = Pre-sales keyword selection (determine which keywords to target)
• geo-visibility-tracker = Post-sign-up baseline (full 48 questions, establish comparison starting point)
• geo-after-sale = Post-sale delivery (monthly progress reports)

Core visual: Competitive ranking chart with the client at the bottom, highlighted in red ⚠️ — instantly devastating.

Report naming convention: GEO_QuickHook_[BrandName]_5engines_[YYYYMMDD].html

---

🚀 Execution Flow (Three Questions + Sub-Agent Execution)#

Rule: After all three questions are confirmed, you must spawn a sub-agent to execute — the Main Brain does not run scripts directly.

Step 1: First Question#

text

Got it, launching pre-sales hook analysis! 🎯

① What is the target client's brand name?

⏸️ Wait for answer

---

Step 2: Second Question#

text

Got it! ② Who are the competitors? We recommend 5-8 top industry names.
(The bigger the competitors, the more impactful the contrast!)

⏸️ Wait for answer

---

Step 3: Third Question#

text

③ What are the target keywords? 1-2 is ideal — focus the firepower.
(These are the keywords the sales rep is pitching to this client.)

⏸️ Wait for answer, then spawn sub-agent to execute

---

Step 4: Spawn Sub-Agent#

Sub-agent execution command:

bash

python3 <skill_dir>/scripts/quick_hook.py \
  --brand "[BrandName]" \
  --competitors "[Comp1,Comp2,Comp3...]" \
  --keywords "[keyword1,keyword2]"

Environment variables must be set in advance:

bash

export LLM_API_KEY="your-api-key-here"
export LLM_BASE_URL="https://api.openai.com/v1"
export LLM_MODEL="gpt-4o"

After the report is generated, screenshot and send via Feishu (html-to-feishu standard flow):

text

HTML_FILE=$(ls -t ~/Desktop/GEO_QuickHook_*.html | head -1)
ENCODED=$(python3 -c "import urllib.parse,os; print(urllib.parse.quote(os.path.basename('$HTML_FILE')))")
pkill -f "http.server 18899" 2>/dev/null
python3 -m http.server 18899 --directory ~/Desktop &
SERVER_PID=$!
for i in 1 2 3 4 5; do
  STATUS=$(curl -s -o /dev/null -w "%{http_code}" "http://localhost:18899/" 2>/dev/null)
  if [ "$STATUS" = "200" ]; then break; fi
  sleep 1
done
browser(action="open", profile="openclaw", url="http://localhost:18899/$ENCODED") → targetId
browser(action="screenshot", profile="openclaw", targetId=targetId, fullPage=True, type="jpeg") → img_path
local_path = img_path.replace("MEDIA:", "")  # strip prefix to get local path
message(action="send", channel="feishu", target="user:YOUR_FEISHU_OPEN_ID",
        message="⚡ [BrandName] Pre-Sales Hook Report — Competitive ranking at a glance!")
message(action="send", channel="feishu", target="user:YOUR_FEISHU_OPEN_ID",
        media=local_path)
kill $SERVER_PID 2>/dev/null

---

📊 Output Description#

ModuleContentCoverBrand name + 5 engines + dateComparison card (per keyword)Brand × engine matrix + combined average bar chart + fatal conclusionCitation comparison rowWhether competitors appear as citations (✅ cited / - listed only) + citation warning textBottom hook"Want to learn how to change this?" (fixed copy)

---

🔧 Technical Details#

Script path: skills/geo-quick-hook/scripts/quick_hook.py

5 engines: Qwen / Doubao / DeepSeek / Kimi / Ernie (parallel collection)

Note: In the open-source version, all engines share the same LLM_API_KEY / LLM_BASE_URL / LLM_MODEL environment variables. To connect each engine to its own independent API, configure separate environment variables in ENGINE_MAP.

Usage example:

bash

export LLM_API_KEY="sk-xxxx"
export LLM_BASE_URL="https://api.openai.com/v1"
export LLM_MODEL="gpt-4o"

python3 quick_hook.py \
  --brand "Brand X" \
  --competitors "CompA,CompB,CompC,CompD,CompE" \
  --keywords "keyword1,keyword2"