Linkedin Job Post To Buyer Pain Map

LinkedIn Job Post to Buyer Pain Map

Take LinkedIn job posts. Decode them into a structured buyer pain map with scores, pains, and outreach angles.

Critical rule: Every inferred pain must cite specific language from the job description that supports it. Never hallucinate pains that are not grounded in the text. If a post is too generic to infer pain, say so explicitly and assign a low signal strength score.

Ethical rule: Do not infer personal attributes or protected characteristics about candidates. Focus strictly on company-level operational pain and organizational needs.

Step 1: Setup Check

Confirm required env vars:

echo "GEMINI_API_KEY: ${GEMINI_API_KEY:+set}"

If GEMINI_API_KEY is missing: Stop. Tell the user: "GEMINI_API_KEY is required. Get it at aistudio.google.com. Add it to your .env file."

Step 2: Collect Inputs

The skill needs 3 required inputs. Collect them before proceeding.

2a: Product Brief

Ask: "Describe your product in 2-5 sentences. What do you do, what is your core value prop, and who do you target?"

If the user already included this in their prompt: Extract it. Confirm: "Product brief captured: [summary]."

2b: ICP Description

Ask: "Describe your ideal customer profile in 2-6 bullets: industries, company sizes, roles you sell to, tech stack hints."

If the user already included this in their prompt: Extract it. Confirm: "ICP captured: [summary]."

2c: Hiring Posts

Ask: "Paste the job descriptions you want analyzed. For each post, include the company name, job title, and the full description text. You can paste 1-15 posts."

Accepted formats:

• Raw pasted text with company name and job title clearly labeled
• Structured JSON objects with fields: company_name, job_title, location (optional), seniority (optional), team_or_function (optional), job_description_text, job_url (optional)
• Multiple posts separated by clear delimiters (--- or numbered)

If any field is missing: Infer what you can from the description text. If company_name or job_description_text is missing, ask for it before proceeding.

2d: Optional Inputs

If the user provides any of these, capture them:

• account_notes: additional context per company (funding, tech stack, known tools, contacts)
• focus_dimension: "pipeline" (bias toward scoring and prioritization), "positioning" (bias toward messaging angles), or "both" (default)

Step 3: Extract Signals

For each job post, parse and extract:

1. Team / function: Which team is this role on? (GTM, Product, Infra, Data, RevOps, CS, Engineering, etc.)
2. Seniority: IC, Senior IC, Manager, Director, VP, C-level
3. Responsibilities emphasis: Classify the dominant mode:
   • Fire-fighting: "stabilize", "fix", "reduce downtime", "unblock"
   • Building new: "build from scratch", "0→1", "greenfield", "design and implement"
   • Optimizing: "scale", "optimize", "improve efficiency", "automate"
   • Replacing: "replace legacy", "migrate from", "modernize"
4. Requirement language: Note keywords that signal intent: "first X hire", "critical role", "immediate", "must have"
5. Tool / stack hints: Any references to specific tools, platforms, or technology categories that overlap with the user's product area

Group by company. If multiple posts come from the same company, group their signals together.

State: "Extracted signals from X posts across Y companies."

Step 4: Score with the LLM

Read references/scoring-rubric.md for the full scoring model.

Build the LLM request:

cat > /tmp/pain-map-score-request.json << 'ENDJSON'
{
  "system_instruction": {
    "parts": [{
      "text": "You are a GTM analyst who specializes in decoding hiring signals into buyer intent. For each account provided, you will score three dimensions and infer company context. Rules: (1) Every score must include a one-sentence plain-text explanation. (2) signal_strength measures how many and how specific the hiring signals are relative to the user's product area. (3) urgency measures how time-sensitive the hiring need appears. (4) icp_fit measures how closely the company matches the user's ICP description. (5) Each score is 1-10. (6) overall_score = round((0.4 * signal_strength + 0.3 * urgency + 0.3 * icp_fit) * 10). (7) Infer buy_vs_build from job language. Use EXACTLY one of these labels: 'Leaning build', 'Leaning buy', 'Hybrid (buy-and-build)', 'Unknown'. (8) Infer stage_guess from company clues: funding stage, employee count, company type. (9) Output valid JSON only."
    }]
  },
  "contents": [{
    "parts": [{
      "text": "SCORING_CONTEXT_HERE"
    }]
  }],
  "generationConfig": {
    "temperature": 0.2,
    "maxOutputTokens": 4096
  }
}
ENDJSON
curl -s -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/pain-map-score-request.json \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['candidates'][0]['content']['parts'][0]['text'])"

Replace SCORING_CONTEXT_HERE with:

• The product brief and ICP description from Step 2
• The extracted signals per company from Step 3
• The scoring rubric rules from references/scoring-rubric.md
• Instructions to output JSON with: company_name, headline, overall_score, score_breakdown (signal_strength, urgency, icp_fit — each with score and explanation), stage_guess, buy_vs_build_guess

Step 5: Build Pain Map

For each account, use the extracted signals and LLM scoring context to build the buyer pain map.

cat > /tmp/pain-map-analysis-request.json << 'ENDJSON'
{
  "system_instruction": {
    "parts": [{
      "text": "You are a B2B pain analyst who reads job descriptions and identifies the operational pains a company is experiencing. Rules: (1) Every pain must have a short label, a 1-3 sentence description, and specific phrases quoted from the job post as supporting evidence. (2) Classify pains as primary (directly relevant to the user's product) or secondary (real pain but tangential to the product). (3) If a post is too generic to infer specific pain, state that explicitly — do not hallucinate. (4) For each account, also generate 1-3 recommended outreach angles. Each angle has: angle_name (short), narrative (1-2 sentences on how to lead), and talk_track_bullets (2-4 concrete talking points). (5) Outreach angles must reference specific pains and evidence, not generic value props. No banned words: synergy, leverage, innovative, cutting-edge, best-in-class, world-class, game-changing, disruptive, seamless, robust, comprehensive, revolutionize, transform, streamline. (6) Output valid JSON only."
    }]
  },
  "contents": [{
    "parts": [{
      "text": "ANALYSIS_CONTEXT_HERE"
    }]
  }],
  "generationConfig": {
    "temperature": 0.4,
    "maxOutputTokens": 8192
  }
}
ENDJSON
curl -s -X POST \
  "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -d @/tmp/pain-map-analysis-request.json \
  | python3 -c "import sys,json; d=json.load(sys.stdin); print(d['candidates'][0]['content']['parts'][0]['text'])"

Replace ANALYSIS_CONTEXT_HERE with:

• The product brief and ICP description
• Each company's job posts (full text)
• The scores and context from Step 4
• Account notes if provided
• The focus_dimension preference (pipeline / positioning / both)
• Instructions to output per-account: buyer_pain_map (primary_pains, secondary_pains) and recommended_outreach_angles

Read references/examples.md to calibrate the expected output quality and depth.