Analyzes pasted LinkedIn job posts to create structured buyer pain maps with inferred pains, capability gaps, buy-vs-build signals, account priority scores, and outreach angles.
npx claudepluginhub varnan-tech/opendirectory --plugin opendirectory-gtm-skillsThis skill uses the workspace's default tool permissions.
Take LinkedIn job posts. Decode them into a structured buyer pain map with scores, pains, and outreach angles.
Audits job postings for quality, realism, internal consistency, and market alignment using a 100-point scoring rubric identifying red flags and unrealistic expectations.
Analyzes job postings to extract requirements/keywords, calculate resume match scores (70-90% targets), identify skill gaps/red flags, and create application strategies.
Identifies potential B2B clients matching service definitions and ideal client profiles using industry, size, location filters and 10-point fit scoring. Outputs prospects to Markdown files.
Share bugs, ideas, or general feedback.
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.
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."
The skill needs 3 required inputs. Collect them before proceeding.
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]."
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]."
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:
company_name, job_title, location (optional), seniority (optional), team_or_function (optional), job_description_text, job_url (optional)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.
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)For each job post, parse and extract:
Group by company. If multiple posts come from the same company, group their signals together.
State: "Extracted signals from X posts across Y companies."
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:
references/scoring-rubric.mdcompany_name, headline, overall_score, score_breakdown (signal_strength, urgency, icp_fit — each with score and explanation), stage_guess, buy_vs_build_guessFor 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:
buyer_pain_map (primary_pains, secondary_pains) and recommended_outreach_anglesRead references/examples.md to calibrate the expected output quality and depth.
For each account, generate a structured handoff block that outreach-sequence-builder can consume directly:
handoff:
for_outreach_sequence_builder:
account_summary: 2-3 sentence recap of the company's situation and hiring context
key_pain: The single most actionable pain in one sentence
suggested_personas: 2-4 job titles of the people most likely to own this pain
tone: One sentence describing the recommended outreach tone
The handoff must be concrete enough that someone could paste it into outreach-sequence-builder's prompt and get a usable sequence without re-researching the account.
Run every check and fix violations before presenting:
round((0.4 × signal + 0.3 × urgency + 0.3 × icp_fit) × 10)Fix any violation before presenting.
Present the full analysis in this format:
## Buyer Pain Map — [YYYY-MM-DD]
**Product:** [product name from brief]
**Posts analyzed:** X posts across Y companies
**Focus:** [pipeline / positioning / both]
---
### [Company Name] — Score: [N]/100
**Headline:** [one-line summary of what the hiring reveals]
| Dimension | Score | Explanation |
|-----------|-------|-------------|
| Signal Strength | N/10 | [one sentence] |
| Urgency | N/10 | [one sentence] |
| ICP Fit | N/10 | [one sentence] |
**Stage:** [stage guess] | **Buy-vs-Build:** [posture label]
#### Primary Pains
- **[Pain label]:** [1-3 sentence description]
- Evidence: "[quoted phrase from job post]"
#### Secondary Pains
- **[Pain label]:** [1-3 sentence description]
- Evidence: "[quoted phrase from job post]"
#### Recommended Outreach Angles
1. **[Angle name]:** [narrative]
- [bullet 1]
- [bullet 2]
- [bullet 3]
#### Handoff → outreach-sequence-builder
> **Summary:** [2-3 sentences]
> **Key pain:** [one sentence]
> **Suggested personas:** [list]
> **Tone:** [one sentence]
---
[repeat for each company, ordered by overall_score descending]
mkdir -p docs/pain-maps
OUTFILE="docs/pain-maps/$(date +%Y-%m-%d).md"
cat > "$OUTFILE" << 'EOF'
REPORT_CONTENT_HERE
EOF
echo "Pain map saved to $OUTFILE"
If multiple companies are analyzed, also save individual files using slugified company names (lowercase, replace spaces and special characters with hyphens, strip trailing hyphens):
e.g., "ACME, Inc." → acme-inc.md, "CoolStartup Inc" → coolstartup-inc.md
SLUG=$(echo "COMPANY_NAME" | tr '[:upper:]' '[:lower:]' | sed 's/[^a-z0-9]/-/g' | sed 's/--*/-/g' | sed 's/-$//')
cat > "docs/pain-maps/${SLUG}.md" << 'EOF'
INDIVIDUAL_ACCOUNT_CONTENT_HERE
EOF
linkedin-hiring-intent-scanner or yc-intent-radar-skill instead)outreach-sequence-builder instead — feed it the handoff from this skill)reddit-icp-monitor or twitter-GTM-find-skill instead)handoff.for_outreach_sequence_builder block directly as context when building a sequence.