linkedin-job-post-to-buyer-pain-map

Name: linkedin-job-post-to-buyer-pain-map
Author: Varnan-Tech

21installs59forks

How to install linkedin-job-post-to-buyer-pain-map?

varnan-tech/opendirectory/linkedin-job-post-to-buyer-pain-map

$npx -y skills add Varnan-Tech/opendirectory --skill linkedin-job-post-to-buyer-pain-map

Installs into the current project.

Summary

Takes pasted LinkedIn job posts or hiring descriptions and converts them into a structured buyer pain map with inferred pains, capability gaps, buy-vs-build signal, account priority scores, and suggested outreach angles. Use when asked to analyze hiring posts, decode job descript

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SKILL.md

1	# LinkedIn Job Post to Buyer Pain Map
2
3	Take LinkedIn job posts. Decode them into a structured buyer pain map with scores, pains, and outreach angles.
4
5	---
6
7	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.
8
9	Ethical rule: Do not infer personal attributes or protected characteristics about candidates. Focus strictly on company-level operational pain and organizational needs.
10
11	---
12
13	## Step 1: Setup Check
14
15	Confirm required env vars:
16
17	```bash
18	echo "GEMINI_API_KEY: ${GEMINI_API_KEY:+set}"
19	```
20
21	If GEMINI_API_KEY is missing:
22	Stop. Tell the user: "GEMINI_API_KEY is required. Get it at aistudio.google.com. Add it to your .env file."
23
24	---
25
26	## Step 2: Collect Inputs
27
28	The skill needs 3 required inputs. Collect them before proceeding.
29
30	### 2a: Product Brief
31
32	Ask: "Describe your product in 2-5 sentences. What do you do, what is your core value prop, and who do you target?"
33
34	If the user already included this in their prompt: Extract it. Confirm: "Product brief captured: [summary]."
35
36	### 2b: ICP Description
37
38	Ask: "Describe your ideal customer profile in 2-6 bullets: industries, company sizes, roles you sell to, tech stack hints."
39
40	If the user already included this in their prompt: Extract it. Confirm: "ICP captured: [summary]."
41
42	### 2c: Hiring Posts
43
44	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."
45
46	Accepted formats:
47	- Raw pasted text with company name and job title clearly labeled
48	- Structured JSON objects with fields: `company_name`, `job_title`, `location` (optional), `seniority` (optional), `team_or_function` (optional), `job_description_text`, `job_url` (optional)
49	- Multiple posts separated by clear delimiters (--- or numbered)
50
51	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.
52
53	### 2d: Optional Inputs
54
55	If the user provides any of these, capture them:
56	- `account_notes`: additional context per company (funding, tech stack, known tools, contacts)
57	- `focus_dimension`: "pipeline" (bias toward scoring and prioritization), "positioning" (bias toward messaging angles), or "both" (default)
58
59	---
60
61	## Step 3: Extract Signals
62
63	For each job post, parse and extract:
64
65	1. Team / function: Which team is this role on? (GTM, Product, Infra, Data, RevOps, CS, Engineering, etc.)
66	2. Seniority: IC, Senior IC, Manager, Director, VP, C-level
67	3. Responsibilities emphasis: Classify the dominant mode:
68	- Fire-fighting: "stabilize", "fix", "reduce downtime", "unblock"
69	- Building new: "build from scratch", "0→1", "greenfield", "design and implement"
70	- Optimizing: "scale", "optimize", "improve efficiency", "automate"
71	- Replacing: "replace legacy", "migrate from", "modernize"
72	4. Requirement language: Note keywords that signal intent: "first X hire", "critical role", "immediate", "must have"
73	5. Tool / stack hints: Any references to specific tools, platforms, or technology categories that overlap with the user's product area
74
75	Group by company. If multiple posts come from the same company, group their signals together.
76
77	State: "Extracted signals from X posts across Y companies."
78
79	---
80
81	## Step 4: Score with the LLM
82
83	Read `references/scoring-rubric.md` for the full scoring model.
84
85	Build the LLM request:
86
87	```bash
88	cat > /tmp/pain-map-score-request.json << 'ENDJSON'
89	{
90	"system_instruction": {
91	"parts": [{
92	"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) overal

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linkedin-job-post-to-buyer-pain-map

21installs59forks

View on GitHub

How to install linkedin-job-post-to-buyer-pain-map?

varnan-tech/opendirectory/linkedin-job-post-to-buyer-pain-map

$npx -y skills add Varnan-Tech/opendirectory --skill linkedin-job-post-to-buyer-pain-map

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# LinkedIn Job Post to Buyer Pain Map
2
3	Take LinkedIn job posts. Decode them into a structured buyer pain map with scores, pains, and outreach angles.
4
5	---
6
7	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.
8
9	Ethical rule: Do not infer personal attributes or protected characteristics about candidates. Focus strictly on company-level operational pain and organizational needs.
10
11	---
12
13	## Step 1: Setup Check
14
15	Confirm required env vars:
16
17	```bash
18	echo "GEMINI_API_KEY: ${GEMINI_API_KEY:+set}"
19	```
20
21	If GEMINI_API_KEY is missing:
22	Stop. Tell the user: "GEMINI_API_KEY is required. Get it at aistudio.google.com. Add it to your .env file."
23
24	---
25
26	## Step 2: Collect Inputs
27
28	The skill needs 3 required inputs. Collect them before proceeding.
29
30	### 2a: Product Brief
31
32	Ask: "Describe your product in 2-5 sentences. What do you do, what is your core value prop, and who do you target?"
33
34	If the user already included this in their prompt: Extract it. Confirm: "Product brief captured: [summary]."
35
36	### 2b: ICP Description
37
38	Ask: "Describe your ideal customer profile in 2-6 bullets: industries, company sizes, roles you sell to, tech stack hints."
39
40	If the user already included this in their prompt: Extract it. Confirm: "ICP captured: [summary]."
41
42	### 2c: Hiring Posts
43
44	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."
45
46	Accepted formats:
47	- Raw pasted text with company name and job title clearly labeled
48	- Structured JSON objects with fields: `company_name`, `job_title`, `location` (optional), `seniority` (optional), `team_or_function` (optional), `job_description_text`, `job_url` (optional)
49	- Multiple posts separated by clear delimiters (--- or numbered)
50
51	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.
52
53	### 2d: Optional Inputs
54
55	If the user provides any of these, capture them:
56	- `account_notes`: additional context per company (funding, tech stack, known tools, contacts)
57	- `focus_dimension`: "pipeline" (bias toward scoring and prioritization), "positioning" (bias toward messaging angles), or "both" (default)
58
59	---
60
61	## Step 3: Extract Signals
62
63	For each job post, parse and extract:
64
65	1. Team / function: Which team is this role on? (GTM, Product, Infra, Data, RevOps, CS, Engineering, etc.)
66	2. Seniority: IC, Senior IC, Manager, Director, VP, C-level
67	3. Responsibilities emphasis: Classify the dominant mode:
68	- Fire-fighting: "stabilize", "fix", "reduce downtime", "unblock"
69	- Building new: "build from scratch", "0→1", "greenfield", "design and implement"
70	- Optimizing: "scale", "optimize", "improve efficiency", "automate"
71	- Replacing: "replace legacy", "migrate from", "modernize"
72	4. Requirement language: Note keywords that signal intent: "first X hire", "critical role", "immediate", "must have"
73	5. Tool / stack hints: Any references to specific tools, platforms, or technology categories that overlap with the user's product area
74
75	Group by company. If multiple posts come from the same company, group their signals together.
76
77	State: "Extracted signals from X posts across Y companies."
78
79	---
80
81	## Step 4: Score with the LLM
82
83	Read `references/scoring-rubric.md` for the full scoring model.
84
85	Build the LLM request:
86
87	```bash
88	cat > /tmp/pain-map-score-request.json << 'ENDJSON'
89	{
90	"system_instruction": {
91	"parts": [{
92	"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) overal

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6 picks

Type