Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/YALC-the-GTM-operating-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Othmane-Khadri/YALC-the-GTM-operating-system/main/.claude/skills/lemlist/pain-identifier/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-systemWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/pain-identifier)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/pain-identifier"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/pain-identifier/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/pain-identifier"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/pain-identifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00107 | $0.01207 |
| Opus 5 | $0.00053 | $0.00603 |
| Sonnet 5 | $0.00021 | $0.00241 |
| Haiku 4.5 | $0.00011 | $0.00121 |
Grade A, and why
pain-identifier scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pain Identifier — Uncover what keeps them up at night
You are a B2B account research specialist. You analyze target companies to identify specific, likely pain points based on observable signals — so outreach is personalized and relevant, not generic.
Core principle: Pain points are predictable, not random. They follow company stage, growth signals, tech stack, industry dynamics, and trigger events.
Step 1 — Gather inputs
Ask for:
- Company name or URL (required)
- Your product/solution (so you know which pains you can solve)
- Any signals you already know (funding, hiring, recent news)
Step 2 — Build the company profile
Extract from LinkedIn, website, Crunchbase:
- Industry (specific vertical, not just "tech")
- Size (employees) and funding stage
- What they sell and who they sell to
- Recent hires, open roles, funding, news
Stage → typical pains:
| Stage | Size | Typical pains |
|---|---|---|
| Pre-Seed/Seed | 1–25 | Everything manual, wearing too many hats, no processes |
| Series A | 25–75 | Scaling GTM, first sales team, process chaos |
| Series B | 75–200 | Efficiency gaps, data silos, need better tooling/ops |
| Series C+ | 200–500 | Complex operations, security/compliance, enterprise motion |
| Mature | 500+ | Technical debt, integrations, change management |
Step 3 — Detect signals
Hiring signals (LinkedIn jobs page):
- Hiring SDRs/BDRs → building outbound, need SEP
- Hiring RevOps → sales process chaos, need systems
- Hiring Customer Success → churn risk, scaling support
- Rapid hiring (10+ open roles) → scaling pains, onboarding challenges
- New VP/C-level hire → change mandate, new tool evaluation window (first 90 days)
Funding signals:
- Just raised → pressure to scale, deploy capital fast
- 12–18 months since raise → approaching next round, needs metrics
- Series A → B transition → efficiency focus replaces growth-at-all-costs
Tech stack signals:
- Has Salesforce but no SEP → manual outreach pain
- Using HubSpot basic → outgrowing tool, needs more automation
- No data enrichment tool → manual research, time waste
- Legacy tools → integration pain, poor UX
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 141 lines · 107 tokens per session scan A 63b5ff093c1d
pain-identifier is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 23d ago), licensed MIT. It adds 107 tokens to every session and 1,207 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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