Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/gtm-engineer-playbook. 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/gtm-engineer-playbook/main/.claude/skills/gtm-playbook/account-research-brief/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/gtm-engineer-playbookWrote 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/gtm-engineer-playbook/account-research-brief)<a href="https://agentmods.dev/skills/othmane-khadri/gtm-engineer-playbook/account-research-brief"><img src="https://agentmods.dev/badge/skills/othmane-khadri/gtm-engineer-playbook/account-research-brief/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/gtm-engineer-playbook/account-research-brief"><img src="https://agentmods.dev/badge/skills/othmane-khadri/gtm-engineer-playbook/account-research-brief.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00063 | $0.01982 |
| Opus 5 | $0.00032 | $0.00991 |
| Sonnet 5 | $0.00013 | $0.00396 |
| Haiku 4.5 | $0.00006 | $0.00198 |
Grade A, and why
account-research-brief 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 12d 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Research Brief
Produce a comprehensive 1-page company intelligence brief in 2-3 minutes using WebSearch. Everything a GTM engineer needs to engage a target account — from company overview to ready-to-use outreach angles.
Steps
1. Collect Input
Ask the user for:
- Company name (required)
- Your product/service (optional) — if provided, engagement angles will be tailored to the user's offering. If omitted, angles stay generic but actionable.
- Target persona title (optional) — focuses the Decision Makers section on that role. Default to CRO / VP Sales / Head of Growth if not specified.
Do NOT proceed until you have at least the company name.
2. Company Overview
Run 1-2 WebSearch queries (e.g., "{company} what does it do", "{company} funding crunchbase").
Gather:
- What they do (one paragraph, plain language)
- Founded year and HQ location
- Company size — employee count, revenue range if public
- Funding stage and total raised (for startups / private companies)
- Key product(s) or service(s)
- Recent news from the last 90 days (1-3 headlines with URLs)
If any data point is not findable, write "Not found" — NEVER fabricate company data.
3. Decision Makers
Run 1-2 WebSearch queries (e.g., "{company} CEO founder", "{company} {target persona title}").
Gather:
- CEO / Founder — name, short background (prior companies, notable achievements)
- Target persona — name and title of the person matching the user's specified role (or CRO / VP Sales / Head of Growth by default)
- LinkedIn activity themes — what topics they post or comment about (if findable)
- Recent public content — podcast appearances, conference talks, blog posts, tweets
- Org structure clues — approximate size of their sales / marketing / growth team (from LinkedIn headcount or job postings)
4. Technology Stack
Run 1-2 WebSearch queries (e.g., "{company} tech stack job postings", "{company} integrations partners").
Gather:
- CRM platform (Salesforce, HubSpot, etc.)
- Marketing automation (Marketo, Pardot, HubSpot, etc.)
- Outreach / sales engagement tools (Outreach, Salesloft, Apollo, etc.)
- Other known tools from job postings, integrations pages, G2 profiles, case studies
- Stack gaps — if the user provided their product/service, note where their product could fit
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.
- 12d ago First seen · 232 lines · 63 tokens per session scan A 11bcdf294f73
account-research-brief is a skill published in the GitHub repository Othmane-Khadri/gtm-engineer-playbook (56 stars, last pushed 5mo ago), licensed MIT. It adds 63 tokens to every session and 1,982 once invoked, about $0.0003 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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