AI Sales Team for Claude Code is a command-line sales workflow that uses Claude Code to research companies, assess leads, identify decision makers, create outreach and follow-up sequences, prepare meetings, draft proposals, and generate pipeline reports. Sales and business-development users employ its commands and parallel agents to turn prospect information into research and sales materials. The catalogue entries are the workflow’s bundled skills and agents.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add zubair-trabzada/ai-sales-team-claude --skill sales-prepgit clone --depth 1 https://github.com/zubair-trabzada/ai-sales-team-claudeWrote 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/zubair-trabzada/ai-sales-team-claude/sales-prep)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-prep"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-prep/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/zubair-trabzada/ai-sales-team-claude/sales-prep"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-prep.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 | $0.00000 | $0.03615 |
| Opus 5.5 | $0.00000 | $0.01446 |
| Sonnet 5.5 | $0.00000 | $0.00723 |
| Haiku 4.5 | $0.00000 | $0.00362 |
Grade A, and why
sales-prep 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 2mo 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 — 388 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meeting Preparation Brief
You generate comprehensive meeting preparation briefs that give salespeople everything they need before walking into a prospect meeting. The brief combines company research, attendee intelligence, competitive context, and tactical preparation into a single actionable document.
Invocation
/sales prep <url>
Where <url> is the prospect company's website URL. Optionally, the user may also provide:
- Names of meeting attendees
- Meeting date and time
- Meeting purpose or agenda
- Your product/service being discussed
Step 1: Research Phase
Execute the following research tasks. Use WebFetch to gather data from each source. Run as many fetches in parallel as possible to minimize preparation time.
1.1 Company Research
Fetch the prospect's website and extract:
- Homepage: Company description, value proposition, key messaging, target market
- About page: Founding story, mission, team size, office locations, company values
- Product/Services pages: What they sell, pricing model (if public), key features
- Blog/News page: Recent announcements, content themes, thought leadership topics
- Careers page: Open roles (indicates growth areas, team gaps, technology choices, budget allocation)
- Case studies/Testimonials page: Their customers, results they highlight, industries they serve
Additionally, search for:
- Recent press coverage or news mentions (use WebSearch with "[Company Name] news [current year]")
- Recent funding rounds or financial events (use WebSearch with "[Company Name] funding OR acquisition OR IPO")
- Company LinkedIn page activity (recent posts, follower count, engagement patterns)
1.2 Attendee Research
If attendee names are provided, research each person:
- LinkedIn profile: Current title, tenure at company, career history, education, shared connections
- Recent LinkedIn posts: Topics they write about, what they engage with, their professional interests
- Conference talks or podcasts: Have they spoken publicly? What topics?
- Published articles or quotes: Any media appearances or thought leadership?
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.
- 2mo ago First seen · 388 lines · 0 tokens per session scan A ee7af83548e6
sales-prep is a skill published in the GitHub repository zubair-trabzada/ai-sales-team-claude (1,508 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 3,615 tokens. 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.
sales-prep compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| sales-prepzubair-trabzada/ai-sales-team-claude | Skill | 1.5k | 7mo ago | MIT | A | 0 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | today | MIT | A | 103 | Claude Code |