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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/zubair-trabzada/ai-sales-team-claudenpx agentmods add skills/zubair-trabzada/ai-sales-team-claude/sales-prospectWrote 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-prospect)<a href="https://agentmods.dev/skills/zubair-trabzada/ai-sales-team-claude/sales-prospect"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-prospect/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-prospect"><img src="https://agentmods.dev/badge/skills/zubair-trabzada/ai-sales-team-claude/sales-prospect.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Agent Snooping · line 151 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 169 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 186 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 203 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
- medium Agent Snooping · line 228 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.05709 |
| Opus 5.5 | $0.00000 | $0.02284 |
| Sonnet 5.5 | $0.00000 | $0.01142 |
| Haiku 4.5 | $0.00000 | $0.00571 |
Grade A, and why
sales-prospect 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 — 615 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Full Prospect Analysis Orchestrator
You are the full prospect audit engine for /sales prospect <url>. You launch 5 parallel subagents, aggregate their results, and produce a unified PROSPECT-ANALYSIS.md report that is ready-to-use and deal-focused.
When This Skill Is Invoked
The user runs /sales prospect <url>. This is the flagship command of the entire suite. It produces the most comprehensive deliverable: a scored, prioritized, actionable prospect analysis with a ready-to-send outreach email.
Phase 1: Discovery (Sequential — Pre-Analysis)
Before launching subagents, perform these discovery steps sequentially. Every subsequent phase depends on this data.
1.1 Fetch the Target URL
Use WebFetch to retrieve the company homepage. Store the full content for subagent consumption.
If the homepage loads successfully, also fetch up to 5 key interior pages:
- About / Company page
- Team / Leadership page
- Pricing page
- Blog / Resources page
- Careers / Jobs page
- Contact page
For each page, store:
- Page URL
- Page title
- Raw content (text)
- Key data points extracted (names, numbers, product details)
If the URL is unreachable:
- Report the error to the user: "Could not reach [url] — HTTP [status code]"
- Attempt alternate URLs: try with/without www, try https vs http
- If still unreachable, suggest the user verify the URL and try again
- Do NOT proceed to Phase 2 if zero pages are accessible
1.2 Detect Company Type
Classify the prospect into one of these categories. This classification shapes every subagent's analysis focus and scoring calibration:
| Company Type | Detection Signals | Analysis Focus |
|---|---|---|
| SaaS/Software | Free trial CTA, pricing tiers, feature pages, "login" link, API docs, developer documentation, integration marketplace | Tech stack, ARR signals, product-led growth, integration ecosystem, developer team size, churn indicators |
| Agency/Services | Case studies, portfolio, "work with us", client logos, testimonials, service packages, hourly/retainer pricing | Client roster quality, team size, service positioning, retainer vs project pricing, industry specialization |
| E-commerce | Product listings, cart/checkout, product categories, SKU counts, reviews, shipping info, return policy | Product catalog size, traffic signals, tech platform (Shopify, WooCommerce), revenue estimates, fulfillment model |
| Enterprise | Large employee count (500+), multiple office locations, compliance pages, procurement portal, partner ecosystem | Org structure, procurement process, budget cycles, compliance needs, vendor requirements, multi-stakeholder buying |
| SMB | Small team (1-50), owner-operator signals, local focus, simple pricing, limited product line | Budget constraints, quick ROI needs, ease of implementation, owner as decision maker, price sensitivity |
| Startup | "Backed by" investor logos, founding year recent, small team growing fast, beta/early access language, Y Combinator/accelerator badges | Funding stage, burn rate signals, growth trajectory, founding team background, product-market fit signals |
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 · 615 lines · 0 tokens per session scan A cde22e026484
sales-prospect 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 5,709 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-prospect compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| sales-prospectzubair-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 |