/sales-prospect

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.

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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:** 1. Report the error to the user: "Could not reach [url] — HTTP [status code]" 2. Attempt alternate URLs: try with/without www, try https vs http 3. If still unreachable, suggest the user verify the URL and try again 4. 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 |

**Detection procedure:** 1. Scan homepage for signals from each category 2. Check careers page for employee count and hiring velocity 3. Check pricing page for pricing model complexity 4. Look for investor/funding mentions 5. Assign the best-fit category (if ambiguous, note the two most likely categories)

1.3 Extract Key Pages and Site Architecture

Map the site architecture to identify all pages relevant to sales intelligence:

| Page Type | Common URLs | Data to Extract | |-----------|-------------|-----------------| | **Homepage** | / | Headline, value prop, CTAs, social proof, product positioning | | **About** | /about, /company, /about-us | Founding story, mission, team size, locations, history | | **Team** | /team, /leadership, /about/team, /people | Names, titles, photos, bios, LinkedIn links | | **Pricing** | /pricing, /plans, /packages | Tiers, prices, features per tier, enterprise CTA | | **Blog** | /blog, /resources, /insights, /news | Recent topics, posting frequency, content depth | | **Careers** | /careers, /jobs, /join-us, /hiring | Open roles, team sizes by department, growth signals | | **Contact** | /contact, /get-in-touch, /demo | Contact forms, phone numbers, office addresses | | **Products** | /products, /features, /solutions, /platform | Product lines, features, use cases, integrations | | **Customers** | /customers, /case-studies, /testimonials | Customer logos, case study details, industry verticals served | | **Partners** | /partners, /integrations, /ecosystem | Technology partners, integration depth, ecosystem maturity |

1.4 Identify Industry Vertical

Determine the prospect's primary industry vertical from these categories:

• Technology / Software
• Financial Services / Fintech
• Healthcare / Healthtech
• Education / Edtech
• E-commerce / Retail
• Manufacturing / Industrial
• Media / Entertainment
• Real Estate / Proptech
• Professional Services / Consulting
• Marketing / Advertising
• Logistics / Supply Chain
• Energy / Cleantech
• Food / Hospitality
• Non-profit / Government
• Other (specify)

**Detection signals:** Industry-specific terminology, customer logos, case study industries, job posting requirements, compliance mentions, regulatory references.

1.5 Run Structured Data Extraction

Execute the Python analysis script for machine-readable data extraction:

python3 scripts/analyze_prospect.py --url <url> --output json

This script extracts:

• Structured company metadata
• Technology stack detection
• Social media profile links
• Contact information patterns
• SEO and traffic signals

**If the script is not available or fails:** 1. Log the error but continue the analysis 2. Perform manual extraction using WebFetch data 3. Note in the report that automated extraction was unavailable

1.6 Compile Dis