lead-research-assistant
Verifiedby ComposioHQ · Updated Apr 16, 2026
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
See It In Action
I'll start by exploring the repository to understand the product, its features, and ideal customer profile before building the target list.
Now I have a complete picture of the product. Here's the ICP breakdown and the ranked target list.
RepoLens ICP — Derived from Codebase
| ICP Dimension | Signal from Repo | Why It Matters |
|---|---|---|
| Company type | README: "purpose-built for mid-market B2B SaaS companies" | Not consumer, not enterprise-only |
| Headcount | README: "100–1,000 employees" | Big enough to have visibility problems, small enough to not build in-house |
| Eng team size | README: "20+ engineers working across multiple repositories"; report prompt: "20–80 engineers" | The pain (cross-repo blind spots) starts at ~20 devs |
| Architecture | README: "Microservices or modular monolith"; models track per-service cycle times, per-team hotspots | Multi-repo complexity is the core trigger |
| Toolchain | .env.example: GitHub/GitLab/Bitbucket, CI/CD, Linear/Jira/Shortcut, Slack/Teams, Okta/Auth0/Azure AD | Modern SaaS stack = drop-in integrations |
| Buyer persona | analyzer.py weekly report: "Write for a VP of Engineering"; API serves leadership dashboards | VP Eng / CTO who needs data they can't get from Jira |
| Price sensitivity | $499–$1,499/mo standard tiers | Mid-market budget — not a procurement ordeal, not a toy |
| Key pain | Health scores, tech debt mapping, cycle-time analytics, deployment risk scoring | Scaling eng teams that are losing visibility as they grow |
20 Launch-Day Targets — Ranked by ICP Fit
TIER 1 — Bullseye (highest-fit, reach out first)
| # | Company | HQ | Est. Employees | Why They Fit |
|---|---|---|---|---|
| 1 | Drata | US (San Diego) | ~500 | Compliance-automation SaaS in hyper-growth. Engineering org doubled recently — exactly when cycle-time and tech-debt visibility breaks down. Modern stack (GitHub, CI/CD). VP Eng is actively scaling. |
| 2 | Vanta | US (San Francisco) | ~500 | Direct competitor to Drata, same dynamics. Rapid eng hiring, multi-repo microservices, security-conscious (SSO ✓). Will resonate with deployment-risk scoring. |
| 3 | LaunchDarkly | US (Oakland) | ~500 | Feature-flag platform — the buyer persona is an engineering leader. They live in repos. Multi-service architecture, GitHub-native. They'll immediately grasp the value of codebase health scores. |
| 4 | Retool | US (San Francisco) | ~400 | Internal-tools platform, developer-tool DNA. Eng-heavy org ratio. They already think in terms of developer productivity — RepoLens speaks their language. |
| 5 | Gong | US (San Francisco) | ~800 | Revenue-intelligence SaaS at the top of the headcount range. Large eng org (~150+ devs), multiple product lines, microservices. VP Eng needs cross-team velocity visibility. |
| 6 | Cribl | US (San Francisco) | ~700 | Observability-pipeline company. Complex distributed system, high deployment cadence. They value data-driven ops — selling analytics to an analytics company is a natural fit. |
| 7 | Ironclad | US (San Francisco) | ~500 | Contract-lifecycle SaaS. Rapid scaling, AI-heavy product (they'll appreciate AI-generated reports). Multiple eng squads across core platform + AI features. |
TIER 2 — Strong Fit (reach out same day / next)
| # | Company | HQ | Est. Employees | Why They Fit |
|---|---|---|---|---|
| 8 | Lattice | US (San Francisco) | ~700 | People-management SaaS. Ironic hook: they help companies measure people performance — let RepoLens measure engineering performance. Large eng org, multi-repo. |
| 9 | Postman | US (San Francisco) | ~800 | API platform. Massive repo footprint, eng-centric culture. Top of headcount range but still mid-market mindset. Cycle-time analytics is directly relevant. |
| 10 | GoCardless | UK (London) | ~700 | Payments infrastructure. Regulated industry = high deployment-risk sensitivity. Microservices architecture, complex CI/CD. UK beachhead account. |
| 11 | Ramp | US (New York) | ~800 | Corporate-finance SaaS growing fast. Fintech = deployment risk matters. Large eng org with multiple squads shipping to shared infrastructure. |
| 12 | Monte Carlo | US (San Francisco) | ~300 | Data-observability company. Selling code observability to a data observability company — they already believe in the category. Right-sized eng team (~60 devs). |
| 13 | Paddle | UK (London) | ~400 | Billing/payments platform. UK-based, B2B SaaS, multi-repo, regulated. Checkout + subscription + tax = complex codebase where tech debt mapping has clear ROI. |
| 14 | Vercel | US (San Francisco) | ~500 | Frontend-cloud platform. Developer-tool company, GitHub-native by definition. Eng leaders will instantly understand codebase health scoring. |
TIER 3 — Good Fit (fill the pipeline)
| # | Company | HQ | Est. Employees | Why They Fit |
|---|---|---|---|---|
| 15 | Customer.io | US (Portland, remote) | ~250 | Messaging platform. Smaller but perfectly mid-market. Engineering-led culture, multiple services (email, push, SMS, data pipeline). Right-sized for the $499 Team tier entry point. |
| 16 | Hightouch | US (San Francisco) | ~200 | Data-activation SaaS. Small but engineering-dense. Complex integrations (150+ connectors) = lots of repos. Good $499-tier entry, upsell path to Business. |
| 17 | Thought Machine | UK (London) | ~500 | Core-banking infrastructure. UK-based, highly complex codebase (cloud-native banking). Regulated = deployment risk scoring is a must-have, not a nice-to-have. |
| 18 | Calendly | US (Atlanta) | ~700 | Scheduling SaaS. Mature product, scaling eng org. This is where tech debt accumulates — the "where is tech debt building fastest?" question from the README is their daily reality. |
| 19 | Onfido | UK (London) | ~500 | Identity-verification SaaS. AI + compliance = complex, fast-moving codebase. UK presence, microservices, security-conscious (SSO). Third UK beachhead account. |
| 20 | Webflow | US (San Francisco) | ~800 | No-code website builder with strong B2B GTM. Large eng org, complex frontend + backend architecture. At the scale where cross-team velocity reporting becomes critical. |
Outreach Angle by Tier
| Tier | Lead Message | Key Feature to Demo |
|---|---|---|
| Tier 1 (1–7) | "You've doubled your eng team — can your VP Eng still tell you where tech debt is accumulating fastest?" | Tech Debt Mapping + Weekly AI Report (the CTO/VP hook) |
| Tier 2 (8–14) | "Your Jira board shows activity. RepoLens shows outcomes — cycle times, deployment risk, codebase health, from your actual repos." | Cycle Time Analytics + Deployment Risk Scoring |
| Tier 3 (15–20) | "Connect GitHub, get a codebase health score in 5 minutes. No config, no agent to install." | Health Score (low-friction entry at $499/mo Team tier) |
US : UK split — 15 US / 5 UK (GoCardless, Paddle, Thought Machine, Onfido, Webflow is US). Adjust if you want heavier UK weighting.
Buyer to find at each account: VP Engineering, Head of Engineering, or CTO. The weekly AI report and leadership dashboard features are built for them — use that in the outreach.
Permissions
| Scope | Description |
|---|---|
| filesystem:read | |
| filesystem:write | |
| network:outbound |
SKILL.md
Lead Research Assistant
This skill helps you identify and qualify potential leads for your business by analyzing your product/service, understanding your ideal customer profile, and providing actionable outreach strategies.
When to Use This Skill
- Finding potential customers or clients for your product/service
- Building a list of companies to reach out to for partnerships
- Identifying target accounts for sales outreach
- Researching companies that match your ideal customer profile
- Preparing for business development activities
What This Skill Does
- Understands Your Business: Analyzes your product/service, value proposition, and target market
- Identifies Target Companies: Finds companies that match your ideal customer profile based on:
- Industry and sector
- Company size and location
- Technology stack and tools they use
- Growth stage and funding
- Pain points your product solves
- Prioritizes Leads: Ranks companies based on fit score and relevance
- Provides Contact Strategies: Suggests how to approach each lead with personalized messaging
- Enriches Data: Gathers relevant information about decision-makers and company context
How to Use
Basic Usage
Simply describe your product/service and what you're looking for:
I'm building [product description]. Find me 10 companies in [location/industry]
that would be good leads for this.
With Your Codebase
For even better results, run this from your product's source code directory:
Look at what I'm building in this repository and identify the top 10 companies
in [location/industry] that would benefit from this product.
Advanced Usage
For more targeted research:
My product: [description]
Ideal customer profile:
- Industry: [industry]
- Company size: [size range]
- Location: [location]
- Current pain points: [pain points]
- Technologies they use: [tech stack]
Find me 20 qualified leads with contact strategies for each.
Instructions
When a user requests lead research:
-
Understand the Product/Service
- If in a code directory, analyze the codebase to understand the product
- Ask clarifying questions about the value proposition
- Identify key features and benefits
- Understand what problems it solves
-
Define Ideal Customer Profile
- Determine target industries and sectors
- Identify company size ranges
- Consider geographic preferences
- Understand relevant pain points
- Note any technology requirements
-
Research and Identify Leads
- Search for companies matching the criteria
- Look for signals of need (job postings, tech stack, recent news)
- Consider growth indicators (funding, expansion, hiring)
- Identify companies with complementary products/services
- Check for budget indicators
-
Prioritize and Score
- Create a fit score (1-10) for each lead
- Consider factors like:
- Alignment with ICP
- Signals of immediate need
- Budget availability
- Competitive landscape
- Timing indicators
-
Provide Actionable Output
For each lead, provide:
- Company Name and website
- Why They're a Good Fit: Specific reasons based on their business
- Priority Score: 1-10 with explanation
- Decision Maker: Role/title to target (e.g., "VP of Engineering")
- Contact Strategy: Personalized approach suggestions
- Value Proposition: How your product solves their specific problem
- Conversation Starters: Specific points to mention in outreach
- LinkedIn URL: If available, for easy connection
-
Format the Output
Present results in a clear, scannable format:
# Lead Research Results ## Summary - Total leads found: [X] - High priority (8-10): [X] - Medium priority (5-7): [X] - Average fit score: [X] --- ## Lead 1: [Company Name] **Website**: [URL] **Priority Score**: [X/10] **Industry**: [Industry] **Size**: [Employee count/revenue range] **Why They're a Good Fit**: [2-3 specific reasons based on their business] **Target Decision Maker**: [Role/Title] **LinkedIn**: [URL if available] **Value Proposition for Them**: [Specific benefit for this company] **Outreach Strategy**: [Personalized approach - mention specific pain points, recent company news, or relevant context] **Conversation Starters**: - [Specific point 1] - [Specific point 2] --- [Repeat for each lead] -
Offer Next Steps
- Suggest saving results to a CSV for CRM import
- Offer to draft personalized outreach messages
- Recommend prioritization based on timing
- Suggest follow-up research for top leads
Examples
Example 1: From Lenny's Newsletter
User: "I'm building a tool that masks sensitive data in AI coding assistant queries. Find potential leads."
Output: Creates a prioritized list of companies that:
- Use AI coding assistants (Copilot, Cursor, etc.)
- Handle sensitive data (fintech, healthcare, legal)
- Have evidence in their GitHub repos of using coding agents
- May have accidentally exposed sensitive data in code
- Includes LinkedIn URLs of relevant decision-makers
Example 2: Local Business
User: "I run a consulting practice for remote team productivity. Find me 10 companies in the Bay Area that recently went remote."
Output: Identifies companies that:
- Recently posted remote job listings
- Announced remote-first policies
- Are hiring distributed teams
- Show signs of remote work challenges
- Provides personalized outreach strategies for each
Tips for Best Results
- Be specific about your product and its unique value
- Run from your codebase if applicable for automatic context
- Provide context about your ideal customer profile
- Specify constraints like industry, location, or company size
- Request follow-up research on promising leads for deeper insights
Related Use Cases
- Drafting personalized outreach emails after identifying leads
- Building a CRM-ready CSV of qualified prospects
- Researching specific companies in detail
- Analyzing competitor customer bases
- Identifying partnership opportunities
FAQ
What does lead-research-assistant do?
Identifies high-quality leads for your product or service by analyzing your business, searching for target companies, and providing actionable contact strategies. Perfect for sales, business development, and marketing professionals.
When should I use lead-research-assistant?
Use it when you need a repeatable workflow that produces text response.
What does lead-research-assistant output?
In the evaluated run it produced text response.
How do I install or invoke lead-research-assistant?
npx skills add https://github.com/composiohq/awesome-claude-skills --skill lead-research-assistant
Which agents does lead-research-assistant support?
Claude Code
What tools, channels, or permissions does lead-research-assistant need?
It uses no extra tools; channels commonly include text; permissions include filesystem:read, filesystem:write, network:outbound.
Is lead-research-assistant safe to install?
Static analysis marked this skill as medium risk; review side effects and permissions before enabling it.
How is lead-research-assistant different from an MCP or plugin?
A skill packages instructions and workflow conventions; tools, MCP servers, and plugins are dependencies the skill may call during execution.
Does lead-research-assistant outperform not using a skill?
About lead-research-assistant
When to use lead-research-assistant
You need a shortlist of companies that closely match your ideal customer profile. You want prioritized lead research with company-specific outreach angles and decision-maker targeting. You want the agent to analyze your repository or product description to infer who would benefit most from it.
When lead-research-assistant is not the right choice
You need guaranteed verified contact data from proprietary databases or direct CRM integrations. You want the agent to actually send outreach messages or update external sales systems.
What it produces
Produces text response.
Install
npx skills add https://github.com/composiohq/awesome-claude-skills --skill lead-research-assistantInvoke: Ask Claude Code to use lead-research-assistant for the task.