SkillAtlasSkill 详情

inbound-lead-enrichment

Put your AI agent on the growth team.

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项目 README

来源文件:README.md

抓取于 2026年8月21日
CleanShot 2026-07-13 at 20 15 47@2x CleanShot 2026-07-13 at 20 16 54@2x

AI Skills for Brand Growth

Put your AI agent on the growth team.

Research customers and competitors, analyze what is working, create the next campaign, and learn from the result. Goose Skills gives Claude Code, Cursor, Codex, and other coding agents ready-to-use workflows for ads, social media, content, competitive intelligence, SEO, lead generation, and GTM.

Browse all skills at https://skills.gooseworks.ai

Works with Claude Code · Cursor · Codex

npm version License: MIT Skills


Contents


Quick Start

AI Coding Agents (Claude Code, Cursor, Codex, etc)

Paste this into your coding agent (Claude Code, Cursor, or Codex) and it'll set everything up:

Install the Gooseworks skills:

In the terminal, run `npx gooseworks install --all`.

Then run `npx gooseworks login` and it'll open a browser to sign in and set up the tools, then confirm it worked.

The skills can be used with /gooseworks <prompt>

Claude Cowork

Run this command in a terminal first:

npx gooseworks install --all

Then authenticate:

npx gooseworks login

Then make sure you're working inside a local folder on your machine, and then you can use the skills in Cowork like this:

Use /gooseworks skill to generate some ad creatives

Install manually

Prefer to run it yourself? Use the command directly:

npx gooseworks install --all       # All detected agents

This gives your coding agent access to the full catalog of 200+ skills. After installing, just ask your agent to use any skill by name.

If you want a cloud-based AI coworker that already knows all these skills and more, sign up to Gooseworks


Brand Growth collection

The Brand Growth collection is a curated path through the normal Goose Skills catalog for consumer and ecommerce brand work. It is not a separate package or command: install GooseWorks once, then ask /gooseworks for the outcome you need.

StageWhat your agent can doExample skills
ResearchUnderstand the brand, customers, competitors, audiences, creators, trends, comments, and product demandbrand-research, audience-research, comment-mining, competitor-social-research, influencer-prospecting, trend-discovery, product-demand-research
AnalyzeDiagnose ads, creator profiles, transcripts, policy risk, landing-page message match, and unusual social performancecompetitor-ad-intelligence, creator-profile-teardown, transcript-intelligence, meta-ads-analyzer, meta-ad-policy-checker, ad-to-landing-page-auditor, outlier-post-finder
CreateRepurpose research, remix graphic ads, make product photography and social graphics, and animate static imagescontent-repurposing, remix-graphic-ad-from-reference, product-photoshoot, goose-graphics, animate-image
Learn and iterateBring results back into research and analysis, then decide the next testRe-run the relevant analysis skill with current performance and audience evidence

ScrapeCreators powers structured public social and ad-library research behind several workflows. Signed-in GooseWorks users access it through the managed first-party proxy and do not need a separate ScrapeCreators key. The user-facing skills turn that source data into a brief, shortlist, analysis, or recommendation instead of returning raw API output.

Browse the Brand Growth collection

After installation, start with:

/gooseworks onboard me

The agent will collect the useful company context for future growth work and finish by asking what you want to do first. Existing users can skip onboarding and keep using /gooseworks exactly as they do today.


Commands

npx gooseworks search "reddit scraping"   # Search the skill catalog
npx gooseworks credits                     # Check your credit balance
npx gooseworks update                      # Update to latest skill version

Skills Catalog

200+ skills across the growth stack, grouped by focus area:

CategoryWhat's inside
AdsResearch, build, and analyze paid campaigns across Meta and Google
SEOKeyword research, content gaps, SERP analysis, technical audits
Lead generationFind, enrich, and qualify prospects for your pipeline
OutreachDraft, personalize, and run outbound across email and social
ContentBlog posts, social content, carousels, video scripts, newsletters
ResearchCompany, market, and prospect deep-dives
Competitive intelTrack competitor pricing, launches, positioning, and ads
MonitoringWatch for mentions, signals, and changes across the web
SocialScrape and analyze social platforms and audiences
BrandVoice, positioning, and visual brand assets

Browse and search every skill at skills.gooseworks.ai.


Usage Examples

After installing, just ask your coding agent naturally:

"/gooseworks Generate static ad creatives for my brand"
"/gooseworks Use the reddit-post-finder skill to search r/startups"
"/gooseworks Use the apollo-lead-finder skill to find CTOs at AI companies"
"/gooseworks Use the competitor-intel skill to research Acme Corp"
"/gooseworks Use the goose-graphics skill to create a LinkedIn carousel about our launch"

Your agent will search the GooseWorks catalog, download the skill, and run it automatically.


Building from Source

git clone https://github.com/gooseworks-ai/goose-skills.git
cd goose-skills
node scripts/validate-skills.js  # Validate SKILL.md + skill.meta.json contract
node scripts/build-index.js      # Generate skills-index.json
node bin/goose-skills.js list    # Test locally

Skill Metadata Contract

Each skill directory must include:

  • SKILL.md — Skill documentation and usage guide
  • skill.meta.json — Machine-readable metadata

skill.meta.json fields:

FieldRequiredDescription
slugYesUnique kebab-case identifier
categoryYescapabilities, composites, or playbooks
tagsYesString array of category tags
installation.base_commandYesInstall command
installation.supportsYesArray: claude, codex, cursor
featuresNoFeature flags
github_urlNoSource repository URL
authorNoSkill author
example_promptNoCopyable prompt shown in the catalog and docs for trying the skill

Security & Trust

These skills run inside your coding agent, so it's worth knowing exactly what they do:

  • Open source & inspectable. Every skill — its SKILL.md instructions and all scripts — lives in this repo under the MIT license. The gooseworks CLI fetches skills at runtime so recipes stay current, but the source you'd run is right here to read, diff, or pin before you run it.
  • Scripts run locally. Skill scripts execute on your machine and write to /tmp/gooseworks-scripts/, never into your project directory. Only API requests go through GooseWorks servers; review any script before letting your agent run it.
  • Your agent stays in control. The skills are a tool your agent reaches for when it fits the task (data at scale, sources behind auth, a specific provider) — not a replacement for its built-in web search or fetch on quick lookups. You can read or edit any installed SKILL.md to tune that behavior.
  • Credentials stay local. Auth is a Bearer token stored at ~/.gooseworks/credentials.json (file mode 0600). Third-party provider keys (Apify, Apollo, etc.) are held server-side — your token never touches them. All network calls are HTTPS.
  • The MCP server is opt-in. Registering the GooseWorks MCP server is off by default; it only happens if you explicitly run gooseworks install --mcp.

Found something that looks off? Open an issue — we'd rather fix it in public.


License

MIT — see LICENSE for details.

The skill files and CLI in this repository are MIT-licensed. The GooseWorks API they connect to is a separate paid service governed by its own terms.

其他

高风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 存在潜在风险命令,请谨慎安装。
  • 扫描发现:1 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/lead-generation/composites/inbound-lead-enrichment" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/lead-generation/composites/inbound-lead-enrichment" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/lead-generation/composites/inbound-lead-enrichment" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/lead-generation/composites/inbound-lead-enrichment" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库:git clone https://github.com/gooseworks-ai/goose-skills.git
  3. 将 "skills/lead-generation/composites/inbound-lead-enrichment" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: inbound-lead-enrichment
version: 1.0.0
description: >
  Fills in missing data for inbound leads — researches the company, identifies the person's
  role and seniority, finds other stakeholders at the company, checks for existing CRM
  relationships, and updates the lead record. Produces enriched lead data ready for
  qualification or outreach. Tool-agnostic.
tags: [lead-generation]

Inbound Lead Enrichment

Takes inbound leads with incomplete data and fills in the gaps. Researches the company, identifies the person's role, finds other stakeholders at the company, and checks for existing relationships in CRM. Turns a bare email address into a full lead profile.

When to Auto-Load

Load this composite when:

  • User says "enrich these leads", "fill in the missing data", "research these inbound leads"
  • inbound-lead-qualification flags leads as insufficient_data
  • inbound-lead-triage detects leads with missing company/title fields
  • User has a list of emails or partial lead data and needs complete profiles

Architecture

[Raw Leads] → Step 1: Assess Gaps → Step 2: Company Research → Step 3: Person Research → Step 4: Stakeholder Discovery → Step 5: Relationship Check → Step 6: Compile & Output
                   ↓                      ↓                         ↓                          ↓                             ↓                          ↓
            Gap inventory        Company profiles         Person profiles            Buying committee          CRM/pipeline matches       Enriched lead records

Step 0: Configuration (Once Per Client)

On first run, establish enrichment tool preferences.

{
  "enrichment_tools": {
    "company_research": {
      "primary": "SixtyFour | Orthogonal | web-search",
      "secondary": "web-search"
    },
    "person_research": {
      "primary": "SixtyFour | Orthogonal | web-search",
      "secondary": "web-search"
    },
    "stakeholder_finding": {
      "primary": "SixtyFour | Orthogonal | web-search",
      "secondary": "web-search"
    }
  },
  "crm_source": {
    "tool": "HubSpot | Salesforce | CSV | none",
    "access_method": ""
  },
  "buyer_personas": [],
  "enrichment_depth": {
    "tier_1_leads": "deep",
    "tier_2_leads": "deep",
    "tier_3_leads": "standard",
    "tier_4_leads": "minimal",
    "untiered_leads": "standard"
  }
}

On subsequent runs: Load config silently.


Step 1: Assess Data Gaps

Process

For each lead, inventory what's known vs. unknown:

Required fields (must fill):

  • company_name — What company do they work for?
  • company_domain — Company website domain
  • person_name — Full name
  • person_title — Current job title
  • person_email — Contact email (usually already have this from inbound)

Valuable fields (fill if possible):

  • company_size — Employee count or range
  • company_industry — Industry classification
  • company_stage — Funding stage or maturity
  • company_hq — Headquarters location
  • company_description — One sentence about what they do
  • person_seniority — IC, Manager, Director, VP, C-Level, Founder
  • person_department — Engineering, Sales, Marketing, etc.
  • person_linkedin — LinkedIn profile URL
  • person_tenure — How long at current company

Bonus fields (nice to have):

  • company_tech_stack — Known technologies used
  • company_recent_news — Any recent events (funding, launches, hires)
  • person_background — Previous companies, education
  • person_social_activity — Recent posts or engagement topics

Gap Classification

For each lead, classify the enrichment effort needed:

Gap LevelMissingEnrichment NeededCost
Minimal1-2 valuable fieldsQuick web searchFree
StandardCompany or title missingWeb search + possible API lookupLow
DeepMultiple required fields missingMulti-source researchMedium
Email-onlyOnly have an email addressFull research from scratchHigh

Output

  • Gap inventory table showing each lead and what's missing
  • Recommended enrichment depth per lead (based on gap level AND urgency tier if available)
  • Cost estimate if paid tools will be used

Human Checkpoint

"Here's what's missing across your leads. [X] need deep enrichment, [Y] need standard, [Z] just need a quick lookup. Estimated cost: [amount]. Proceed?"


Step 2: Company Research

Process

For each unique company in the lead list (deduplicate — don't research the same company twice for multiple leads):

From email domain (if company name is missing):

  1. Extract domain from email (e.g., jane@acme.com → acme.com)
  2. Skip personal email domains (gmail, yahoo, hotmail, outlook, etc.)
  3. Look up the domain → company name, description

Company profile research:

FieldPrimary SourceFallback Source
Company nameDomain lookupWeb search
DescriptionCompany website (homepage, about page)LinkedIn company page, web search
Employee countSixtyFour or Orthogonal, LinkedIn company pageWeb search
IndustryLinkedIn company page, SixtyFour or OrthogonalInfer from website content
Stage/FundingSixtyFour or Orthogonal, news articlesWeb search
HQ LocationLinkedIn company page, websiteWeb search
Tech stackJob postings, BuiltWithWeb search
Recent newsWeb search (last 90 days)Twitter/social mentions

Research depth by config:

  • Deep: All fields, multiple sources, verify across sources
  • Standard: Required + valuable fields, primary source only
  • Minimal: Company name + description + size only

Output

Each company gets a company_profile block:

{
  "company_name": "",
  "company_domain": "",
  "company_description": "",
  "employee_count": "",
  "employee_range": "",
  "industry": "",
  "sub_industry": "",
  "stage": "",
  "last_funding": "",
  "hq_location": "",
  "tech_stack": [],
  "recent_news": [],
  "research_sources": [],
  "confidence": "high | medium | low"
}

Handling Personal Email Domains

If the lead used a personal email (gmail, etc.):

  1. Check if name + any other available data can identify the company (e.g., form field, chat message)
  2. If company is mentioned in their form submission or chat, use that
  3. If truly unknown, flag as company_unidentified — still proceed with person research if name is available

Step 3: Person Research

Process

For each lead, build a person profile:

From name + company (if title is missing):

  1. Search LinkedIn for person at company (via configured tool or web search)
  2. Cross-reference with SixtyFour or Orthogonal
  3. If multiple matches, use email domain to disambiguate

Person profile research:

FieldPrimary SourceFallback Source
Full nameInput dataLinkedIn profile
Current titleLinkedIn profile, SixtyFour or OrthogonalWeb search
Seniority levelInfer from titleLinkedIn profile
DepartmentInfer from titleLinkedIn profile
Tenure at companyLinkedIn profileWeb search
Previous companiesLinkedIn profileWeb search
EducationLinkedIn profileSkip
LinkedIn URLSixtyFour or Orthogonal, web searchSkip
LinkedIn headlineLinkedIn profileSkip
Recent activityLinkedIn posts (if scraper configured)Skip

Seniority inference rules:

  • Titles containing: Intern, Associate, Coordinator, Specialist → IC_junior
  • Titles containing: Analyst, Engineer, Designer, Developer (no "Senior/Lead/Staff") → IC_mid
  • Titles containing: Senior, Lead, Staff, Principal → IC_senior
  • Titles containing: Manager, Team Lead → Manager
  • Titles containing: Director, Head of → Director
  • Titles containing: VP, Vice President, SVP, EVP → VP
  • Titles containing: Chief, C-level abbreviations (CTO, CMO, CRO, CFO), President → C_Level
  • Titles containing: Founder, Co-founder, Owner → Founder

Adjust for company size:

  • At companies <20 employees: inflate seniority one level (a "Manager" has Director-level scope)
  • At companies >5000 employees: deflate seniority one level (a "Director" may have Manager-level autonomy)

Output

Each lead gets a person_profile block:

{
  "full_name": "",
  "current_title": "",
  "seniority_level": "",
  "department": "",
  "tenure_months": null,
  "previous_companies": [],
  "education": "",
  "linkedin_url": "",
  "linkedin_headline": "",
  "recent_activity_summary": "",
  "research_sources": [],
  "confidence": "high | medium | low"
}

Step 4: Stakeholder Discovery

Process

For each company in the lead list, identify other relevant people — the buying committee.

Why this matters:

  • Inbound leads are rarely the sole decision-maker
  • Finding the rest of the buying committee early accelerates the deal
  • Multi-threading (engaging multiple people at a company) dramatically improves win rates

Who to find (based on buyer personas from config):

  1. Economic buyer — Person who signs the check. Usually VP+ or C-level in the relevant department.
  2. Champion — Person most likely to push for adoption internally. Usually a senior IC or Director who feels the pain.
  3. Technical evaluator — Person who will assess the product's technical fit. Usually engineering or ops.
  4. End user — Person who will use the product daily. Their buy-in prevents post-sale churn.

Process per company:

  1. Using the buyer personas, determine which roles to search for
  2. Search via configured tool (SixtyFour or Orthogonal, LinkedIn, company-contact-finder)
  3. For each stakeholder found, capture: name, title, seniority, LinkedIn URL, email (if available)
  4. Note the relationship to the inbound lead: same team? Same department? Different function?

Depth control:

  • Deep enrichment (Tier 1-2 leads): Find all 4 stakeholder types. Research each.
  • Standard enrichment (Tier 3 leads): Find economic buyer + champion only.
  • Minimal enrichment (Tier 4 / untiered): Skip stakeholder discovery.

Output

Each company gets a stakeholder_map:

{
  "company": "",
  "inbound_lead": {
    "name": "",
    "title": "",
    "role_in_deal": "economic_buyer | champion | evaluator | user | unknown"
  },
  "stakeholders_found": [
    {
      "name": "",
      "title": "",
      "seniority": "",
      "linkedin_url": "",
      "email": "",
      "role_in_deal": "",
      "relationship_to_lead": "",
      "confidence": "high | medium | low"
    }
  ],
  "buying_committee_completeness": "full | partial | minimal",
  "recommended_multi_thread": ""
}

Stakeholder Prioritization

If the inbound lead IS the economic buyer → stakeholders are supporting context If the inbound lead is a user/evaluator → finding the economic buyer is critical If the inbound lead is unknown → identifying their role determines the multi-threading strategy


Step 5: Relationship Check

Process

For each lead AND each discovered stakeholder, check existing systems for prior relationships:

Check 1 — CRM (HubSpot, Salesforce, CSV):

  • Does this person already exist in our system?
  • If yes: what's their current status? (active lead, contacted, nurture, customer, churned)
  • If yes: who owns the relationship?

Check 2 — Outreach history (outreach_log):

  • Have we emailed/messaged this person before?
  • If yes: when, what channel, what was the outcome?
  • Critical: prevent the "we cold-emailed you last week and you ignored us, now you came inbound" collision

Check 3 — Company-level pipeline (companies table or CRM):

  • Is there an active deal with this company?
  • If yes: at what stage? Who's the deal owner?
  • An inbound lead at a company with an active deal is a HUGE signal — flag it prominently

Check 4 — Signal history (signals table):

  • Has this company appeared in any signal scans?
  • If yes: which signals? Were they acted on?

Check 5 — Mutual connections (if data available):

  • Does anyone on our team know someone at this company?
  • If yes: note the warm intro path

Output

Each lead gets a relationship_context block:

{
  "person_in_crm": true/false,
  "person_crm_status": "",
  "person_outreach_history": [
    {
      "date": "",
      "channel": "",
      "campaign": "",
      "outcome": ""
    }
  ],
  "company_in_pipeline": true/false,
  "company_deal_stage": "",
  "company_deal_owner": "",
  "company_signal_history": [],
  "mutual_connections": [],
  "relationship_summary": ""
}

Step 6: Compile & Output

Enriched Lead Record

Merge all research into a single enriched record per lead:

{
  "original_data": {},
  "company_profile": {},
  "person_profile": {},
  "stakeholder_map": {},
  "relationship_context": {},
  "enrichment_metadata": {
    "enrichment_depth": "deep | standard | minimal",
    "fields_filled": X,
    "fields_still_missing": [],
    "sources_used": [],
    "confidence_overall": "high | medium | low",
    "enrichment_date": "",
    "cost_incurred": ""
  }
}

Output Formats

Primary: Enriched CSV

Produce a CSV that extends the original lead data with all enriched fields:

Original Fields+ Company Fields+ Person Fields+ Stakeholder Fields+ Relationship Fields+ Metadata
All input columnscompany_description, employee_count, industry, stage, hq, tech_stack, recent_newscurrent_title, seniority, department, tenure, linkedin_url, headlinestakeholder_1_name, stakeholder_1_title, stakeholder_1_role, ... (up to 4)in_crm, crm_status, in_pipeline, deal_stage, outreach_history_summaryenrichment_depth, confidence, fields_missing, sources_used

Save to the current working directory or wherever the user prefers (e.g., leads/inbound-enriched-[date].csv).

Secondary: Enrichment Report

## Lead Enrichment Report: [Date]

### Summary
- **Total leads enriched:** X
- **Deep enrichment:** X leads (Tier 1-2)
- **Standard enrichment:** X leads (Tier 3)
- **Minimal enrichment:** X leads (Tier 4)

### Data Quality
- **Fully enriched** (all required + valuable fields): X leads
- **Mostly enriched** (all required, some valuable): X leads
- **Partially enriched** (some required fields still missing): X leads
- **Could not enrich** (insufficient starting data): X leads

### Company Research
- **Unique companies researched:** X
- **Companies already in CRM:** X
- **Companies with active deals:** X (flag for deal owner)
- **Companies with signal history:** X

### Stakeholder Discovery
- **Total stakeholders found:** X across Y companies
- **Economic buyers identified:** X
- **Champions identified:** X
- **Full buying committees mapped:** X companies

### Relationship Flags
- **Leads already in CRM:** X (update status, don't create duplicates)
- **Previously contacted leads:** X (check outreach history before re-engaging)
- **Companies with active deals:** X (coordinate with deal owner)
- **Warm intro paths found:** X

### Cost
- **Enrichment tool credits used:** [breakdown by tool]
- **Cost per lead:** [average]

### CSV saved to: [path]

Handling Edge Cases

Lead with only an email, nothing else:

  1. Extract domain → look up company
  2. Search "[name] [company]" on LinkedIn/web
  3. If still can't identify: flag as enrichment_failed with reason, recommend manual lookup
  4. Don't waste paid API credits on truly unidentifiable leads — web search first

Same company appears multiple times (multiple inbound leads):

  • Research the company ONCE, apply to all leads
  • Stakeholder discovery runs once per company, not per lead
  • Note the multi-lead signal: "3 people from [Company] came inbound — buying committee forming?"

Lead claims a title that doesn't match LinkedIn:

  • Trust LinkedIn over self-reported form data (people sometimes inflate titles on forms)
  • Note the discrepancy: "Form says 'VP of Engineering', LinkedIn says 'Senior Engineer'"
  • Use the LinkedIn title for qualification purposes

Company recently renamed, merged, or was acquired:

  • If the company domain redirects, follow the redirect
  • Note the corporate action: "[Company] was acquired by [Parent] in [date]"
  • Qualify against the current entity, not the historical one

Person left the company since filling the form:

  • If LinkedIn shows a different company than the form submission, flag it
  • Note: "Lead submitted via [Company] but now at [New Company] as of [date]"
  • Qualify both companies if both are potentially relevant

Enrichment tool rate limits or failures:

  • If primary tool fails, fall back to secondary (web search is always available)
  • If both fail for a specific lead, mark as enrichment_partial and move on
  • Never block the entire batch because one lead failed

Very high volume (100+ leads):

  • Batch company research first (deduplicate companies)
  • Parallelize person research via Task agents (15-20 leads per batch)
  • Skip stakeholder discovery for Tier 4 and untiered leads
  • Provide cost estimate before running paid enrichment tools

Personal email domains:

  • If form had a company field: use it even though email is personal
  • If no company info: attempt LinkedIn search by name
  • Last resort: flag as company_unidentified, enrich person only

Update Protocol

After enrichment is complete, update the source systems:

  1. If CRM is configured: Create/update lead records with enriched data. Don't overwrite existing data — append or fill gaps only.
  2. Flag duplicates: If enrichment reveals a lead already exists in CRM under a different email, flag the duplicate rather than creating a second record.

Tools Required

  • Web search — primary fallback for all research
  • SixtyFour or Orthogonal — company + person lookup (optional, enhances depth)
  • LinkedIn scraper (Apify) — person profile enrichment (optional)
  • Company-contact-finder — stakeholder discovery
  • CRM access — relationship checks (HubSpot, Salesforce, CSV)
  • Read/Write — CSV I/O and config management
  • Task tool — for parallelizing enrichment across large lead batches

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