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Agent skill

Targeted Prospecting

by gooseworks-ai in gooseworks-ai/goose-skills

Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals.

MITAuto-check passedSales & Support

Install Targeted Prospecting

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill targeted-prospecting -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills targeted-prospecting --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/lead-generation/capabilities/targeted-prospecting .claude/skills/targeted-prospecting && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
targeted-prospecting
GitHub stars
1.2k
Used in
1 other repo
Token cost
~7.5k tokens
SKILL.md length
1,675 words
Files
2
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals.

  • Works in 8 steps: Parse the Request → Find Target Companies → Extract & Deduplicate → …
  • Asked to find leads by industry
  • SKILL.md covers Setup, Workflow, APIs Used and Examples, plus 2 more sections
  • Calls curl, python3 and npx; reaches api.gooseworks.ai and acmestaffing.com; needs GOOSEWORKS_API_KEY

What it does

Targeted Prospecting is an agent skill from gooseworks-ai/goose-skills. Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals. Use when asked to find leads by industry, build an account list with specific titles, prospect companies that are actively hiring, or create a targeted outreach list filtered by company size, location, and hiring activity.

Its SKILL.md is about 7.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `skill.meta.json`).

It sits in Sales & Support, covering Cold outreach and Lead generation. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Asked to find leads by industry
  • Build an account list with specific titles
  • Prospect companies that are actively hiring
  • Create a targeted outreach list filtered by company size

Example prompts

  • “/targeted-prospecting”

Requirements

  • Python 3
  • Node.js
  • A credential in GOOSEWORKS_API_KEY

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Parse the Request
  2. Find Target Companies
  3. Extract & Deduplicate
  4. Find Decision Makers
  5. Enrich Contacts
  6. Hiring / Intent Signals
  7. Competitive Intel (Optional)
  8. Present Results

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • curl
    • python3
    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.gooseworks.ai
    • acmestaffing.com
    • betacorp.com
    • smallco.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • GOOSEWORKS_API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Targeted Prospecting loads about 7.5k tokens when it runs. Until then it costs about 88 tokens; SKILL.md has 1,675 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~88
When it runs · the whole SKILL.md, loaded when a task matches
~7.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 1,675 words, ~7,462 tokens.

Download SKILL.mdSave it as .claude/skills/targeted-prospecting/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
targeted-prospecting
description
Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals. Use when asked to find leads by industry, build an account list with specific titles, prospect companies that are actively hiring, or create a targeted outreach list filtered by company size, location, and hiring activity.
source
orthogonal

Targeted Prospecting — Industry + Decision Makers + Hiring Signals

Setup

Read your credentials from ~/.gooseworks/credentials.json:

bash
export GOOSEWORKS_API_KEY=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json'))['api_key'])")
export GOOSEWORKS_API_BASE=$(python3 -c "import json;print(json.load(open('$HOME/.gooseworks/credentials.json')).get('api_base','https://api.gooseworks.ai'))")

If ~/.gooseworks/credentials.json does not exist, tell the user to run: npx gooseworks login

All endpoints use Bearer auth: -H "Authorization: Bearer $GOOSEWORKS_API_KEY"

Build a prioritized prospect list for any industry. Finds companies matching your ICP, identifies decision makers by title, enriches with verified contact info, and layers on hiring/intent signals to prioritize who's ready to buy now.

Workflow

1. Parse the Request

Extract from the user's query:

  • Industry/vertical (required) — e.g., staffing, fintech, healthcare IT, construction
  • Decision maker titles (required) — e.g., COO, VP Engineering, Head of Marketing
  • Location (optional, default: US) — country, state, city, or region
  • Company size (optional) — employee count min/max, revenue floor
  • Hiring signal roles (optional) — job postings that indicate buying intent (e.g., "Scheduling Coordinator" = ops pain, "DevOps Engineer" = infra investment)
  • Max results (optional, default 15)
  • Company/product (optional) — if user mentions what they're selling, triggers competitive intel in Step 7
2. Find Target Companies

Run 2-3 search strategies in parallel:

Strategy A — Scrapegraph searchscraper (primary — most targeted results):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "user_prompt": "top {industry} companies in {location} with company name, website, employee count, and headquarters",
  "num_results": 15
}'

Best source for industry-specific company lists. Returns targeted results from industry directories, Inc 5000 lists, and trade publications. In testing, returned 28 staffing companies in a single call vs Fiber's noisy mix of tech giants and staffing firms.

Strategy B — Fiber NL company search (co-primary — best structured data):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "{industry} companies in {location} with {employee_min}+ employees",
  "pageSize": 20
}'

Returns structured company data with employee counts, domains, LinkedIn URLs, and descriptions. Caveat: For niche industries (staffing, construction, etc.), Fiber NL search often returns broad/noisy results mixed with unrelated companies. Filter results by industry keywords from the description, li_industries, and crunchbase_categories fields. Use company names field (not name_consensus) for the company name.

Strategy C — Nyne company search (supplemental — attempt, may return errors):

bash
# Step 1: POST to start search
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"nyne","path":"/company/search","body":{"query":"{industry} companies {location} {size_qualifier}"}}'
# Step 2: Poll with GET using request_id
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"nyne","path":"/company/search","query":{"request_id":"REQUEST_ID"}}'

Nyne is async — POST returns a request_id, poll with GET until complete (5-20s). Note: Nyne company search can return 400 errors depending on query format. If it fails, proceed with Scrapegraph + Fiber results — don't block on Nyne.

Scaling Up

For 20+ results, run parallel searches by sub-region or sub-vertical:

bash
# Parallel searches for different sub-regions
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "{industry} companies in New York with {size}+ employees",
  "pageSize": 15
}'

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "{industry} companies in California with {size}+ employees",
  "pageSize": 15
}'
3. Extract & Deduplicate

Merge results from all strategies. For each company, extract:

  • Company name
  • Domain / website URL
  • Employee count (primary size proxy — revenue data is often unavailable)
  • Headquarters / location
  • LinkedIn company URL (if returned by Fiber/Nyne)
  • Description / industry tags

Deduplicate by domain first, then by normalized company name. Apply user's size filters — use employee count as revenue proxy when revenue is unavailable (100+ employees ≈ $10M+ revenue as rough heuristic).

Enrich top companies with Brand.dev for industry context:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"brand-dev","path":"/v1/brand/retrieve","query":{"domain":"{company_domain}"}}'
4. Find Decision Makers

Cost ranking (25 results): Apollo $0.01 | Fiber $0.50 | Nyne/PDL $7.50. Always try Apollo first.

Best approach: Apollo search first, then Fiber for NL queries, then per-company fallbacks.

Primary — Apollo people search (cheapest at $0.01 flat):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/apollo/mixed_people/search \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "person_titles": ["{title_1}", "{title_2}"],
  "person_locations": ["{location}"],
  "per_page": 25
}'

Fallback — Fiber NL profile search ($0.02/record, good for broad industry queries):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/fiber/v1/natural-language-search/profiles \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "{title_1} or {title_2} at a {industry} company in {location}",
  "pageSize": 15
}'

This is the highest-yield approach. Returns decision makers across the industry with LinkedIn URLs, current titles, and company names.

Per-company fallback — Fiber NL profile search ($0.02/record) (for companies not covered above):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/fiber/v1/natural-language-search/profiles \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "query": "{title_1} or {title_2} at {company_name}",
  "pageSize": 5
}'

Per-company queries often return empty results, especially for large enterprises where C-suite profiles may not be indexed. Use this only for high-priority companies missing from the broad search.

Last resort — Nyne person search (EXPENSIVE: $0.30/record, async):

Only use if Apollo and Fiber returned insufficient results.

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/pdl/person/search \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"query":"{title} at {company_name} {location}"}'

Fallback — Scrapegraph website scrape (scrape the company's leadership page):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://{company_domain}/about",
  "user_prompt": "Extract names, titles, and any contact info for the leadership team. Identify anyone with these titles: {target_titles}"
}'

If /about returns 422, fall back to the homepage URL.

5. Enrich Contacts

For each decision maker found, run all of these in parallel:

Email discovery — Sixtyfour first (highest hit rate for small/mid-market domains):

bash
# Sixtyfour AI email finder (PRIMARY — found 9/12 emails in testing)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/find-email"}'
  "lead": {"first_name": "{first}", "last_name": "{last}", "domain": "{company_domain}"}
}'

# Hunter email-finder (supplemental — often returns null for small company domains)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"hunter","path":"/v2/email-finder","query":{"domain":"{company_domain}","first_name":"{first}","last_name":"{last}"}}'

# Tomba email-finder (supplemental — similar limitations to Hunter on small domains)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"tomba","path":"/v1/email-finder","query":{"domain":"{company_domain}","company":"{company_name}","first_name":"{first}","last_name":"{last}"}}'

# Tomba LinkedIn-to-email (if LinkedIn URL found in Step 4)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"tomba","path":"/v1/linkedin","query":{"url":"{linkedin_url}"}}'

In testing, Sixtyfour found emails for 9 out of 12 prospects where Hunter and Tomba returned null. Sixtyfour is the most reliable source for small/mid-market company domains. Still run all sources in parallel — each occasionally finds emails the others miss.

Phone discovery:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/find-phone"}'
  "lead": {"first_name": "{first}", "last_name": "{last}", "company": "{company_name}"}
}'

Sixtyfour find-phone had a 100% hit rate in testing (10/10 prospects).

Deep enrichment (fire early, don't block — takes 30-60s):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/enrich-lead"}'
  "lead_info": {
    "first_name": "{first}", "last_name": "{last}",
    "company": "{company_name}", "linkedin_url": "{linkedin_url}"
  },
  "struct": {
    "work_email": "Work email",
    "personal_email": "Personal email",
    "phone": "Phone number",
    "title": "Current job title",
    "bio": "Short professional bio"
  }
}'

Fiber kitchen-sink enrichment (if LinkedIn URL available — may return 400):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/kitchen-sink/person"}'
  "profileIdentifier": "{linkedin_url}"
}'

Kitchen-sink can intermittently return 400 errors regardless of parameter format. If it fails, proceed with Sixtyfour + Hunter + Tomba results — don't block on kitchen-sink.

Triple email verification — verify ALL found emails with 3 services:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"hunter","path":"/v2/email-verifier","query":{"email":"{email}"}}'
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"tomba","path":"/v1/email-verifier","query":{"email":"{email}"}}'
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/validate-email/single","body":{"email":"{email}"}}'

Take the consensus. Label each email as verified/unverified. Collect both work and personal emails.

6. Hiring / Intent Signals

Only run this step if the user specified hiring signal roles. This is the key differentiator for prioritization.

Primary — Scrapegraph searchscraper for hiring signals:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "user_prompt": "{industry} companies hiring {signal_role} in {location}, list company name, job title, and location",
  "num_results": 15
}'

Supplemental — Tavily for job board coverage:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"tavily","path":"/search"}'
  "query": "{industry} {signal_role} job opening {location}",
  "max_results": 10,
  "include_answer": false
}'

Then scrape top job board results for company names:

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "{job_board_url}",
  "user_prompt": "Extract all company names hiring for {signal_role}, with job title and location"
}'

Optional — Fiber job search (attempt, may be unreliable):

bash
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/job-search"}'
  "searchParams": {
    "job_titles": ["{signal_role}"],
    "industries": ["{industry}"]
  },
  "pageSize": 20
}'

Note: Fiber job-search with searchParams filters can return 400 errors. Attempt it but don't rely on it — Scrapegraph is the primary method for hiring signals.

Cross-reference: Match companies found hiring signal roles against the company list from Step 2. Matches become High Priority prospects. Companies hiring for signal roles that weren't in your original list are bonus leads — add them.

Growth signals: Check Fiber company data (from Step 4 kitchen-sink results) for headcount growth percentage. Companies growing >20% YoY are additional high-priority signals.

7. Competitive Intel (Optional)

Only run if the user mentioned their product/company. Research what the user sells and check prospects for competing solutions.

bash
# Research user's product
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://{user_company_domain}",
  "user_prompt": "What does this company sell? Describe the product in one sentence."
}'

# Find competitors
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "user_prompt": "competitors and alternatives to {user_product} for {industry}",
  "num_results": 5
}'

# Check each prospect's website for competing products (parallel)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/smartscraper"}'
  "website_url": "https://{prospect_domain}",
  "user_prompt": "Does this company use or mention: {competitor_1}, {competitor_2}, {competitor_3}? Check page content, footer, and embedded widgets."
}'

Flag prospects: Greenfield (no competitor detected) > Competitive displacement (uses a competitor — note which one) > Unknown.

8. Present Results

Output a prioritized table with full URLs (not markdown links — users need to copy-paste):

## Prospect List: {Title} at {Industry} Companies in {Location}

Found {N} companies with {M} decision makers identified.

### High Priority — Hiring Signal Detected
| # | Company | Website | Employees | Decision Maker | Title | Email | Email Status | Phone | Signal |
|---|---------|---------|-----------|---------------|-------|-------|-------------|-------|--------|
| 1 | Acme Staffing | https://acmestaffing.com | 250 | Jane Smith | COO | jane@acme.com | Verified | (555) 123-4567 | Hiring Scheduling Coordinator |

### Medium Priority — Matches ICP, No Signal Detected
| # | Company | Website | Employees | Decision Maker | Title | Email | Email Status | Phone | Notes |
|---|---------|---------|-----------|---------------|-------|-------|-------------|-------|-------|
| 5 | Beta Corp | https://betacorp.com | 180 | John Doe | VP Ops | john@beta.com | Verified | — | Growing 25% YoY |

### Lower Priority — Limited Data or Below Target Size
| # | Company | Website | Employees | Decision Maker | Title | Email | Phone | Notes |
|---|---------|---------|-----------|---------------|-------|-------|-------|-------|
| 10 | Small Co | https://smallco.com | 85 | — | — | — | — | Below 100 employee threshold |

### Summary
- **Companies found**: {N}
- **Decision makers identified**: {count}/{N}
- **With verified email**: {count}
- **With phone**: {count}
- **High priority (hiring signal)**: {count}
- **Medium priority (right profile)**: {count}
- **Lower priority (limited data)**: {count}

APIs Used

APIEndpointPurpose
Fiber/v1/natural-language-search/companiesFind companies by industry + size
Fiber/v1/natural-language-search/profilesFind decision makers by title + company
Fiber/v1/kitchen-sink/personEnrich person by LinkedIn URL or name+company
Fiber/v1/kitchen-sink/companyEnrich company data
Fiber/v1/job-searchJob postings (unreliable, attempt only)
Fiber/v1/validate-email/singleEmail verification
Nyne/company/searchAsync company search by industry
Nyne/person/searchAsync person search by company + role
Scrapegraph/v1/searchscraperWeb search for companies + hiring signals
Scrapegraph/v1/smartscraperScrape websites for leadership/competitive intel
Tavily/searchSupplemental web search for job boards
Hunter/v2/email-finderFind email by name + domain
Hunter/v2/email-verifierEmail verification
Tomba/v1/email-finderFind email by name + domain
Tomba/v1/linkedinEmail from LinkedIn URL
Tomba/v1/email-verifierEmail verification
Sixtyfour/find-emailAI email finder
Sixtyfour/find-phoneAI phone finder
Sixtyfour/enrich-leadAI deep enrichment
Brand.dev/v1/brand/retrieveCompany overview/context
Show full SKILL.md (661 more words)Show less

Examples

Example 1 — Staffing/recruiting (the Clay use case):

"Find COOs at US staffing firms with 100+ employees that are hiring Scheduling Coordinators"

bash
# Step 2: Find staffing companies (parallel)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "staffing and recruiting companies in the United States with 100 or more employees",
  "pageSize": 20
}'

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"nyne","path":"/company/search","body":{"query":"staffing recruiting firms US 100+ employees"}}'

curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "user_prompt": "top staffing and recruiting companies in the US with company name, website, employee count, and headquarters",
  "num_results": 15
}'

# Step 4: Find COOs (parallel, per company)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/profiles"}'
  "query": "COO or Chief Operating Officer or Head of Operations at {company_name}",
  "pageSize": 3
}'

# Step 5: Enrich (parallel, per person)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"hunter","path":"/v2/email-finder","query":{"domain":"{domain}","first_name":"{first}","last_name":"{last}"}}'
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/find-email","body":{"lead":{"first_name":"{first}","last_name":"{last}","domain":"{domain}"}}}'
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"sixtyfour","path":"/find-phone","body":{"lead":{"first_name":"{first}","last_name":"{last}","company":"{company}"}}}'

# Step 6: Hiring signals
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "user_prompt": "staffing companies hiring Scheduling Coordinator or Recruiting Coordinator in the US, list company name, job title, and location",
  "num_results": 15
}'

Example 2 — SaaS sales (fintech):

"Find VP Engineering or CTO at fintech startups with 50-200 employees in the US that are hiring DevOps engineers"

bash
# Companies
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "fintech startups in the United States with 50 to 200 employees",
  "pageSize": 20
}'

# Decision makers (per company)
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/profiles"}'
  "query": "VP Engineering or CTO at {company_name}",
  "pageSize": 3
}'

# Hiring signal
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"scrapegraph","path":"/v1/searchscraper"}'
  "user_prompt": "fintech companies hiring DevOps Engineer or Site Reliability Engineer in the US, list company name and job title",
  "num_results": 15
}'

Example 3 — Recruiting (healthcare in Texas):

"Find HR Directors at healthcare companies in Texas with 500+ employees"

bash
# Companies
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "healthcare companies in Texas with 500 or more employees",
  "pageSize": 20
}'

# Decision makers
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/profiles"}'
  "query": "HR Director or VP Human Resources at {company_name}",
  "pageSize": 3
}'

Example 4 — Simple, no hiring signals (construction):

"Build a prospect list of construction companies in California with Head of Safety as decision maker"

bash
# Companies
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/companies"}'
  "query": "construction companies in California",
  "pageSize": 15
}'

# Decision makers
curl -s -X POST $GOOSEWORKS_API_BASE/v1/proxy/orthogonal/run \
  -H "Authorization: Bearer $GOOSEWORKS_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"api":"fiber","path":"/v1/natural-language-search/profiles"}'
  "query": "Head of Safety or Safety Director or VP Safety at {company_name}",
  "pageSize": 3
}'

Error Handling

  • Fiber NL company search returns noisy results — For niche industries, Fiber often returns unrelated companies mixed in (e.g., tech giants alongside staffing firms). Filter results by li_industries, crunchbase_categories, or keywords in short_description. If too noisy, use Scrapegraph searchscraper as primary source instead
  • Fiber NL profile search returns empty per-company — Per-company queries often return 0 results, especially for large enterprises. Use a broad industry-wide query instead (e.g., "COO at a staffing company in the US") which yields 10-15x more results
  • Fiber kitchen-sink returns 400 — Can fail intermittently regardless of parameter format (profileIdentifier, slug, or full URL all tested). This appears to be an API reliability issue, not a format issue. Proceed with Sixtyfour + Hunter + Tomba for enrichment
  • Nyne returns 400 — Nyne company and person search can return 400 errors. Query format sensitivity is unclear. Don't block on Nyne — proceed with Scrapegraph + Fiber results
  • Fiber job-search returns 400 — Known issue with searchParams filters. Use Scrapegraph searchscraper for hiring signals instead
  • Smartscraper 422 on /about path — Fall back to scraping the homepage URL (no path appended)
  • Hunter/Tomba return null for email — Expected for small/mid-market company domains. In testing, Hunter and Tomba returned null for most staffing firms while Sixtyfour found 9/12. Always run Sixtyfour as primary email source
  • No hiring signal found — Not every industry/location has active job postings for specific roles. Mark as "No signal detected" — these are still valid medium-priority prospects

Tips

  • Scrapegraph is the best company finder for niche industries — In testing, Scrapegraph returned 28 targeted staffing companies vs Fiber's noisy mix. Use Scrapegraph as primary for industry-specific lists, Fiber as co-primary for structured data (employee counts, domains)
  • Broad profile search beats per-company search — One query for "COO at staffing companies in the US" returned 15 profiles. The same search run per-company (8 companies) returned only 2 profiles total. Always start with a broad industry-wide NL profile search
  • Sixtyfour is the #1 email finder — Found 9/12 emails in testing where Hunter and Tomba returned null. For small/mid-market company domains, Sixtyfour's AI approach dramatically outperforms pattern-based tools. Still run all sources in parallel for maximum coverage
  • Sixtyfour find-phone is highly reliable — 100% hit rate in testing (10/10 prospects). Always include phone discovery
  • Hiring signals are the #1 prioritization tool — A company actively hiring for a role your product replaces/supports is 3-5x more likely to buy. Scrapegraph searchscraper is the best source — found Randstad and Robert Half hiring for Scheduling Coordinators in a single call
  • Employee count is the best size proxy — Revenue data is rarely available from APIs. Use employee count: 50+ ≈ established, 100+ ≈ mid-market, 500+ ≈ enterprise
  • Fiber kitchen-sink may be unreliable — Can return 400 errors intermittently. Don't depend on it as the sole enrichment source — always have Sixtyfour running in parallel as fallback
  • LinkedIn URLs dramatically improve enrichment — When Fiber NL profile search returns LinkedIn URLs, feed them into Tomba-LinkedIn for email and Sixtyfour enrich-lead for deep context
  • Deduplicate aggressively — Multiple search strategies will return overlapping results. Dedup by domain first (most reliable), then by normalized company name
  • Hunter email-verifier is fast and reliable — Even when Hunter email-finder returns null, Hunter email-verifier is excellent for verifying emails found by Sixtyfour. Every email verified came back with score 89-100
  • Include title variations — Search for "COO OR Chief Operating Officer OR Head of Operations" to catch different title formats at the same level
  • Filter Fiber company results by industry — Use li_industries, crunchbase_categories, or keywords in short_description to filter out irrelevant companies from Fiber NL results

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/lead-generation/capabilities/targeted-prospecting of gooseworks-ai/goose-skills.

  • SKILL.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gooseworks-ai/goose-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Targeted Prospecting next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Targeted Prospecting compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Targeted Prospecting this skillgooseworks-ai/goose-skills1.2k1 repos~7.5kAutomated safety check: PassMIT
Lead Gen Tool Builderexplorium-ai/gtm-skills185—~1.8kAutomated safety check: NotesMIT
B2B Lead Generationminhnv0807/ai-business-skills609—~1.2kAutomated safety check: PassMIT
Google Maps Search API Skillbrowser-act/skills6.1k1 repos~1.6kAutomated safety check: PassMIT
Money Outreachiamzifei/show-me-the-money1k—~3kAutomated safety check: PassCustom licence
33 B2b Lead Genminhnv0807/ai-business-skills609—~3.1kAutomated safety check: PassMIT

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Categories

Questions about Targeted Prospecting

What does Targeted Prospecting do?

Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals. Targeted Prospecting is an agent skill from gooseworks-ai/goose-skills. Build a prospect list of companies with decision makers, verified contact info, and hiring/intent signals.

When should I use Targeted Prospecting?

Targeted Prospecting fits situations like: asked to find leads by industry; build an account list with specific titles; prospect companies that are actively hiring; create a targeted outreach list filtered by company size.

How do I install Targeted Prospecting in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill targeted-prospecting -a claude-code`. Or copy the skill folder (skills/lead-generation/capabilities/targeted-prospecting in gooseworks-ai/goose-skills) into .claude/skills/targeted-prospecting in your project. Claude Code loads it when a task matches its description.

How do I install Targeted Prospecting in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill targeted-prospecting -a codex`. Or copy the skill folder (skills/lead-generation/capabilities/targeted-prospecting in gooseworks-ai/goose-skills) into .agents/skills/targeted-prospecting in your project. Codex loads it when a task matches its description.

Can I use Targeted Prospecting in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill targeted-prospecting -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/targeted-prospecting, .gemini/skills/targeted-prospecting, .github/skills/targeted-prospecting and .opencode/skills/targeted-prospecting in your project.

What does Targeted Prospecting need to run?

Going by SKILL.md and its folder, Targeted Prospecting needs the command-line tools its instructions call (curl, python3 and npx) and credentials named GOOSEWORKS_API_KEY. Our summary lists: Python 3; Node.js; A credential in GOOSEWORKS_API_KEY.

Does Targeted Prospecting access the network?

SKILL.md names 4 domains. In commands or code: api.gooseworks.ai, acmestaffing.com, betacorp.com and smallco.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Targeted Prospecting safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Targeted Prospecting use?

Targeted Prospecting is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Targeted Prospecting use?

About 7.5k tokens (SKILL.md is roughly 30k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Targeted Prospecting?

Skills that share tags, products or a category with Targeted Prospecting: Lead Gen Tool Builder (explorium-ai/gtm-skills, 185 stars), B2B Lead Generation (minhnv0807/ai-business-skills, 609 stars), Google Maps Search API Skill (browser-act/skills, 6.1k stars) and Money Outreach (iamzifei/show-me-the-money, 1k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Targeted Prospecting?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.