Registry indexed

disco-like

Overview

Find lookalike companies via DiscoLike's 65M+ business domain database. Search by seed domains ("find companies like clay.com and apollo.io") or natural-language ICP text ("B2B cold email outreach"). Supports negation domains (exclude competitors/existing customers) and country filtering. Use when you already know 3-10 reference companies and want hundreds more that look like them. Outputs CSV ready for /blitz-list-builder or the email waterfall.

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Disco-Like

Lookalike company discovery. Give it 3-10 seed domains you know are a good fit; it returns hundreds of similar companies by domain, industry, and business characteristics. Useful for expanding from a small known-good list to a much bigger TAM without manual research.

When to use

You have 3-10 customer domains you love, want "more like these"
You want to expand a small client list into a full TAM
You have an ICP description but don't want to manually build Prospeo filters
Competitive / adjacent-market expansion

When NOT to use

You need PEOPLE, not companies (use Prospeo or Blitz after this)
Your ICP is extremely narrow or nascent (<5 seed examples exist)
Budget is tight — DiscoLike charges per call + per record; see cost section

Two search modes

Mode A — Seed domains (most common)

npx tsx scripts/discover.ts --domains "clay.com,apollo.io,outreach.io" --country US --limit 500 --out lookalikes.csv

DiscoLike finds companies with similar characteristics (industry mix, employee count range, business type, tech stack) to your seeds.

Mode B — Natural-language ICP

npx tsx scripts/discover.ts --text "B2B SaaS companies selling outbound sales software to RevOps teams" --country US --out lookalikes.csv

Uses DiscoLike's text matching. Less precise than seeds, but useful when you don't have named comparables.

Hybrid mode

npx tsx scripts/discover.ts --domains "clay.com" --text "outbound automation" --country US --out lookalikes.csv

Combines both — starts from seeds, expands via text semantics.

Negation (exclude existing customers / competitors)

npx tsx scripts/discover.ts \
  --domains "clay.com,apollo.io" \
  --negation-domains "yourcompany.com,yourbigcustomer.com" \
  --country US \
  --out lookalikes.csv

Always include your own domain + existing customers + known-unfit competitors. Saves enrichment cost downstream.

Inputs

DISCOLIKE_API_KEY (env) — from DiscoLike dashboard
Either --domains or --text (at least one required)
Optional: --negation-domains, --country, --limit, --max-companies

Outputs

CSV with columns: domain, company_name, industry, headcount_range, headcount, location_country, location_state, location_city, linkedin_url, description, source

All rows have source=discolike so you can mix with other list-builder outputs without collisions.

Cost

$0.10 per API call + $2.00 per 1,000 records returned
Default page size: 100 per call
A 500-company discovery = ~5 calls + 500 records ≈ $1.50
A 10,000-company discovery ≈ $10 + $20 = $30

Compare to Prospeo, which charges per export. DiscoLike is typically cheaper per company-discovered but more expensive per enriched contact (DiscoLike gives companies, not people).

Required step: Qualify with /icp-prompt-builder

This is a required step. Do not skip it.

Before pulling 5,000 companies, run DiscoLike on a small sample (50-100), then invoke /icp-prompt-builder:

Evaluate which of the 50 are actually good ICP fits
Refine your ICP description / negation list based on what DiscoLike returned
Only then scale to 5,000+

Why required: DiscoLike lookalike results are only as good as your seed domains. If 80% of the first 50 are wrong, you need to change seeds, not pay to pull more. At $0.10/call + $2/1K records, a wrong-seeded 10K pull costs $20-$30 in DiscoLike fees AND cascades into wasted email-finder fees downstream. Qualifying the first 50 catches bad seeds before they become expensive.

Recommended flow

/icp-onboarding → nail down seed companies (your best 5 customers)
/disco-like --domains="seed1,seed2,..." --limit=100 --out=sample.csv → sample run
/icp-prompt-builder → score the sample, tune ICP prompt
If sample quality is high, scale: /disco-like ... --limit=5000 --out=full.csv
/blitz-list-builder --domains-file=full.csv → find decision-makers at each
/email-waterfall → fill in emails
Upload to Smartlead

API details (reference)

Base URL: https://api.discolike.com/v1

Auth: x-discolike-key header

Endpoints:

Method	Path	Purpose
GET	`/count?domains=X&text=Y`	Total matching companies (before paying to pull)
GET	`/discover?domains=X&text=Y&country=Z&limit=100&offset=0`	Paginated lookalike results
GET	`/bizdata?domain=X`	Detailed data for a single domain

Data returned per company:

domain, name, description
industry_groups (weighted dict — script takes top industry)
employees (range string like "51-200")
address (country, state, city)
social_urls (script extracts LinkedIn company URL)

Rate limit: Conservative — script throttles at 5 concurrent, 10 req/sec. No 429s observed on normal runs.

Common gotchas

Seed domains must be clean bare domains. clay.com works, https://clay.com/ doesn't.
Text mode is fuzzier than you think. "Outbound sales" returns SaaS, agencies, consultancies — broad. Tighten with seeds.
No people data. DiscoLike is company-level. Always chain with Blitz or Prospeo for contacts.
Non-US coverage varies. US has deepest data. EU/APAC coverage is thinner; count may be misleading.
Check the count FIRST. Before paying for 10,000 records, run /count to confirm the universe actually has 10,000. Many narrow ICPs top out at 500-2000.

Scripts

scripts/discover.ts — main search + CSV output
scripts/count.ts — pre-check universe size before paying
scripts/bizdata.ts — single-domain lookup

What to do next

Run /icp-prompt-builder on your 50-company sample (required step above). Then either:

/blitz-list-builder to find owner contacts at each filtered domain, OR
/list-quality-scorecard directly if this is companies-only and you'll enrich another way

Or wait: if the 50-sample ICP fit was poor (<40% matches), don't scale. Change your seed domains and re-run with better inputs.

/icp-onboarding — defines the seed domains you'll use
/icp-prompt-builder — quality-check the first 50 results before scaling
/blitz-list-builder — chain to find contacts at each discovered company
/email-waterfall — fill missing emails after Blitz
/cold-email-starter-kit → 06-list-building-prospeo.md for broader list-building patterns

View source ↗

File metadata

name: disco-like
description: Find lookalike companies via DiscoLike's 65M+ business domain database. Search by seed domains ("find companies like clay.com and apollo.io") or natural-language ICP text ("B2B cold email outreach"). Supports negation domains (exclude competitors/existing customers) and country filtering. Use when you already know 3-10 reference companies and want hundreds more that look like them. Outputs CSV ready for /blitz-list-builder or the email waterfall.

View original text

---
name: disco-like
description: Find lookalike companies via DiscoLike's 65M+ business domain database. Search by seed domains ("find companies like clay.com and apollo.io") or natural-language ICP text ("B2B cold email outreach"). Supports negation domains (exclude competitors/existing customers) and country filtering. Use when you already know 3-10 reference companies and want hundreds more that look like them. Outputs CSV ready for /blitz-list-builder or the email waterfall.
---

# Disco-Like

Lookalike company discovery. Give it 3-10 seed domains you know are a good fit; it returns hundreds of similar companies by domain, industry, and business characteristics. Useful for expanding from a small known-good list to a much bigger TAM without manual research.

## When to use

- You have 3-10 customer domains you love, want "more like these"
- You want to expand a small client list into a full TAM
- You have an ICP description but don't want to manually build Prospeo filters
- Competitive / adjacent-market expansion

## When NOT to use

- You need PEOPLE, not companies (use Prospeo or Blitz after this)
- Your ICP is extremely narrow or nascent (<5 seed examples exist)
- Budget is tight — DiscoLike charges per call + per record; see cost section

## Two search modes

### Mode A — Seed domains (most common)

```bash
npx tsx scripts/discover.ts --domains "clay.com,apollo.io,outreach.io" --country US --limit 500 --out lookalikes.csv
```

DiscoLike finds companies with similar characteristics (industry mix, employee count range, business type, tech stack) to your seeds.

### Mode B — Natural-language ICP

```bash
npx tsx scripts/discover.ts --text "B2B SaaS companies selling outbound sales software to RevOps teams" --country US --out lookalikes.csv
```

Uses DiscoLike's text matching. Less precise than seeds, but useful when you don't have named comparables.

### Hybrid mode

```bash
npx tsx scripts/discover.ts --domains "clay.com" --text "outbound automation" --country US --out lookalikes.csv
```

Combines both — starts from seeds, expands via text semantics.

## Negation (exclude existing customers / competitors)

```bash
npx tsx scripts/discover.ts \
  --domains "clay.com,apollo.io" \
  --negation-domains "yourcompany.com,yourbigcustomer.com" \
  --country US \
  --out lookalikes.csv
```

Always include your own domain + existing customers + known-unfit competitors. Saves enrichment cost downstream.

## Inputs

- `DISCOLIKE_API_KEY` (env) — from DiscoLike dashboard
- Either `--domains` or `--text` (at least one required)
- Optional: `--negation-domains`, `--country`, `--limit`, `--max-companies`

## Outputs

CSV with columns: `domain, company_name, industry, headcount_range, headcount, location_country, location_state, location_city, linkedin_url, description, source`

All rows have `source=discolike` so you can mix with other list-builder outputs without collisions.

## Cost

- **$0.10 per API call** + **$2.00 per 1,000 records returned**
- Default page size: 100 per call
- A 500-company discovery = ~5 calls + 500 records ≈ $1.50
- A 10,000-company discovery ≈ $10 + $20 = **$30**

Compare to Prospeo, which charges per export. DiscoLike is typically cheaper per company-discovered but more expensive per enriched contact (DiscoLike gives companies, not people).

## Required step: Qualify with /icp-prompt-builder

**This is a required step. Do not skip it.**

Before pulling 5,000 companies, run DiscoLike on a small sample (50-100), then invoke `/icp-prompt-builder`:
1. Evaluate which of the 50 are actually good ICP fits
2. Refine your ICP description / negation list based on what DiscoLike returned
3. Only then scale to 5,000+

**Why required:** DiscoLike lookalike results are only as good as your seed domains. If 80% of the first 50 are wrong, you need to change seeds, not pay to pull more. At $0.10/call + $2/1K records, a wrong-seeded 10K pull costs $20-$30 in DiscoLike fees AND cascades into wasted email-finder fees downstream. Qualifying the first 50 catches bad seeds before they become expensive.

## Recommended flow

1. `/icp-onboarding` → nail down seed companies (your best 5 customers)
2. `/disco-like --domains="seed1,seed2,..." --limit=100 --out=sample.csv` → sample run
3. `/icp-prompt-builder` → score the sample, tune ICP prompt
4. If sample quality is high, scale: `/disco-like ... --limit=5000 --out=full.csv`
5. `/blitz-list-builder --domains-file=full.csv` → find decision-makers at each
6. `/email-waterfall` → fill in emails
7. Upload to Smartlead

## API details (reference)

**Base URL:** `https://api.discolike.com/v1`

**Auth:** `x-discolike-key` header

**Endpoints:**

| Method | Path | Purpose |
|---|---|---|
| GET | `/count?domains=X&text=Y` | Total matching companies (before paying to pull) |
| GET | `/discover?domains=X&text=Y&country=Z&limit=100&offset=0` | Paginated lookalike results |
| GET | `/bizdata?domain=X` | Detailed data for a single domain |

**Data returned per company:**
- `domain`, `name`, `description`
- `industry_groups` (weighted dict — script takes top industry)
- `employees` (range string like "51-200")
- `address` (country, state, city)
- `social_urls` (script extracts LinkedIn company URL)

**Rate limit:** Conservative — script throttles at 5 concurrent, 10 req/sec. No 429s observed on normal runs.

## Common gotchas

- **Seed domains must be clean bare domains.** `clay.com` works, `https://clay.com/` doesn't.
- **Text mode is fuzzier than you think.** "Outbound sales" returns SaaS, agencies, consultancies — broad. Tighten with seeds.
- **No people data.** DiscoLike is company-level. Always chain with Blitz or Prospeo for contacts.
- **Non-US coverage varies.** US has deepest data. EU/APAC coverage is thinner; count may be misleading.
- **Check the count FIRST.** Before paying for 10,000 records, run `/count` to confirm the universe actually has 10,000. Many narrow ICPs top out at 500-2000.

## Scripts

- `scripts/discover.ts` — main search + CSV output
- `scripts/count.ts` — pre-check universe size before paying
- `scripts/bizdata.ts` — single-domain lookup

## What to do next

**Run `/icp-prompt-builder`** on your 50-company sample (required step above). Then either:
- `/blitz-list-builder` to find owner contacts at each filtered domain, OR
- `/list-quality-scorecard` directly if this is companies-only and you'll enrich another way

**Or wait:** if the 50-sample ICP fit was poor (<40% matches), don't scale. Change your seed domains and re-run with better inputs.

## Related skills

- `/icp-onboarding` — defines the seed domains you'll use
- `/icp-prompt-builder` — quality-check the first 50 results before scaling
- `/blitz-list-builder` — chain to find contacts at each discovered company
- `/email-waterfall` — fill missing emails after Blitz
- `/cold-email-starter-kit` → `06-list-building-prospeo.md` for broader list-building patterns

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
CSV output is not sanitized against spreadsheet formula injection: fields returned by the DiscoLike API (company name, description, etc.) could begin with '=', '+', '-', or '@' and execute as formulas when opened in Excel or Google Sheets.
The script writes to an arbitrary user-supplied --out path without validation, which could overwrite unintended files if misused by an agent or prompt injection.
No explicit timeout or response-size limit is shown for the fetch calls, which could hang or consume excessive memory on a malicious or misbehaving API response.
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

More details

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  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "CSV output is not sanitized against spreadsheet formula injection: fields returned by the DiscoLike API (company name, description, etc.) could begin with '=', '+', '-', or '@' and execute as formulas when opened in Excel or Google Sheets.",
    "No OpenAgentSkill engagement data yet",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision"
  ],
  "agent_contract": {
    "task_input": "Use disco-like in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 65/100 Manual review",
      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "growthenginenowoslawski-disco-like (disco-like)",
      "install_command": "npx skills add growthenginenowoslawski/coldoutboundskills --skill disco-like",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "growthenginenowoslawski-disco-like",
      "task": "Use disco-like in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/growthenginenowoslawski-disco-like",
    "api": "https://www.openagentskill.com/api/agent/skills/growthenginenowoslawski-disco-like",
    "audit": "https://www.openagentskill.com/skills/growthenginenowoslawski-disco-like/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=growthenginenowoslawski-disco-like&task=Use%20disco-like%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20disco-like%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20disco-like%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/growthenginenowoslawski-disco-like/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/growthenginenowoslawski-disco-like"
  }
}

For the creator

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Creator: growthenginenowoslawski
Source: growthenginenowoslawski/coldoutboundskills
Indexed by: OpenAgentSkill community index

Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.

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This Registry indexed listing is attributed to growthenginenowoslawski but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.

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[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/growthenginenowoslawski-disco-like?metric=listed&label=Listed)](https://www.openagentskill.com/skills/growthenginenowoslawski-disco-like?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/growthenginenowoslawski-disco-like?metric=trust&label=Trust)](https://www.openagentskill.com/skills/growthenginenowoslawski-disco-like?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/growthenginenowoslawski-disco-like?metric=audit&label=Audit)](https://www.openagentskill.com/skills/growthenginenowoslawski-disco-like/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/growthenginenowoslawski-disco-like?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/growthenginenowoslawski-disco-like?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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disco-like

Overview

Disco-Like

When to use

When NOT to use

Two search modes

Mode A — Seed domains (most common)

Mode B — Natural-language ICP

Hybrid mode

Negation (exclude existing customers / competitors)

Inputs

Outputs

Cost

Required step: Qualify with /icp-prompt-builder

Recommended flow

API details (reference)

Common gotchas

Scripts

What to do next

Review the source

Source & usage notes

Agent access

For the creator

Registry indexed

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