Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AlexisMarasigan/coldoutboundskills --skill disco-likegit clone --depth 1 https://github.com/AlexisMarasigan/coldoutboundskillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/alexismarasigan/coldoutboundskills/disco-like)<a href="https://agentmods.dev/skills/alexismarasigan/coldoutboundskills/disco-like"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/disco-like/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/alexismarasigan/coldoutboundskills/disco-like"><img src="https://agentmods.dev/badge/skills/alexismarasigan/coldoutboundskills/disco-like.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00098 | $0.01760 |
| Opus 5 | $0.00049 | $0.00880 |
| Sonnet 5 | $0.00020 | $0.00352 |
| Haiku 4.5 | $0.00010 | $0.00176 |
Grade A, and why
disco-like scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to disco-like — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
--domainsor--text(at least one required) - Optional:
--negation-domains,--country,--limit,--max-companies
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 155 lines · 98 tokens per session scan A 08f979eb1cdf
disco-like is a skill published in the GitHub repository AlexisMarasigan/coldoutboundskills (4 stars, last pushed 4mo ago), licensed MIT. It adds 98 tokens to every session and 1,760 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to disco-like, differing in 0 lines, and is treated as a copy.
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