Borrowing it
Nothing to install: this file belongs to Othmane-Khadri/YALC-the-GTM-operating-system. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Othmane-Khadri/YALC-the-GTM-operating-system/main/.claude/skills/predictleads-lookalikes/SKILL.mdgit clone --depth 1 https://github.com/Othmane-Khadri/YALC-the-GTM-operating-systemWrote 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/othmane-khadri/yalc-the-gtm-operating-system/predictleads-lookalikes)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/predictleads-lookalikes"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/predictleads-lookalikes/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/othmane-khadri/yalc-the-gtm-operating-system/predictleads-lookalikes"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/predictleads-lookalikes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 23 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 26 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
- medium MCP Rug Pull · line 29 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00074 | $0.00681 |
| Opus 5 | $0.00037 | $0.00341 |
| Sonnet 5 | $0.00015 | $0.00136 |
| Haiku 4.5 | $0.00007 | $0.00068 |
Grade A, and why
predictleads-lookalikes 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 12d 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.
How it starts
The opening of the file, as written. The whole thing — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PredictLeads Lookalikes (account discovery)
Pulls up to 50 companies similar to a seed domain via PredictLeads' similar_companies endpoint. Returns ranked results with a similarity score (0–1) and (when available) a sentence explaining why each is similar. Cheapest possible discovery: 1 credit per seed domain.
When to use
- Expanding a target list from a known anchor account ("find more like Stripe")
- Sanity-checking that a prospect fits an existing client's pattern
- Building a watchlist of competitors for monitoring
- First step of
prospect-discovery-pipeline(but use that skill if outreach is downstream)
Don't use when: you need contacts/CMOs at the lookalikes (use prospect-discovery-pipeline); you want signal data on a single known company (use predictleads-signals).
Quick reference
# 50 lookalikes from a seed domain (1 credit)
npx tsx src/cli/index.ts signals:similar --domain stripe.com --limit 50
# Tighter list (still 1 credit per call)
npx tsx src/cli/index.ts signals:similar --domain linear.app --limit 20
# Read back from local SQLite (no API call)
npx tsx src/cli/index.ts signals:show --domain stripe.com --type similar
Cost
Always 1 credit per seed domain regardless of --limit. Result rows land in company_signals with signal_type='similar_company' and stay cached for 7 days.
Output shape
Each result is a domain + score + reason:
deel.com (score=0.85)
remote.com (score=0.849)
oysterhr.com (score=0.848) — Both provide global EOR services for distributed teams.
Reasons are populated for some seed domains and not others (PredictLeads inconsistent). Score above 0.80 typically means strong match in their model.
Common pitfalls
- Megacaps in results: PredictLeads returns market peers including SAP, Microsoft, Google for many B2B SaaS seeds. Filter by hand or via Crustdata
company_identifybefore going further. - Multiple seed merge: if you run two seeds (
stripe.comandlinear.app), results are stored separately by source domain. Dedupe in code or useprospect-discovery-pipelinewhich handles the merge. similar_companyrows have no event date: they're a lookup, not an event. Don't sort by event_date; sort by score (in payload) or position.
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.
- 12d ago First seen · 57 lines · 74 tokens per session scan A 0c32396b5778
predictleads-lookalikes is a skill published in the GitHub repository Othmane-Khadri/YALC-the-GTM-operating-system (301 stars, last pushed 23d ago), licensed MIT. It adds 74 tokens to every session and 681 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
cn-check
Install and run the Continue CLI (cn) to execute AI agent checks on local code changes. Use when asked to "run checks", "lint with AI", "review my changes with cn", or set up Continue CI locally.
kn-spec
Use when creating a specification document for a feature (SDD workflow).
kn-handoff
Use when a feature crosses repository boundaries and one side must hand work to the other - generates a self-contained frontend-to-backend brief or backend-to-frontend API contract.
kn-flow
Use when orchestrating a full Knowns spec or task wave through planning, implementation, review, integration, and verification, optionally using sub-agents when scopes are parallel-safe.
kn-research
Use when you need to understand existing code, find patterns, search project knowledge, investigate current external facts, or explore a large codebase before implementation.
kn-debug
Use when debugging errors, test failures, build issues, or blocked tasks — structured triage to fix to learn.