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/find-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/find-lookalikes)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/find-lookalikes"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/find-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/find-lookalikes"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/find-lookalikes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
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 →
- high Privilege Escalation · line 36 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 37 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 | $0.00090 | $0.00514 |
| Opus 5 | $0.00045 | $0.00257 |
| Sonnet 5 | $0.00018 | $0.00103 |
| Haiku 4.5 | $0.00009 | $0.00051 |
Grade A, and why
find-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 13d 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.
What it actually says
Find Lookalikes
I'll wrap signals:similar. Ask for a seed domain, shell out to PredictLeads, dedupe + cache, and surface the lookalike list as a result set.
When This Skill Applies
- "find lookalikes for [domain]"
- "companies similar to [name]"
- "show me lookalike accounts"
- "expand from this company"
- "discover similar prospects"
NOT this skill (use prospect-discovery-pipeline instead):
- "find prospects like our best client" — that's the full 5-phase orchestrator (lookalikes → ICP filter → CMO finder → signals → variants).
NOT this skill (use enrich-with-signals instead):
- "enrich these companies with PredictLeads signals" — that pulls jobs/news/funding/tech for an EXISTING list.
Workflow
Step 0 — Ask for seed domain
"What's the seed domain or company URL?"
Step 1 — Validate (basic domain format)
Step 2 — Shell out
cd ~/Desktop/gtm-os && set -a && source .env.local && set +a && \
npx tsx src/cli/index.ts signals:similar --domain <domain>
Single-command side-effecting → shell out per the benchmark.
Step 3 — Parse output
The CLI emits the lookalike list + result set id + cache hit/miss flags.
Step 4 — Render
See references/example-output.md.
Step 5 — Offer follow-ups
"Want me to (a) enrich these with signals via
enrich-with-signals, (b) qualify them viaqualify-leads?"
Notes
- Requires
PREDICTLEADS_API_KEYin~/.gtm-os/.env. - Cached 7 days per seed domain.
- ~1 PredictLeads credit per call.
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.
- 13d ago First seen · 59 lines · 90 tokens per session scan A 665a7d7ffa1b
find-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 90 tokens to every session and 514 once invoked, about $0.0005 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.
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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.