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/prospect-discovery-pipeline/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/prospect-discovery-pipeline)<a href="https://agentmods.dev/skills/othmane-khadri/yalc-the-gtm-operating-system/prospect-discovery-pipeline"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/prospect-discovery-pipeline/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/prospect-discovery-pipeline"><img src="https://agentmods.dev/badge/skills/othmane-khadri/yalc-the-gtm-operating-system/prospect-discovery-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 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 25 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 67 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 74 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 | $0.00097 | $0.01457 |
| Opus 5 | $0.00048 | $0.00728 |
| Sonnet 5 | $0.00019 | $0.00291 |
| Haiku 4.5 | $0.00010 | $0.00146 |
Grade A, and why
prospect-discovery-pipeline 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.
How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prospect Discovery Pipeline
End-to-end pipeline: PredictLeads lookalikes → ICP filter → Crustdata CMO finder → multi-signal enrichment → 2 LinkedIn variants drafted with per-lead personalization. Pauses for user review before any expensive operation. Always quotes credit cost up front.
When to use
- Building a target account list anchored on 1–2 known clients
- Generating a campaign-ready batch (10–25 leads with full signal context)
- Producing 2 A/B-testable LinkedIn message variants tied to actual signal data per lead
Don't use when: ad-hoc lookup of one company (use predictleads-signals); just lookalike domains without contacts (use predictleads-lookalikes); enriching a list you already have qualified leads for (use signals:enrich --result-set directly).
The 5-phase flow
Always follow this order. Quote credit cost before each phase.
Phase 1 — Discovery (2 PL credits for 2 anchors)
npx tsx src/cli/index.ts signals:similar --domain anchor1.com --limit 50
npx tsx src/cli/index.ts signals:similar --domain anchor2.com --limit 50
Merge into a candidate pool, dedupe by domain. Expect 30–80 unique candidates per pair.
Phase 2 — ICP filter (FREE, pause for user review)
Hand-filter the pool against the user's ICP criteria:
- Employee count (use Crustdata
company_identify— FREE — only when judgement uncertain) - Industry vertical (back-office SaaS, commerce infra, HR-tech, etc.)
- HQ region
- Marketing maturity proxies (visible content investment)
STOP and present the 10 finalists to the user before spending more credits. Surface any obvious gaps or weak fits. Wait for explicit approval.
Phase 3 — CMO finder (3 Crustdata credits, batch)
Single batch search across all 10 companies:
filters = {
op: 'and',
conditions: [
{ column: 'current_employers.company_website_domain', type: 'in', value: ['10 domains'] },
{ column: 'current_employers.title', type: '[.]', value: 'Marketing' },
{ column: 'current_employers.seniority_level', type: 'in', value: ['CXO', 'Vice President', 'Director'] },
],
}
limit: 50
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 · 132 lines · 97 tokens per session scan A 74793c946585
prospect-discovery-pipeline 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 97 tokens to every session and 1,457 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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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.