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 swan-gtm/gtm-skills --skill scale-b2b-qualified-pipelinegit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/scale-b2b-qualified-pipeline)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/scale-b2b-qualified-pipeline"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/scale-b2b-qualified-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/swan-gtm/gtm-skills/scale-b2b-qualified-pipeline"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/scale-b2b-qualified-pipeline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00051 | $0.02447 |
| Opus 5 | $0.00026 | $0.01223 |
| Sonnet 5 | $0.00010 | $0.00489 |
| Haiku 4.5 | $0.00005 | $0.00245 |
Grade A, and why
scale-b2b-qualified-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 9d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Scale B2B Qualified Pipeline on Meta
A step-by-step playbook for turning Meta Ads spend into qualified pipeline (SQLs) for B2B, not cheap lead volume. This is the end-to-end system written at full depth.
This playbook is the spine. For the deeper mechanics of any single piece, link out (one level) to the reference files in this folder:
audience-strategy.md,meta-capi-and-events.md,campaign-structure.md,creative-strategy.md,creative-cadence-operating-system.md,optimization-playbook.md,meta-ads-operating-system.md.
The core idea (read first)
Most B2B teams run a Meta campaign, get a pile of low-quality leads, decide "Meta doesn't work for B2B," and move their budget somewhere else. In almost every case the setup was the problem, not the campaign.
Meta is extremely good at finding exactly what you ask it to find. If you optimise for "Lead" and give it nothing else to go on, it does its job perfectly and brings you the cheapest form-fills it can. They are real leads, they just never turn into pipeline.
The whole game is to change what you are asking for. When you (1) give Meta the right data so it understands your actual ICP, and (2) feed back which leads become qualified pipeline, Meta learns to go and find more of those. It is the same optimisation engine, now working toward qualified pipeline instead of cheap volume.
Everything below is how you do that.
Step 1 - Map the market & build the audience targeting
Goal: give Meta a high-quality, matchable audience built from your real target market.
- Map your TAM. Tier every company Tier 1 -> Tier 4 by fit. Decide where you want to go.
- Pull the contacts at those companies. That is your Tier 1-4 contact list - this list is the audience targeting.
- Enrich the list. Raw B2B / work-email lists match badly on Meta. Enrich for personal email + mobile numbers to lift the match rate, or the custom audience lands too small to run. (Mobile advertiser IDs / MAIDs are a minor assist only - iOS App Tracking Transparency degraded them; personal email + mobile number do the heavy lifting.)
- Enrichment data: Contact Level.
- Enrich + auto-sync audiences into Meta: Clay / Freckle.
- Push the enriched list to Meta as a custom audience.
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.
- 9d ago First seen · 140 lines · 51 tokens per session scan A 4eafc8e1d689
scale-b2b-qualified-pipeline is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 51 tokens to every session and 2,447 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
ad-campaign-analyzer
Analyze cross-channel campaign data, quantify uncertainty, and propose evidence-labeled budget tests without overstating causality.
google-search-ads-builder
End-to-end Google Search Ads campaign builder. Performs deep keyword research (competitor SEO, review language mining, Reddit/HN community terminology, site audit), builds keyword architecture with funnel mapping and intent classification, creates ad group structure, generates headline/description variants, builds…
meta-ads-analyzer
Diagnose Meta Ads campaign performance and account gaps using Meta's actual system mechanics — including customer-journey coverage, Breakdown Effect, Learning Phase, Auction Overlap, Pacing, and Creative Fatigue. Use for performance diagnosis, account audits, full-funnel or TOF/MOF/BOF gap analysis, deciding what to…
ad-campaign-analyzer
Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.
competitor-ad-intelligence
Scrape competitor ads from Meta, TikTok, Google, and LinkedIn ad libraries, analyze creative patterns (hooks, formats, CTAs), reverse-engineer landing page funnels, and produce a strategic teardown with vulnerability analysis and counter-play recommendations. Use when you need to understand the competitive ad…
ad-angle-miner
Mine the highest-converting ad angles from customer reviews, Reddit complaints, support tickets, and competitor ads. Extracts actual pain language, competitor weaknesses, and outcome phrases that real buyers use. Outputs a ranked angle bank with proof quotes and recommended ad formats per angle.