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 account-selection-frameworkgit 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/account-selection-framework)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/account-selection-framework"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/account-selection-framework/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/account-selection-framework"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/account-selection-framework.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 | $0.00049 | $0.02732 |
| Opus 5 | $0.00024 | $0.01366 |
| Sonnet 5 | $0.00010 | $0.00546 |
| Haiku 4.5 | $0.00005 | $0.00273 |
Grade A, and why
account-selection-framework 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 — 273 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Selection - Framework
How to build, score, stage, and manage target account lists for ABM campaigns.
The Account Selection Principle
ABM starts with accounts, not leads. You're choosing who to pursue before anything else. Every dollar of ad spend and every BDR hour gets concentrated on accounts that actually fit your ICP.
The math matters: To close $1M in ARR from ABM with a $50K ACV, 25% close rate, and 75% qualification rate, you need ~3,250 target accounts in your campaigns (working backwards through stage conversion benchmarks).
How Many Accounts Do You Need?
Reverse-Engineering from Revenue Target
Revenue Target ÷ ACV = Deals Needed
Deals ÷ Close Rate ÷ Qualification Rate ÷ Considering Rate ÷ Interested Rate ÷ Aware Rate
= Total Target Accounts
Example:
$1,000,000 ÷ $50,000 = 20 deals
20 ÷ 0.25 ÷ 0.75 ÷ 0.20 ÷ 0.30 ÷ 0.55 = ~3,250 accounts
Stage Conversion Benchmarks (ABX Benchmarks)
| Stage | Definition | Conversion to Next |
|---|---|---|
| Identified | All accounts targeted in campaign | 55% become Aware |
| Aware | 50+ ad impressions | 30% become Interested |
| Interested/Engaged | 5+ ad clicks OR 10+ engagements | 20% become Considering |
| Considering | Booked a demo / signed up for trial | Close rate applies |
| Selecting | Open deal in pipeline | Win rate applies |
The headline number: across these stages, roughly 2.5% of targeted accounts convert to pipeline (an open, qualified opportunity) and about 0.6% close. So a ~3,250-account program produces ~80 opportunities and ~20 deals at a $50K deal size.
Account Selection Criteria
Layer 1: Firmographic Fit
| Criteria | Example | Source |
|---|---|---|
| Company Size | SMB (50-500) or Mid-Market (500-2000) | Clay, Apollo, LinkedIn |
| Revenue | $5M+ annual revenue or comparable funding | Clay, Crunchbase |
| Industry | Digital-first (SaaS, eCommerce, EdTech, FinTech, HealthTech) | Clay, LinkedIn |
| Location | USA, Canada, Australia, NZ, Ireland, Israel, Western/Northern Europe | Clay, Apollo |
| Business Model | Product-led growth, B2B SaaS | Manual + Clay enrichment |
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 · 273 lines · 49 tokens per session scan A 4c4db5c582ac
account-selection-framework is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 49 tokens to every session and 2,732 once invoked, about $0.0002 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
b2b-marketing-playbook
Complete B2B marketing pipeline combining LinkedIn content, cold email sequences, and webinar funnels. Designed for SaaS founders doing $0–$1M ARR who need predictable lead generation. By @WeiYipei.
abm-dev
Enrich B2B people and companies with abm.dev — verified work emails, LinkedIn profiles, firmographics, and AI research with a citation and confidence on every field. Use when the user needs contact data, company intelligence, email-finding, people search at target accounts, or CRM enrichment writeback.
bdr-enablement-generator
Generates account research briefs, personalized outreach sequences, persona-specific talk tracks, and objection handling frameworks for B2B SaaS BDR teams. Use when preparing BDR outreach for target accounts, building prospecting sequences, creating call scripts, developing objection responses, onboarding new BDRs, or…
competitive-battlecard-generator
Generates and maintains competitive battlecards from win/loss data, competitor intel, G2 reviews, and sales feedback. Use when building battlecards for a specific competitor, updating existing battlecards with new intel, preparing BDRs or AEs for competitive deals, analyzing win/loss patterns against a competitor, or…
marketing-ops-sop-generator
Generates standard operating procedures for B2B SaaS marketing operations: campaign naming conventions, UTM governance, tool administration, workflow QA, data hygiene, lead routing, and incident response playbooks. Use when setting up or auditing marketing ops processes, onboarding a new marketing ops hire…
abm-program-orchestrator
End-to-end ABM program design and orchestration for B2B SaaS mid-market and enterprise motions. Use when building a target account list, designing account tiering criteria, planning ABM channel orchestration, coordinating BDR outreach with marketing campaigns, defining ABM measurement frameworks, or running an ABM…