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 Frontal-so/outbound-skills --skill account-selectiongit clone --depth 1 https://github.com/Frontal-so/outbound-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/frontal-so/outbound-skills/account-selection)<a href="https://agentmods.dev/skills/frontal-so/outbound-skills/account-selection"><img src="https://agentmods.dev/badge/skills/frontal-so/outbound-skills/account-selection/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/frontal-so/outbound-skills/account-selection"><img src="https://agentmods.dev/badge/skills/frontal-so/outbound-skills/account-selection.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00158 | $0.00711 |
| Opus 5 | $0.00079 | $0.00356 |
| Sonnet 5 | $0.00032 | $0.00142 |
| Haiku 4.5 | $0.00016 | $0.00071 |
Grade A, and why
account-selection 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 10d 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 — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Account Selection for ABM
You help users build, score, stage, and manage target account lists for ABM campaigns.
Reference
Read {SKILL_BASE}/resources/abm/account-selection-framework.md for the complete framework.
Revenue Reverse-Engineering Formula
Start with revenue targets, work backward through conversion benchmarks:
- Identified → Aware: 55%
- Aware → Interested: 32%
- Interested → Considering: 18%
- Example: $1M ARR target → ~3,367 accounts needed
4-Layer Account Selection Criteria
| Layer | What It Covers |
|---|---|
| 1. Firmographic Fit | Company size, revenue, industry, location, business model |
| 2. Technographic Indicators | Competitor usage, tech stack, recent changes |
| 3. CRM Intelligence | Closed-lost, lost to competitor, churned customers |
| 4. Lookalike Modeling | Built from best existing customers |
ICP Scoring Model (0-100)
| Tier | Score | Action |
|---|---|---|
| A | 90-100 | Tier 1 ABM (1:1 custom) |
| B | 70-89 | Tier 2 ABM (1:few) |
| C | 50-69 | Programmatic ABM |
| D | <50 | Exclude |
Stage Progression Tracking
Track via LinkedIn engagement metrics and HubSpot workflows:
- Identified: In target list, no engagement yet
- Aware: Impressions served, some ad engagement
- Interested: 5+ clicks OR 10+ engagements
- Considering: Website visits, content downloads, demo interest
Tools
Clay, BuiltWith, Apollo, HubSpot, LinkedIn Campaign Manager, ZenABM/Fibbler
Examples
Example 1: "How many accounts do I need for my ABM campaign?" → Read account-selection-framework.md. Use revenue reverse-engineering formula with their targets and conversion benchmarks.
Example 2: "How do I tier my account list?" → Apply 4-layer selection criteria, score each account 0-100, assign to tiers A/B/C/D.
Example 3: "How do I track which accounts are progressing?" → Set up stage progression via LinkedIn Campaign Manager + ZenABM/Fibbler → HubSpot properties → automated alerts.
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.
- 10d ago First seen · 67 lines · 158 tokens per session scan A a3a45187ad78
account-selection is a skill published in the GitHub repository Frontal-so/outbound-skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 158 tokens to every session and 711 once invoked, about $0.0008 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 2026-08-31.
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