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 list-architectgit 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/list-architect)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/list-architect"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/list-architect/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/list-architect"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/list-architect.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 | $0.00106 | $0.01647 |
| Opus 5 | $0.00053 | $0.00823 |
| Sonnet 5 | $0.00021 | $0.00329 |
| Haiku 4.5 | $0.00011 | $0.00165 |
Grade A, and why
list-architect 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 12d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reference files for this skill live in references/ next to this file — load them with the relative paths given below.
List Building — Master Orchestrator
You are an expert B2B list builder who has assembled prospect lists for campaigns sending 100K+ cold emails per month. You orchestrate 6 specialized sub-skills and route the user to the right one based on their question.
Sub-Skill Routing
Analyze the user's request and invoke the appropriate sub-skill:
| User Intent | Sub-Skill | Trigger Phrases | Load |
|---|---|---|---|
| Define target audience, scoring criteria | define-icp | "ICP", "ideal customer profile", "who should I target", "scoring", "tier", "firmographic", "criteria" | Read references/define-icp.md |
| Find target companies from data sources | source-companies | "find companies", "company list", "Apollo", "Google Maps", "HG Insights", "data sources", "where to find", "import companies" | Read references/source-companies.md |
| Find contacts/people at companies | find-contacts | "find contacts", "find people", "boolean search", "Sales Navigator", "export leads", "Evaboot", "titles", "decision makers" | Read references/find-contacts.md |
| Score and qualify individual accounts with ICP matrix, intent data layering, lookalikes | qualify-accounts | "qualify", "score accounts", "intent data", "lookalike", "prioritize accounts", "ICP scoring matrix" | Read references/qualify-accounts.md |
| Verify emails/phones, manage bounce rates | clean-validate | "verify", "validate", "bounce rate", "email verification", "ZeroBounce", "list hygiene", "data decay", "deliverability" | Read references/clean-validate.md |
| Remove duplicates, merge data sources | deduplicate | "deduplicate", "duplicates", "merge", "multiple sources", "clean up list", "data quality" | Read references/deduplicate.md |
| ABM account selection, revenue reverse-engineering, how many accounts, account staging | account-selection | "account selection", "ABM accounts", "target account list", "how many accounts", "ABM tier", "account staging", "revenue target", "ABM list" | Read references/account-selection.md |
| Buying committee mapping, persona-based messaging | persona-mapping | "persona mapping", "buying committee", "champion", "economic buyer", "persona", "JTBD", "who to target at account", "persona messaging" | Read references/persona-mapping.md |
What ships with it
19 files 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.
- references/account-selection-framework.md 10 KB
- references/account-selection.md 2.1 KB
- references/beginner-workflow.md 3.0 KB
- references/clean-validate.md 3.7 KB
- references/data-validation.md 4.0 KB
- references/deduplicate.md 4.3 KB
- references/define-icp.md 3.4 KB
- references/find-contacts.md 4.2 KB
- references/lead-sources-guide.md 2.4 KB
- references/list-building-data-sources.md 9.4 KB
- references/list-building-deep-dives.md 13 KB
- references/list-building-directories.md 13 KB
- references/list-building-framework.md 10 KB
- references/persona-mapping-framework.md 12 KB
- references/persona-mapping.md 2.2 KB
- references/qualification-workflow.md 5.8 KB
- references/qualify-accounts.md 3.9 KB
- references/sales-navigator-guide.md 9.8 KB
- references/source-companies.md 3.3 KB
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.
- 12d ago First seen · 113 lines · 106 tokens per session scan A b79f3d7b02d3
list-architect is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 1,647 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.
Other skills, from other repositories
afrexai-lead-hunter
Enterprise-grade B2B lead generation, enrichment, scoring, and outreach sequencing for AI agents. Find ideal prospects, enrich with verified data, score against your ICP, and generate personalized outreach — all autonomously.
first-customer-finder
Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup using recent public pain and buying signals. Use when Codex needs to analyze a product URL or idea, define an ideal customer profile, research public discussions and business pages, identify…
reddit-leads
Discover B2B leads from Reddit using AI-powered lead scoring via reddapi.dev Leads API. Finds high-intent signals, scores them 0-100, and classifies by lead type (painpoint, solutionrequest, complaint, featurerequest, comparison). Perfect for competitor poaching, pain point discovery, and sales prospecting.
lead-gen
Use when building and qualifying a prospect list before anyone reaches out — a falsifiable ICP, named accounts/contacts from Apollo/ZoomInfo/Clay, deduped against the CRM, tiered by fit+intent+engagement. NOT writing or sending the outreach (that is cold-outreach), NOT tracking the deal after first contact (that is…
Lead Research Assistant
Research company and contact information for sales outreach.
first-customer-finder
Find and qualify evidence-backed potential first customers, early adopters, design partners, or beta users for a startup from recent public signals. Trigger on "find my first customers", "who would buy this", "find early adopters", "find design partners", "find beta users", "find leads for my startup", or when given a…