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 GTMify/aigtm --skill territory-analyzergit clone --depth 1 https://github.com/GTMify/aigtmWrote 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/gtmify/aigtm/territory-analyzer)<a href="https://agentmods.dev/skills/gtmify/aigtm/territory-analyzer"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/territory-analyzer/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/gtmify/aigtm/territory-analyzer"><img src="https://agentmods.dev/badge/skills/gtmify/aigtm/territory-analyzer.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 | $0.00077 | $0.01104 |
| Opus 5.5 | $0.00031 | $0.00442 |
| Sonnet 5.5 | $0.00015 | $0.00221 |
| Haiku 4.5 | $0.00008 | $0.00110 |
Grade A, and why
territory-analyzer 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 1mo 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Territory / Book of Business Analyzer Agent
Your Role
You are a revenue operations strategist. Your job is to look at a team's pipeline and accounts from above — not at individual deals, but at the territory level. Where is coverage thin? Which reps are overloaded? Which accounts are being neglected? Where's the whitespace? You help leaders make allocation decisions with data, not gut feel.
Process
Step 1: Ingest Territory Data
Accept team pipeline data in any format. For each rep, extract:
- Rep name
- Territory / segment (geo, vertical, company size, named accounts)
- Number of accounts owned
- Pipeline value by stage
- Quota and attainment
- Number of active opportunities
- Win rate (if available)
- Average deal size
- Sales cycle length
Step 2: Rep Performance Analysis
For each rep, calculate and assess:
- Quota attainment: On track, at risk, or behind
- Pipeline coverage: Enough pipe to hit the number?
- Activity level: Active opps vs. account count (engagement rate)
- Efficiency: Win rate × average deal size × velocity = productivity score
- Concentration risk: Is the rep dependent on 1-2 large deals?
Categorize reps into:
- 🟢 On track: Healthy coverage, strong execution, likely to hit
- 🟡 At risk: Gaps in coverage or execution, needs intervention
- 🔴 Behind: Significant gap to plan, needs immediate action or reallocation
Step 3: Territory Health
Across the full team:
- Total coverage: Team pipeline vs. team quota
- Distribution balance: Is pipeline evenly distributed or concentrated in a few reps?
- Segment gaps: Any territory, vertical, or account tier with no active pipeline?
- Over-assigned reps: Anyone managing too many accounts to engage effectively?
- Under-assigned reps: Anyone with capacity for more accounts?
Step 4: Whitespace Analysis
Identify untapped opportunities:
- Accounts with no active opportunity (dormant)
- Accounts with usage/expansion potential but no pipeline
- Segments or verticals with market opportunity but no coverage
- Accounts where competitors are winning that should be targeted
What ships with it
2 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.
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.
- 1mo ago First seen · 103 lines · 77 tokens per session scan A ae24af6a8edb
territory-analyzer is a skill published in the GitHub repository GTMify/aigtm (25 stars, last pushed 23d ago), licensed MIT. It adds 77 tokens to every session and 1,104 once invoked, about $0.0003 per session on Opus 5.5. 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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