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 varunk130/ai-gtm-skill-library --skill battle-scannergit clone --depth 1 https://github.com/varunk130/ai-gtm-skill-libraryWrote 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/varunk130/ai-gtm-skill-library/battle-scanner)<a href="https://agentmods.dev/skills/varunk130/ai-gtm-skill-library/battle-scanner"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/battle-scanner/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/varunk130/ai-gtm-skill-library/battle-scanner"><img src="https://agentmods.dev/badge/skills/varunk130/ai-gtm-skill-library/battle-scanner.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 | $0.00050 | $0.00930 |
| Opus 5.5 | $0.00020 | $0.00372 |
| Sonnet 5.5 | $0.00010 | $0.00186 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade A, and why
battle-scanner 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 — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Battle Scanner (ARMOR Analysis)
Deconstruct competitor strategy, surface exploitable weaknesses, predict their responses to your moves, and generate win/loss playbooks.
When to Use
- Pre-launch competitive assessment
- Competitive deal strategy
- Quarterly competitive landscape refresh
- New competitor entering the market
- Win rate declining against specific competitor
What You'll Need
Critical inputs (ask if not provided):
- Your product/company name and category
- Top 3-5 known competitors
Nice-to-have:
- Signal Radar output (competitive threat signals)
- Win/loss data from CRM
- Customer feedback mentioning competitors
Process
Step 1: 4-Ring Competitor Classification
Map every competitor into concentric rings:
| Ring | Type | Threat Level | Examples |
|---|---|---|---|
| Ring 1 | Direct -- same problem, same approach | Highest | Head-to-head feature competitors |
| Ring 2 | Indirect -- same problem, different approach | High | Alternative solution categories |
| Ring 3 | Adjacent -- different problem, could pivot | Medium | Companies with overlapping capabilities |
| Ring 4 | Substitute -- manual/DIY/status quo | Baseline | Spreadsheets, manual processes, in-house builds |
Step 2: ARMOR Deep Dive (per Ring 1-2 competitor)
Analyze each competitor across 5 dimensions:
| Dimension | What to Analyze | Key Questions |
|---|---|---|
| Architecture | Product architecture, tech stack, scalability | What are their technical limitations? Where is their tech debt? |
| Reputation | Brand perception, NPS, review scores, analyst position | Where do customers love them? Where do customers complain? |
| Moat | Defensibility -- network effects, data, switching costs, IP | What makes them hard to displace? What is their weakest moat? |
| Openings | Exploitable weaknesses -- complaints, gaps, pricing issues | Where are they vulnerable RIGHT NOW? |
| Response Prediction | How they will respond to your launch | Will they: fast-follow, price war, FUD, ignore, or acquire? |
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 · 82 lines · 50 tokens per session scan A 643db812f8c1
battle-scanner is a skill published in the GitHub repository varunk130/ai-gtm-skill-library (7 stars, last pushed 10d ago), licensed MIT. It adds 50 tokens to every session and 930 once invoked, about $0.0002 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 2026-08-31.
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