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.
git clone --depth 1 https://github.com/gtmagents/gtm-agentsWrote 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/commands/gtmagents/gtm-agents/analyze-competitive-landscape)<a href="https://agentmods.dev/commands/gtmagents/gtm-agents/analyze-competitive-landscape"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/analyze-competitive-landscape/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/commands/gtmagents/gtm-agents/analyze-competitive-landscape"><img src="https://agentmods.dev/badge/commands/gtmagents/gtm-agents/analyze-competitive-landscape.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 | $0.00022 | $0.00374 |
| Opus 5 | $0.00011 | $0.00187 |
| Sonnet 5 | $0.00004 | $0.00075 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
analyze-competitive-landscape 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 8d 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.
What it actually says
Command: analyze-competitive-landscape
Inputs
- scope – product line, segment, or geo focus.
- audience – exec | product | sales | marketing.
- window – analysis timeframe (30d, 60d, quarter, custom).
- format – deck | memo | docset.
- signals – optional URLs/files to include (research, CRM stats, win/loss exports).
Workflow
- Signal Aggregation – pull intel from research feeds, CRM, sales notes, and market news.
- Threat Assessment – evaluate competitor moves, assign severity/confidence, link to metrics.
- Differentiation Mapping – highlight core strengths/weaknesses vs each rival.
- Action Planning – recommend counter plays, investments, or messaging pivots per stakeholder.
- Executive Packaging – format narrative, visuals, and action register for the requested format.
Outputs
- Competitive brief (deck/memo) with threat levels, trends, and insights.
- Action register with owners, due dates, and success metrics.
- Appendix with referenced signals, quotes, and supporting data.
Agent/Skill Invocations
market-insights-director– leads synthesis + executive narrative.battlecard-program-manager– feeds differentiation + plays into the brief.market-signal-trackerskill – structures signals + metadata.executive-briefing-kitskill – enforces storytelling + action format.
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.
- 8d ago First seen · 35 lines · 22 tokens per session scan A b19a992ce431
analyze-competitive-landscape is a command published in the GitHub repository gtmagents/gtm-agents (398 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 22 tokens to every session and 374 once invoked, about $0.0001 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.
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