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 competitor-positioninggit 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/competitor-positioning)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/competitor-positioning"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/competitor-positioning/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/competitor-positioning"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/competitor-positioning.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 | $0.00138 | $0.01637 |
| Opus 5 | $0.00069 | $0.00818 |
| Sonnet 5 | $0.00028 | $0.00327 |
| Haiku 4.5 | $0.00014 | $0.00164 |
Grade A, and why
competitor-positioning 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Runs when a marketing team needs to see how competitors position themselves on their own websites and content — on a recurring cadence, before a messaging refresh, or ahead of a launch.
Produces a marketing briefing, not a signal feed: messaging matrix, positioning profiles, content gaps, white space, and recommended actions — every insight answering "what does this mean for our messaging?"
Pick the mode before doing anything
Keep a running file per competitor in your workspace (format: references/snapshot-format.md). At the start of every run, load those files and check when you last ran:
- Full snapshot — first run, or no prior snapshots, or last run more than 14 days ago. Capture everything.
- Delta mode — last run within 14 days. Research the same pages, but the output reports only what changed. A delta run that re-narrates the whole landscape buries the one tagline change that matters.
- Same-day repeat — already ran today? Ask before re-running; don't silently burn research effort on pages that haven't changed since the morning.
If you also keep business-signal notes on these companies (funding, hiring, leadership changes), load those too — they often explain why positioning shifted, e.g. a funding round preceding an enterprise-messaging pivot.
Confirm scope
Work from the confirmed competitor list. If a competitor profile already exists in this workspace (from a prior run or a competitor-monitoring setup), load it and confirm it's still current; otherwise ask the user for their domain and the competitors they care about, propose any obvious additions, and get an explicit yes on the final list before spending research time. Four or fewer competitors: analyze all. More: ask which to focus on this run — group them by category and let the user pick. Every competitor costs real research time; don't spend it on companies nobody asked about.
Capture your own baseline first
Before touching any competitor, capture your own positioning from the user's own domain (confirmed during setup) the exact same way you'll capture theirs — otherwise the messaging matrix has an empty row where "us" should be, and the comparison collapses into a list.
What ships with it
3 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.
- 12d ago First seen · 89 lines · 138 tokens per session scan A 18cedbe66d06
competitor-positioning is a skill published in the GitHub repository swan-gtm/gtm-skills (148 stars, last pushed yesterday), licensed MIT. It adds 138 tokens to every session and 1,637 once invoked, about $0.0007 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.
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