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 adaptico/adaptico-os --skill gtm-landinggit clone --depth 1 https://github.com/adaptico/adaptico-osWrote 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/adaptico/adaptico-os/gtm-landing)<a href="https://agentmods.dev/skills/adaptico/adaptico-os/gtm-landing"><img src="https://agentmods.dev/badge/skills/adaptico/adaptico-os/gtm-landing/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/adaptico/adaptico-os/gtm-landing"><img src="https://agentmods.dev/badge/skills/adaptico/adaptico-os/gtm-landing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Memory Poisoning · line 99 Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 | $0.00075 | $0.04656 |
| Opus 5 | $0.00037 | $0.02328 |
| Sonnet 5 | $0.00015 | $0.00931 |
| Haiku 4.5 | $0.00007 | $0.00466 |
Grade A, and why
gtm-landing 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 10d 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 — 380 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Page CRO Analysis
Default lens: a SaaS / AI software startup. Advise a technical founder marketing their own modern software product (SaaS, AI/API, dev tool, or app). Tailor every recommendation to that reader.
Stage-fit (
landing): Tier 1 Useful · Tier 2 Core · Tier 3 Useful. Appropriate at every served tier - generate with no stage note.
Full persona and general guidance: read
../gtm/templates/advisor-prompt.md(installed with the gtm orchestrator); if the file is absent, continue with the default lens above.
Skill Purpose
Perform a comprehensive Conversion Rate Optimization (CRO) analysis on any landing page. This skill produces a section-by-section teardown with prioritized, actionable fixes that directly impact conversion rates.
When to Use
- User provides a landing page URL and asks for conversion optimization
- User asks for landing page feedback, review, or audit
- User wants to improve signup, lead capture, or purchase rates
- Triggered by
/gtm landing <target>or/gtm cro <target>
Phase 0: Gather Context
Before fetching the page, run the orchestrator's Project Resolution. With a profile loaded, read PROFILE.md and pull the fields that frame the teardown - /gtm init captured them, and /gtm position / /gtm competitors may have sharpened them, so don't re-derive from the page what's already here:
- ICP, Secondary audience, Key pain points - who the page must convert and the pain it should name; these set the relevance bar for the Hero (Section 1), Value Proposition (Section 2), and Objection Handling (Section 5).
- Differentiator and Key messages - the positioning the page should lead with (set by
/gtm position//gtm competitors); judge the hero and value-prop copy against these, and have every rewrite reflect them rather than invent a new angle. - User-Added and AI-Researched competitors - the alternatives a visitor is weighing; use them to sharpen Objection Handling (Section 5) and the comparison-with-alternatives check. Read what's already in the profile - don't run full discovery (that's
/gtm competitors). - Primary channel today and Existing assets - where the page's traffic comes from; the hero is judged for message match against this source (Section 1).
- Tone and Avoid - the voice every rewrite and A/B-test copy must honor, and the claims the page must never make.
- Project type, Stage, and Main goal - frame the read: project type sets the expected Page Type and benchmark (Step 1), and the goal is the conversion the teardown optimizes toward.
- Then read any
YYYY-MM-DD-positioning.md,YYYY-MM-DD-competitor-report.md, orYYYY-MM-DD-gtm-audit.mdin the folder for detail.
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.
- 10d ago First seen · 380 lines · 75 tokens per session scan A 459208abd540
gtm-landing is a skill published in the GitHub repository adaptico/adaptico-os (18 stars, last pushed 24d ago), licensed MIT. It adds 75 tokens to every session and 4,656 once invoked, about $0.0004 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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