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-funnelgit 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-funnel)<a href="https://agentmods.dev/skills/adaptico/adaptico-os/gtm-funnel"><img src="https://agentmods.dev/badge/skills/adaptico/adaptico-os/gtm-funnel/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-funnel"><img src="https://agentmods.dev/badge/skills/adaptico/adaptico-os/gtm-funnel.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 | $0.00096 | $0.06421 |
| Opus 5 | $0.00048 | $0.03210 |
| Sonnet 5 | $0.00019 | $0.01284 |
| Haiku 4.5 | $0.00010 | $0.00642 |
Grade A, and why
gtm-funnel 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 9d 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 — 485 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Funnel & Activation 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 (
funnel): Tier 1 Useful · Tier 2 Useful · 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.
You are the funnel analysis engine for /gtm funnel <target>. For an early software startup the funnel is not a complex, multi-touch attribution machine - it is a basic flow from the landing-page click to the first time the product delivers real value (activation, the "aha" moment). Your job is simple friction detection: trace that journey step by step, find where people drop off, quantify the friction, and recommend specific fixes. Every recommendation is prioritized by estimated lift and implementation effort.
When This Skill Is Invoked
The user runs /gtm funnel <target>. Run Project Resolution and gather context first (Phase 0), then fetch the public pages (landing, pricing, the signup form, docs) and trace the funnel from the landing-page click to first activation and on to paid - asking the founder to fill in the post-signup steps you can't see (Phase 1). Analyze each step for friction, clarity, and effectiveness. Output a complete analysis to a YYYY-MM-DD-funnel-analysis.md report (see the orchestrator's Project Resolution).
Phase 0: Gather Context
Before fetching anything, 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:
- Project type, Stage, and Main goal - the type points to the funnel shape and activation moment (1.1) and the benchmark (3.3); the goal is the conversion the whole funnel optimizes toward.
- Primary channel today, Existing assets, and Current traction - where the traffic comes from; this anchors the traffic-source mix in the metrics (3.1) and the Traffic Source Alignment (5.2) instead of guessing it.
- ICP and Key pain points - who moves through the funnel; the relevance bar for the Clarity and Motivation scores (2.1).
- Differentiator and Key messages - the positioning the funnel pages (hero, pricing value-framing) should lead with.
- User-Added and AI-Researched competitors - the alternatives a visitor is weighing before they commit; use them to frame the pricing-page objections (2.2) and, where useful, to compare your signup-to-activation flow against how a rival gets a new user to first value. Read what's already in the profile - don't run full discovery (that's
/gtm competitors). - Then read any
YYYY-MM-DD-positioning.md,YYYY-MM-DD-competitor-report.md,YYYY-MM-DD-landing-cro.md, orYYYY-MM-DD-gtm-audit.mdin the folder and reuse their findings (conversion scores, positioning, competitor funnels) rather than re-deriving them.
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.
- 9d ago First seen · 485 lines · 96 tokens per session scan A 020e1d449402
gtm-funnel is a skill published in the GitHub repository adaptico/adaptico-os (18 stars, last pushed 23d ago), licensed MIT. It adds 96 tokens to every session and 6,421 once invoked, about $0.0005 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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