Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add Belkins/revenue-os/plugin install revenue-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/commands/belkins/revenue-os/ros-landing-page)<a href="https://agentmods.dev/commands/belkins/revenue-os/ros-landing-page"><img src="https://agentmods.dev/badge/commands/belkins/revenue-os/ros-landing-page/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/belkins/revenue-os/ros-landing-page"><img src="https://agentmods.dev/badge/commands/belkins/revenue-os/ros-landing-page.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 | $0.00009 | $0.00651 |
| Opus 5 | $0.00005 | $0.00326 |
| Sonnet 5 | $0.00002 | $0.00130 |
| Haiku 4.5 | $0.00001 | $0.00065 |
Grade A, and why
ros-landing-page 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Landing Page Generator
Arguments: $ARGUMENTS
Load Data
!bash "${CLAUDE_PLUGIN_ROOT}/scripts/utils/storage.sh" read product
!bash "${CLAUDE_PLUGIN_ROOT}/scripts/utils/storage.sh" read icp
!bash "${CLAUDE_PLUGIN_ROOT}/scripts/utils/storage.sh" read value-prop
Instructions
If $ARGUMENTS is empty or "full":
Generate complete landing page structure:
-
Hero Section
- Headline (from value prop)
- Subheadline
- CTA button
- Social proof snippet
-
Problem Section
- Pain points (from ICP)
- Current alternatives
- Why they fail
-
Solution Section
- How product solves problems
- Key benefits
-
Features Section
- 3-6 features with benefits
- Icons/visual suggestions
-
Social Proof Section
- Testimonial format
- Logos section
- Stats/metrics
-
Pricing Section
- Tiers (if defined)
- Feature comparison
-
FAQ Section
- Objections as FAQs
- Trust-building answers
-
Final CTA
- Urgency/scarcity if appropriate
- Risk reversal (guarantee)
If $ARGUMENTS is "html":
Output as HTML/JSX components.
For specific sections:
Generate just that section.
Output Format
## Landing Page: [Product Name]
### Hero Section
**Headline**
> [Headline from value prop]
**Subheadline**
> [Subheadline]
**CTA Button**: [Button text]
**Social Proof**: [Brief proof]
---
### Problem Section
**Header**: [Problem-focused header]
[Pain point 1 with elaboration]
[Pain point 2 with elaboration]
[Pain point 3 with elaboration]
---
### Solution Section
**Header**: [Solution-focused header]
[How the product solves each problem]
---
### Features Section
**Feature 1**: [Name]
[Description and benefit]
**Feature 2**: [Name]
[Description and benefit]
**Feature 3**: [Name]
[Description and benefit]
---
### Social Proof Section
**Testimonial Template**
> "[Quote about transformation/results]"
> — [Name], [Role] at [Company]
**Stats**
- [Metric 1]
- [Metric 2]
---
### Pricing Section
[Pricing tiers if available]
---
### FAQ Section
**Q: [Objection as question]**
A: [Trust-building answer]
---
### Final CTA Section
**Headline**: [Action-oriented headline]
**CTA Button**: [Button text]
**Risk Reversal**: [Guarantee/trial offer]
---
### Complete HTML (if requested)
```html
[Full HTML structure]
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 · 156 lines · 9 tokens per session scan A eee6257db492
ros-landing-page is a command published in the GitHub repository Belkins/revenue-os (29 stars, last pushed 1mo ago), licensed MIT. It adds 9 tokens to every session and 651 once invoked, about $0.0000 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.
Other commands, from other repositories
maintain
Run automated maintenance — seeker finds bugs from pod logs and raises GitHub issues, fixer picks them up and creates PRs. Can run as a one-shot or scheduled via /schedule.
save
Save current work state for next session. Creates/updates .planning/ files (CHECKPOINT.md, STATE.md, settings.json) so Heimdall resumes with full context. NOT a rewind — saves forward progress. Run before closing a session or at any milestone.
plan-review
Multi-model plan review — AI models independently plan, then converge on the best approach.
review
Multi-model review — AI models independently review any document or general topic, then converge on findings.
code-review
Multi-model code review — AI models independently review code, then converge on findings.
autonomy
Set Heimdall autonomy (1=Guided, 2=Checkpoint, 3=Full Auto) — how much the agent does before asking. Use with a number, +/- to cycle, or no argument to show current.