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 vfarcic/dot-ai --skill dot-ai-request-dot-ai-featuregit clone --depth 1 https://github.com/vfarcic/dot-aiWrote 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/vfarcic/dot-ai/dot-ai-request-dot-ai-feature)<a href="https://agentmods.dev/skills/vfarcic/dot-ai/dot-ai-request-dot-ai-feature"><img src="https://agentmods.dev/badge/skills/vfarcic/dot-ai/dot-ai-request-dot-ai-feature/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/vfarcic/dot-ai/dot-ai-request-dot-ai-feature"><img src="https://agentmods.dev/badge/skills/vfarcic/dot-ai/dot-ai-request-dot-ai-feature.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 | $0.00046 | $0.00589 |
| Opus 5 | $0.00023 | $0.00295 |
| Sonnet 5 | $0.00009 | $0.00118 |
| Haiku 4.5 | $0.00005 | $0.00059 |
Grade A, and why
dot-ai-request-dot-ai-feature 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Request Feature in dot-ai Project
Write a feature request to a file in the target dot-ai project's tmp directory. The user will review and approve the write operation.
Projects
| Project | Directory | Description |
|---|---|---|
| dot-ai | ../dot-ai |
Main MCP server (API endpoints, tools, handlers) |
| dot-ai-ui | ../dot-ai-ui |
Web UI for visualizations and dashboard |
| dot-ai-controller | ../dot-ai-controller |
Kubernetes controller |
| dot-ai-stack | ../dot-ai-stack |
Stack deployment configs |
| dot-ai-website | ../dot-ai-website |
Documentation website |
Important: Do NOT use this skill to request features in the project you're currently working in. Just implement them directly.
Process
- Determine the target project from the user's request
- Determine the current project name from the directory name:
basename $(git rev-parse --show-toplevel) - Delete any existing feature-request.md in the target project's tmp directory (so the diff only shows new content)
- Write the feature request to:
../[target-project]/tmp/feature-request.md - Tell the user to open the target project and run
/process-feature-request
File Format
Write the feature request file with this content (replace [CURRENT_PROJECT] with the actual project name from step 2):
# Feature Request from [CURRENT_PROJECT]
**Requesting project directory:** ../[CURRENT_PROJECT]
## What We Need
[DESCRIPTION OF WHAT WE NEED AND WHY]
## Our Suggestion
(You decide the best approach)
- [Suggested approach or implementation idea]
## Context
[What this unblocks in our project]
## Notes
You're the expert on this codebase. Feel free to implement this differently if there's a better approach, or push back if this doesn't make sense.
## Response Instructions
After implementing this feature, write a response file to help the requesting project integrate:
1. Write to: `../[CURRENT_PROJECT]/tmp/feature-response.md`
2. Include: what was implemented, how to use it (API signatures, endpoints, types), and any usage examples
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 · 74 lines · 46 tokens per session scan A c2de1d38a2ed
dot-ai-request-dot-ai-feature is a skill published in the GitHub repository vfarcic/dot-ai (336 stars, last pushed 3d ago), licensed MIT. It adds 46 tokens to every session and 589 once invoked, about $0.0002 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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