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 agentmods add skills/avicorp/learning-dna-plugin/deploynpx skills add avicorp/learning-dna-plugin --skill deploygit clone --depth 1 https://github.com/avicorp/learning-dna-pluginWhat 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 | $0.00024 | $0.04059 |
| Opus 5 | $0.00012 | $0.02030 |
| Sonnet 5 | $0.00005 | $0.00812 |
| Haiku 4.5 | $0.00002 | $0.00406 |
Grade A, and why
deploy 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 2d 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 — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/deploy — Deploy Your Learning App
Usage
/learning-dna:deploy — choose a platform interactively
/learning-dna:deploy github-pages — deploy to GitHub Pages
/learning-dna:deploy netlify — deploy to Netlify
/learning-dna:deploy vercel — deploy to Vercel
Flow
Self-driving pipeline: After each step completes, proceed immediately to the next. Do NOT pause between steps unless the step explicitly requires user input. Mandatory user interaction points: platform selection (Step 2), platform-specific configuration questions (Step 3). All other transitions are automatic.
Step 1: Precondition Gate
- Check that
learning-app/directory exists. If missing → STOP. Tell the user:No learning app found. Create one first: /learning-dna:new-topic <topic-name> - Run the build:
cd learning-app && npm run build 2>&1- If build fails → STOP. Show the error and tell the user:
The app has build errors. Fix them first, or rebuild: /learning-dna:build-app
- If build fails → STOP. Show the error and tell the user:
- Verify
learning-app/dist/index.htmlexists. If missing → STOP with the same message as above.
Step 2: Platform Selection (MANDATORY unless argument provided)
If the user passed a valid argument (github-pages, netlify, or vercel), skip this step and use that selection.
Otherwise, use AskUserQuestion to present the three options:
| # | header | question | options |
|---|---|---|---|
| 1 | Deploy Platform | Where would you like to deploy your learning app? All three options are completely free. | GitHub Pages — "Free hosting tied to your GitHub repo. Best if your code is already on GitHub. URL: https://<username>.github.io/<repo>/" / Netlify — "Free tier with 100 GB bandwidth/month. Easiest setup — drag-and-drop or CLI. URL: https://<site-name>.netlify.app" / Vercel — "Free for personal projects. Fast global CDN. URL: https://<project>.vercel.app" |
Proceed to the matching Step 3 branch.
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.
- 2d ago First seen · 475 lines · 24 tokens per session scan A a8661541e381
deploy is a skill published in the GitHub repository avicorp/learning-dna-plugin (5 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 4,059 once invoked, about $0.0001 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 2026-08-31.
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