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/new-topicnpx skills add avicorp/learning-dna-plugin --skill new-topicgit clone --depth 1 https://github.com/avicorp/learning-dna-pluginWrote 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/avicorp/learning-dna-plugin/new-topic)<a href="https://agentmods.dev/skills/avicorp/learning-dna-plugin/new-topic"><img src="https://agentmods.dev/badge/skills/avicorp/learning-dna-plugin/new-topic.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.03106 |
| Opus 5 | $0.00013 | $0.01553 |
| Sonnet 5 | $0.00005 | $0.00621 |
| Haiku 4.5 | $0.00003 | $0.00311 |
Grade A, and why
new-topic scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -o /dev/null -w "%{http_code}" http://localhost:5173 How it starts
The opening of the file, as written. The whole thing — 215 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/new-topic — Create a Complete Learning Topic
Usage
/learning-dna:new-topic <topic-name>
Example: /learning-dna:new-topic kubernetes
This is the main entry point. It runs the full pipeline end-to-end and finishes with a working learning app served on localhost.
Flow
Self-driving pipeline: This is an end-to-end 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: DNA questions (Step 1), per-topic override (Step 2), source approval (Step 5), quiz approval (Step 6). All other transitions are automatic.
Step 1: DNA Gate (MANDATORY)
-
Check if
knowledge/LearningDNA.mdexists -
If missing → STOP. Do not proceed. Run the DNA Creation Flow using AskUserQuestion:
a. Inform the user: "Before we create any topic, you need to set up your Learning DNA — this is your personal learner profile that shapes all content generated for you."
b. First AskUserQuestion call — collect 4 answers at once:
# header question options 1 Language What language should your learning content be in? English — "Content in English" / Spanish — "Contenido en español" / Hebrew — "תוכן בעברית" / Arabic — "المحتوى بالعربية" 2 Learner Who is this for? Kids (6-12) — "Simple language, fun analogies" / Youth (13-22) — "Engaging, school-oriented" / Professional (23-60) — "Industry terminology, career-focused" / Lifelong (60+) — "Curiosity-driven, clear structure" 3 Depth How deep should the content go? Brief — "Bullet-points, key takeaways only" / Standard — "Balanced explanation with examples" / Detailed — "Thorough coverage with extra context" / Comprehensive — "Full theory, edge cases, deep dives" 4 Examples What kind of examples do you prefer? Minimal — "Concepts only, few examples" / Code-focused — "Code samples in every section" / Real-world — "Practical, production scenarios" / Mixed — "Blend of code and real-world"
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.
- 4d ago First seen · 215 lines · 25 tokens per session scan A 278559a2eacc
new-topic is a skill published in the GitHub repository avicorp/learning-dna-plugin (5 stars, last pushed 4mo ago), licensed MIT. It adds 25 tokens to every session and 3,106 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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