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/researchnpx skills add avicorp/learning-dna-plugin --skill researchgit 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 | $0.00016 | $0.00975 |
| Opus 5 | $0.00008 | $0.00487 |
| Sonnet 5 | $0.00003 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
research 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/research — Research and Summarize a Subtopic
Usage
/learning-dna:research <topic> <subtopic>
Example: /learning-dna:research kubernetes networking
Flow
Step 1: DNA Gate (MANDATORY)
- Check if
knowledge/LearningDNA.mdexists — if missing, STOP. Do not proceed. Tell the user: "You need to set up your Learning DNA first. Run/learning-dna:new-topic <topic>to get started." - Read
knowledge/LearningDNA.md(global DNA) - Check if
knowledge/{topic}/LearningDNA.mdexists (per-topic override) - Merge: per-topic values override global values → use merged DNA for all content generation
Step 2: Verify Topic Exists
- Check that
knowledge/{topic}/exists - If not, suggest running
/learning-dna:new-topic {topic}first
Step 3: Web Research
GitHub Repo Shortcut: If the subtopic argument is a GitHub URL (
https://github.com/owner/repo,github.com/owner/repo) or anowner/repopattern, read and follow the full flow inskills/inspect-repo/SKILL.md(starting from Step 2 — the DNA gate is already done) instead of performing web research. Skip Steps 3-6 and proceed directly to Step 7.
- Use WebSearch to find 10-15 high-quality sources: official docs, engineering blogs, books, academic papers
- Present the sources to the user in a numbered list with title, URL, and brief description
- Wait for user approval — never auto-approve sources. Ask: "Which sources should I use? (e.g., 1,3,5-8 or 'all')"
Step 4: Fetch and Process Sources
- Use WebFetch on approved sources
- Extract key information, concepts, and examples
Step 5: Structure Content Shaped by DNA
Write markdown following these DNA-driven rules:
Content Depth:
Brief→ 2-3 paragraphs per section, key takeaways only, bullet-point summariesStandard→ balanced explanation with examplesDetailed→ thorough explanations with context and nuanceComprehensive→ full explanations, background theory, trade-off analysis, edge cases
Example Style:
Minimal→ concepts only, few examplesCode-focused→ include code samples in every sectionReal-world scenarios→ practical, production-oriented examplesMixed→ blend of code and real-world 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.
- 2d ago First seen · 93 lines · 16 tokens per session scan A fa7a0ecf2603
research is a skill published in the GitHub repository avicorp/learning-dna-plugin (5 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 975 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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