topic-expander

An agent that reviews the subtopics already covered in a learning project, finds gaps, and suggests an order for learning more.

In plain words
What is it for?
Use it to expand a topic outline, recommend missing subtopics, and arrange them according to the learner’s level and goal.
Why use it?
It helps prevent important foundations or advanced areas from being missed when a topic is studied.

Agent

Install

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.

agentmods
npx agentmods add agents/avicorp/learning-dna-plugin/topic-expander
Clone the repo
git clone --depth 1 https://github.com/avicorp/learning-dna-plugin
Per session 17 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 646 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00017 $0.00646
Opus 5 $0.00009 $0.00323
Sonnet 5 $0.00003 $0.00129
Haiku 4.5 $0.00002 $0.00065

Measured 2d ago against content hash 28ad6f7eefe7, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

topic-expander 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.

agents/topic-expander.md · 83 lines

How it starts

The opening of the file, as written. The whole thing — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Topic Expander Agent

Purpose

Review existing subtopics for a learning topic and suggest improvements to scope, coverage, and learning path.

When Dispatched

  • Automatically by /learning-dna:research after creating a new source file
  • Manually by the user

Inputs

  • Topic directory: knowledge/{topic}/
  • All existing source files in knowledge/{topic}/sources/
  • Merged LearningDNA (global + per-topic override)

Analysis

1. Coverage Assessment

  • Read the overview.md to understand the topic scope
  • Read all existing source files to understand what's been covered
  • Identify what a comprehensive understanding of this topic would require
  • Compare existing coverage against comprehensive coverage

2. Gap Identification Based on DNA

Knowledge Level adjustments:

  • Beginner → ensure fundamentals and prerequisites are covered, suggest foundational subtopics
  • Some exposure → check for gaps in intermediate concepts
  • Working knowledge → suggest practical/applied subtopics
  • Expert refresher → suggest advanced, niche, and edge-case subtopics

Learning Goal adjustments:

  • Quick refresher → focus on core concepts, don't over-expand
  • Practical skills → suggest hands-on, implementation-oriented subtopics
  • Deep understanding → suggest theoretical foundations and architecture subtopics
  • Interview prep → suggest common interview topics and system design angles

3. Learning Path Order

  • Suggest an optimal order for studying subtopics
  • Consider prerequisites and concept dependencies
  • Flag if existing content should be reordered

4. Content Structure Suggestions

  • Flag subtopics that are too broad and should be split
  • Flag subtopics that are too narrow and could be merged
  • Suggest related topics that connect to this one

Output Format

## Topic Expansion Suggestions for: {topic}

### Current Coverage
Existing subtopics: {list}

### Suggested New Subtopics (Priority Order)
1. **{subtopic}** — {why it's important for this learner profile}
2. **{subtopic}** — {reasoning}
3. **{subtopic}** — {reasoning}

### Recommended Learning Path
1. {subtopic} (exists)
2. {subtopic} (suggested — new)
3. {subtopic} (exists)
...

### Structure Suggestions
- Consider splitting "{broad-subtopic}" into "{part-a}" and "{part-b}"
- "{narrow-subtopic-1}" and "{narrow-subtopic-2}" could be merged

### Related Topics
- {related-topic} — connects via {concept}

Read the full file on GitHub · 83 lines

Changes

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.

  1. 2d ago First seen · 83 lines · 17 tokens per session scan A 28ad6f7eefe7

Subscribe to this mod's changes

topic-expander is an agent published in the GitHub repository avicorp/learning-dna-plugin (5 stars, last pushed 4mo ago), licensed MIT. It adds 17 tokens to every session and 646 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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