neural-note-taker
Advanced associative memory helper for building relationships between facts and entities. Use when processing dense information to ensure context is preserved across long sessions.
Best use case
neural-note-taker is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Advanced associative memory helper for building relationships between facts and entities. Use when processing dense information to ensure context is preserved across long sessions.
Teams using neural-note-taker should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/neural-note-taker/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How neural-note-taker Compares
| Feature / Agent | neural-note-taker | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Advanced associative memory helper for building relationships between facts and entities. Use when processing dense information to ensure context is preserved across long sessions.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# Neural Note Taker Connect the dots in your memory. This skill implements a concept-linkage protocol. ## Core Process 1. **Entity Identification**: Extract names, tools, dates, and projects from current chat. 2. **Relationship Mapping**: Use `[[Target]] -> [[Related]]` notation. 3. **Context Weighting**: Assign priority based on recurrence. ## Commands - `memo_link [topicA] [topicB]`: Force a connection in long-term memory. - `memo_query [topic]`: Surface all associated nodes. ## Installation ```bash clawhub install neural-note-taker ```
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