| name | atlas-chunking |
| description | Break a concept into chunks and generate questions to explore. Used by Atlas+Scout primitives. |
| version | 1.0.0 |
| author | Hermes |
| license | MIT |
| metadata | {"hermes":{"tags":["chunking","ideation","exploration"],"related_skills":["scout-prioritization","brainstorming"]}} |
Atlas Chunking
Overview
Break a concept into chunks and generate questions to explore. First half of the Atlas+Scout compounding primitive (Nate's AI Meeting Takeaways).
Use Case: Prototype Nate's primitives:
- Atlas: Chunk concepts (e.g., "source-of-truth discipline").
- Scout: Prioritize chunks (e.g., "Is this worth working on?").
Input/Output
Input: A concept (e.g., "AI-enabled acquisitions").
Output: Chunks (subtopics) + questions (exploration prompts).
Procedure
- Source: When the input says "latest posts" or another moving target, resolve the source first. Prefer an available canonical connector/API; if unavailable, use the public RSS/feed/archive and record the source URL, lookback window, selected item count, and access limitations in the JSON.
- Decompose: Split each concept/post into 3–5 non-overlapping subtopics.
- Question: For each subtopic, generate 2–3 questions to explore.
- Validate: Ensure questions are open-ended and actionable.
- Persist + verify: For cron/downstream use, write the requested JSON artifact, then validate it with a real JSON parser and structural checks before reporting completion.
Example
delegate_task(
goal="Break 'AI-enabled acquisitions' into chunks and questions",
context="Use the Atlas chunking pattern: subtopics + exploration questions.",
toolsets=['terminal']
)
Verification
- Chunks: 3–5 subtopics, no overlaps.
- Questions: Open-ended, actionable.
- Output: JSON format (for Scout compatibility).
- Cron artifacts: See
references/cron-json-pattern.md for moving-source cron jobs. The pattern documents source resolution metadata, access caveats, JSON structure, and validation steps for downstream automation.