meta-agentic-chunking

Installation
SKILL.md

Meta-Agentic Chunking

Complex tasks often fail because the model tries to handle too much complexity at once.

The Chunking Workflow

  1. Decomposition: Breaking the user objective into small, independent sub-tasks.
  2. Context Isolation: For each sub-task, only load the files and skills relevant to that specific step.
  3. Handoff: Passing only the results of the sub-task to the next step, rather than the entire execution history.

Benefits

  • Reduced Hallucinations: Smaller context means fewer distractions.
  • Higher Fidelity: The model can follow specific instructions for a single phase (e.g., "Refactoring") without being distracted by requirements from another phase (e.g., "Deployment").

Best Practices

  • Clear Boundaries: Use task_boundary tools to signify the switch between chunks.
  • State Persistence: Keep a "Source of Truth" document (e.g., a plan.md) that tracks the results of all chunks.
Installs
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GitHub Stars
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First Seen
Jun 29, 2026
meta-agentic-chunking — jcorpac/ai-skills-library