# Meta Agentic Chunking

> Breaking complex tasks into discrete sub-steps with isolated context for maximum precision.

- Skill: `jcorpac/meta-agentic-chunking` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/meta-agentic-chunking`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/meta-agentic-chunking/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/meta-agentic-chunking

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# 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.