🗺️ Skill: Context Distiller (v1.1.0)

Executive Summary

The context-distiller is the master of high-fidelity information management for AI swarms. In 2026, the success of a mission depends on the quality and density of the context provided to the agent. This skill focuses on Automated Context Packing, building Symbolic Project Maps, and managing Agent Memory Rehydration to ensure that every session starts with maximum intelligence and minimum token noise.

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📋 Table of Contents

1. The Distillation Protocol
2. The "Do Not" List (Anti-Patterns)
3. Automated Context Packing (Repomix)
4. Symbolic Project Mapping
5. Agent Memory & Rehydration
6. Hierarchical Inheritance
7. Reference Library

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🛠️ The Distillation Protocol

Before initiating a new mission or subproject, the Distiller MUST:

1. Codebase Scan: Use rg and list_directory to map the active module's boundaries.
2. Essence Extraction: Apply the Impeccable Distill logic—identify the 20% of code that provides 80% of the value. Strip away "Noise" (boilerplate, unused imports, redundant logic).
3. Symbol Indexing: Generate a list of critical types and interfaces.
4. Inheritance Audit: load master rules from docs/AGENTS.md.
5. Local Rehydration: Create or read .gemini/GEMINI.md for mission-specific context.
6. Context Packing: Bundle all findings into a structured Markdown artifact.

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🚫 The "Do Not" List (Anti-Patterns)

Anti-Pattern	Why it fails in 2026	Modern Alternative
Thin Context	Leads to hallucinations and generic code.	Use High-Fidelity Packing.
Token Bloat	High latency and poor reasoning.	Use Semantic Filtering.
Flat History	Agent loses track of past decisions.	Use Memory Rehydration.
Manual Symbol Hunt	Slow and prone to missing definitions.	Use Symbolic Project Maps.
Ignoring Rules	Inconsistent architecture.	Use Hierarchical Inheritance.

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📦 Automated Context Packing

We use Repomix and gitingest to feed the models:

• Structure: Group files by domain (Logic, Types, Tests).
• Optimization: Exclude noise (node_modules, dist, git).
• Security: Mandatory secret scrubbing before ingestion.

See References: Context Packing for workflows.

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🐘 Symbolic Project Mapping

When projects are large, don't read everything—use a map.

• JSON Maps: Indexing every export and its file path.
• Symbolic RAG: fetching only relevant files based on symbol dependency.

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📖 Reference Library

Detailed deep-dives into Information Engineering:

• Impeccable DNA: High-fidelity design standards.
• Context Packing: Bundling artifacts for LLMs.
• Symbolic Mapping: Navigating large codebases.
• Memory Rehydration: Persistent mission context.
• Inheritance Models: Global vs Local rules.

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Updated: January 22, 2026 - 21:40