Auto-Composed for Your Task
Wisdom Curation
Stop guessing what to use. MEGA Code assembles the right skills, strategies, and trajectories for the task.
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Expert Skills
Vetted, refined, and battle-tested
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Atomic Sub-Skills
Decomposed behavioral units
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Wisdom Edges
Cross-skill connections
Explore the Skill Network
Browse skill domains, explore atomic sub-skills, try presets, and see how skills are composed in real time.
How the Wisdom Graph Forms
Scroll or use arrow keys to step through each stage — from a single skill node to a fully connected intelligence network.
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Skill NodeA Skill Appears
Every coding pattern starts as a single skill node — a reusable capability your agent can learn.
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Atomic PrimitivesDecomposed into Atomics
The skill breaks down into atomic primitives — the smallest building blocks of agent capability.
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Skill DomainsMore Skills Emerge
Your coding sessions produce many skill domains, each with their own atomic primitives.
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Context LinksContext Binds Them
Atomics from different domains connect by shared context — forming a unified skill graph.
What Wisdom Means
Wisdom in MEGA Code is composed of four types of reusable intelligence, each carrying a distinct causal judgment and serving a different role in the self-evolving loop.
Skills
What to doReusable procedural knowledge — the executable units composed into an execution plan. Distilled from session traces and retrieved for each new task at query time.
Strategies
When to do itDecision rules and judgment criteria that decide which skills apply and how to sequence them. Encoded as conditions, not procedures.
Curation patterns
How to sequence itWhich skills and strategies worked in what order and under what conditions. Planning-level wisdom that prevents invoking the right skill in the wrong context.
Optimization trajectories
What to try nextWhich changes were attempted, what improved, and what broke. Higher-order wisdom that guides exploration vs. exploitation on future tasks.
See it on a real optimization run.
Pick a MEGA run — multi-hop QA, agent security hardening, or RAG retrieval — and step through every iteration with full access to files and git history as the score climbs.
See the demo