IMCodes Shared Agent Context Layer Model Card

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IMCodes Shared Agent Context Layer Model Card

1. Role in Self Evolve

im4codes/imcodes focuses on shared agent context and memory with supervised execution and cross-agent audit capabilities. It matters because self-evolving agents need repeatable harness control, measurable feedback loops, and reusable skill procedures before claiming stable improvement.

2. Working Principle

establish shared context and memory channels across agent providers -> supervise execution and record cross-agent audit trails -> standardize communication primitives for multi-agent collaboration -> reduce fragmentation and improve reproducibility in mixed-agent systems

3. Evidence Path

public GitHub API metadata showed 131 stars, 11 forks, 1,818 commit pages, MIT license, and TypeScript-first implementation. GitHub metadata was captured via public API in this iteration (without authenticated token); this card marks counts as API-observed with possible rate-limit drift.

4. Teaching Use

Use this card to explain Shared Agent Context, Memory, and Supervised Execution Layer: it shows how harness/runtime/benchmark layers convert agent behavior into reproducible and auditable engineering workflows.

5. Limits

The repository was not cloned in this iteration; no benchmark run, plugin install, workflow execution, or agent loop experiment was executed. Counts and claims are visible public-page/search signals unless independently revalidated later.