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A four-pane neurosymbolic development environment (eNSAID) designed as a cognitive-relief layer for human-machine co-orbit. Built on AffineScript (TEA architecture) and Ephapax (Gossamer webview), with friction monitoring (Vexometer), neural output gating (Anti-Crash); planned TypeLL (type) and ECHIDNA (proof) verification for the symbolic layer.
panll is an MCP server entry in ChatHome Skills, sourced from GitHub MCP Search and scored 51/100. A four-pane neurosymbolic development environment (eNSAID) designed as a cognitive-relief layer for human-machine co-orbit. Built on AffineScript (TEA architect. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding panll to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
git clone https://github.com/hyperpolymath/panll.gitReview the upstream source before running this command.
Scan coverage: Install metadata only — no README or document body could be retrieved, so the absence of signals here is not evidence of safety.
Automated signals from install metadata and a bounded skill body — not a full code security audit and not a recursive source or dependency review.
{
"fullName": "hyperpolymath/panll"
}A four-pane neurosymbolic development environment (eNSAID) designed as a cognitive-relief layer for human-machine co-orbit. Built on AffineScript (TEA architecture) and Ephapax (Gossamer webview), with friction monitoring (Vexometer), neural output gating (Anti-Crash); planned TypeLL (type) and ECHIDNA (proof) verification for the symbolic layer.