io.github.ataberk-xyz/gossipcat

MCPcommunity
v0.2.1io.github.ataberk-xyzUnknownUpdated 2mo agonpmGitHub

Multi-agent orchestration for Claude Code — consensus review, adaptive dispatch, skill learning

weightless in-context RL for code review — agents that learn from grounded signals, no weights touched. Live dashboard at — fleet vortex, signal stream, skill graduation grid, and consensus flow, all in real time. Per-agent leaderboard — accuracy, uniqueness, impact, hallucinations caught, and dispatch weight. Updated after every consensus round. The single-reviewer failure mode: a solo AI…

Automatically indexed from public sources. Not yet verified by the developer on Forge.Claim this listing →
2mo agoLast update
Package
Authorio.github.ataberk-xyz
LicenseUnknown
Version0.2.1
Sourcemcp-registry
npmgossipcat
GitHubgossipcat-ai/gossipcat-ai
Trust Status
B
60/100Good
✓Listed in Forge index+10/10
—Publisher identity verified+0/25
→ Publisher: run `forge publish` from the package repo to claim ownership
—Ed25519 publish signature+0/10
→ Included automatically when the publisher runs `forge publish`
—Domain verification+0/5
→ Publisher: host /.well-known/forge.json on the package homepage with { "publisher": "<github-login>" }
✓CVE scan · clean+30/30
✓Static analysis · clean+20/20
—npm provenance (Sigstore)+0/5
→ Publish from GitHub Actions with the --provenance flag
Paste into Claude Code, Cursor, or any AI assistant to fix all gaps
StatusCommunity-indexed
PublisherUnverified
SignatureUnsigned
Domain—
Provenance—
Dependencies✓ 60 resolved+ · none vulnerable
Tool surface—
Security scan✓ Cleanv0.5.6 · 20d ago
EvalsNone
IndexedJun 13, 2026

Verification confirms publisher identity (repo ownership), not code safety. The security scan covers known CVEs and suspicious install scripts — it cannot prove the absence of malicious code.

About

weightless in-context RL for code review — agents that learn from grounded signals, no weights touched. Live dashboard at — fleet vortex, signal stream, skill graduation grid, and consensus flow, all in real time. Per-agent leaderboard — accuracy, uniqueness, impact, hallucinations caught, and dispatch weight. Updated after every consensus round. The single-reviewer failure mode: a solo AI reviewer ships hallucinated bugs as critical findings 5–10% of the time in our internal usage. Gossipcat's…

Keywords
mcp