github Python analyzed a5efa89

mostlyharmless-ai/watercooler

github

Watercooler: your team's shared reasoning layer for agentic coding. Preserve the "why" around your code.

maintainer
mostlyharmless-ai
license
Apache-2.0
first seen
2026-07-04
last seen
2026-07-22
releases · 30d
4
short id
risk 56/100 · heuristic grade
C elevated
  • capability exposure inferred + 35
  • recent drift inferred + 12
  • tool safety inferred + 12
  • trust mitigators mixed − 3

inferred mixed

The A–E grade is our heuristic synthesis — a "review this" prompt, not a verdict. Each factor is tagged by what backs it: attested (a verifiable record), reported (a third party's claim), or inferred (our own heuristic, e.g. permissions). See methodology.

graded 9m ago · see ecosystem CVEs →

risk trajectory 1 movements
  • B · 32 C · 56
capability exposure grade factor +35
Inferred surface — each links to servers holding it:
vulnerabilities 0 CVEs

No known CVEs for this server.

tool safety 1 findings · grade factor +12
  1. high dangerous code

    committed secret: Slack token · dynamic exec: pickle.loads()

skills & danger signals github-tarball
prompt-surface shipped agent-instruction files + hidden-content / dangerous-code findings — quoted from the analyzed source

analyzed commit a5efa89 · analyzer v28 · 1d ago

skills & prompt files 21

danger signals4

other grade factors evidence elsewhere
embed badge readme-ready
live risk-grade badge preview [![MCP Observatory risk grade](https://mcpobservatory.com/servers/github:mostlyharmless-ai/watercooler/badge.svg)](https://mcpobservatory.com/servers/github:mostlyharmless-ai/watercooler/security)

Heuristic, inferred signals — false positives (legitimately powerful tools, forks, language ports) are expected. Treat each as "review this", not a verdict. See the ecosystem-wide picture on the security hub, or the fleet security of mostlyharmless-ai.