SageFs: Sage Mode for F# development — REPL with solution or project loading, Live Testing for FREE, Hot Reload

by WillEhrendreich · MCP Server · ★ 85

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install SageFs

git clone https://github.com/WillEhrendreich/SageFs.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 63/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

ai-toolsfsharpfsharp-app-developmentfsharp-interactivelive-testingmcpmcp-clientmcp-serverreplvisual-studio-extension

Quick Facts

Stars85
Forks8
LanguageF#
CategoryMCP Server
LicenseMIT
Quality Score63/100
Open Issues7
Last Updated2026-11-04
Created2026-02-14
Platformscli, mcp, vscode
Est. Tokens~23k

About SageFs

SageFs You save. Tests pass. Browser updates. Under a second. A live F# engine — hot reload, live testing, AI-native — for every editor, for free. []() []() What is SageFs? SageFs is a live F# development engine.

Compatible Skills

These tools work well together with SageFs for enhanced workflows:

  • ChatIDE — semantic(0.19)+complementary+rare_topics+similar_pop+shared_platform (46%)
  • Lim-Code — semantic(0.17)+complementary+rare_topics+similar_pop+shared_platform (45%)
  • argus — semantic(0.16)+complementary+rare_topics+similar_pop+shared_platform (45%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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