finance-assistant: Personal finance copilot — real math, not AI guesses

by googlarz · MCP Server · ★ 50

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

How to install finance-assistant

pip install -r ~/.claude/skills/finance-assistant/requirements.txt

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 64/100

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.

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Quick Facts

Stars50
Forks11
LanguagePython
CategoryMCP Server
Quality Score64/100
Last Updated2026-10-30
Created2026-03-02
Platformsclaude-code, mcp, python
Est. Tokens~16k

About finance-assistant

Finance Assistant Real math. Real law. Your machine. A personal finance copilot that applies actual tax statute to your real numbers — not estimates, not vibes. Runs locally through Claude Code; your data never leaves your machine. Open source, free forever.

Compatible Skills

These tools work well together with finance-assistant for enhanced workflows:

  • korean-jangbu-for — semantic(0.31)+complementary+rare_topics+same_lang+similar_pop+shared_platform (60%)
  • frago — semantic(0.19)+complementary+rare_topics+same_lang+similar_pop+shared_platform (56%)

finance-assistant alternative? Top 6 similar tools

Looking for a finance-assistant alternative? If you're comparing finance-assistant with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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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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