tether-ai — security grade SAFE, quality 65/100

Security audit verdict: SAFE · quality 65/100

No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

by tt-11-dd · Codex Skill · ★ 87

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

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About tether-ai

Tether A local-first AI coding workbench built on the Pi ecosystem Let DeepSeek and OpenAI-compatible models inspect, edit, and verify your repositories with explicit safety boundaries. English · 简体中文 · Download latest Tether is an Electron desktop agent for real codebases. It brings model calls, workspace tools, terminal commands, permission prompts, session history, and diff review into one local workbench. The UI and session data stay on your machine; model requests go directly to the provider or local gateway you configure, without a Tether relay. Why Tether DeepSeek first — custom Base URL, model discovery, and reasoning-level controls, plus OpenAI-compatible endpoints such as OneAPI, Ollama, and vLLM. Visible and controllable — inspect tool calls, command output, file changes, and context usage as work happens. Permission boundaries — Plan, Ask, Workspace, and Full Access modes. Recoverable edit

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Quick Facts

Stars87
Forks11
LanguageTypeScript
CategoryCodex Skill
LicenseMIT
Quality Score65.0741120487603/100
Last Updated2026-09-14
Created2026-08-15
Platformsclaude-code, codex, node
Est. Tokens~15k

Compatible Skills

These tools work well together with tether-ai for enhanced workflows:

  • pi-maestro-flow — semantic(0.41)+complementary+same_lang+similar_pop+shared_platform (64%)
  • agent-harness-generator — semantic(0.38)+complementary+same_lang+similar_pop+shared_platform (63%)
  • metaharness — semantic(0.38)+complementary+same_lang+similar_pop+shared_platform (63%)

tether-ai alternative? Top 6 similar tools

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

  • kasetto by pivoshenko · ⭐ 202

    📼 Declarative AI agent environment manager, written in Rust

  • open-jet by L-Forster · ⭐ 50

    A terminal coding agent, and a Python SDK for embedding on-device models in your own apps.

  • kindly-web-search-mcp-server by Shelpuk-AI-Technology-Consulting · ⭐ 388

    Kindly Web Search MCP Server: Web search + robust content retrieval for AI coding tools (Claude Code, Codex, C

  • tingly-box by tingly-dev · ⭐ 348

    Your Intelligence, Orchestrated. Every builder. Every team. Every agent. For Everyone.

  • deepcontext-mcp by Wildcard-Official · ⭐ 275

    DeepContext is an MCP server that adds symbol-aware semantic search to Claude Code, Codex CLI, and other agent

  • vibepod-cli by VibePod · ⭐ 165

    Unified CLI for running AI coding agents in isolated containers. Includes built-in local metrics collection, H

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Frequently Asked Questions

What is tether-ai?

tether-ai is Tether Agent. It is categorized as a Codex Skill with 87 GitHub stars.

What programming language is tether-ai written in?

tether-ai is primarily written in TypeScript. It covers topics such as aagent, ai, claude-code.

How do I install or use tether-ai?

You can find installation instructions and usage details in the tether-ai GitHub repository at github.com/tt-11-dd/tether-ai. The project has 87 stars and 11 forks, indicating an active community.

What license does tether-ai use?

tether-ai is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to tether-ai?

The top alternatives to tether-ai on Agent Skills Hub include kasetto, open-jet, kindly-web-search-mcp-server. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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:

View on GitHub → Browse Codex Skill tools