devswarm: AI Development Environment (ADE) for parallel coding with multiple AI assistants

by devswarm-ai · Codex Skill · ★ 136

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Security audit verdict: SAFE · quality 56/100

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-codingai-development-environmentamazon-qaugmented-development-environmentbranch-isolationclaude-codecode-reviewcodexdesktop-appdeveloper-tools

Quick Facts

Stars136
Forks10
CategoryCodex Skill
Quality Score56/100
Open Issues11
Last Updated2026-09-24
Created2025-09-03
Platformsclaude-code, codex
Est. Tokens~2k

About devswarm

DevSwarm — Parallel AI Coding for Your Desktop DevSwarm is an AI Development Environment (ADE): a desktop app for running multiple AI coding assistants in parallel, each isolated on its own Git branch. Spin up as many workspaces as you need. Each one is an isolated Git worktree paired with an AI assistant terminal and a code editor — separate working directory, independent build, its own branch.

Compatible Skills

These tools work well together with devswarm for enhanced workflows:

  • hal-9000 — semantic(0.43)+complementary+similar_pop+shared_platform (55%)
  • claude-code-starter — semantic(0.38)+complementary+similar_pop+shared_platform (53%)
  • kandev — semantic(0.37)+complementary+similar_pop+shared_platform (53%)

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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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