LongHorizon-Harness: The long-horizon computer-use harness

by AMAP-ML · Codex Skill · ★ 1.8k

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

Security audit verdict: SAFE · quality 70/100

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. Full audit →

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.

agentclaudeclaude-codeclaude-pluginclicodexcodex-desktopcodex-plugincuadeepseek-harness

Quick Facts

Stars1,762
Forks188
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score70/100
Open Issues20
Last Updated2026-10-21
Created2026-08-04
Platformsclaude-code, cli, codex, python
Est. Tokens~17k

About LongHorizon-Harness

LongHorizon-Harness Advancing Long-Horizon Agents for Real-World Tasks Operate the whole computer like a human. Work across desktop apps and the command line for dozens of hours. No state drift. Verifiable progress. Complex tasks carried through to completion. [![Be

Compatible Skills

These tools work well together with LongHorizon-Harness for enhanced workflows:

  • agentic-grpo-longhorizon — semantic(0.36)+complementary+rare_topics+same_lang+similar_pop+shared_platform (67%)
  • Toolathlon — semantic(0.40)+complementary+same_lang+similar_pop+shared_platform (64%)

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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:

View on GitHub → Browse Codex Skill tools