hermes-incident-commander: Autonomous SRE agent that detects, diagnoses, and self-heals production incidents

by Lethe044 · Codex Skill · ★ 70

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

Security audit verdict: CAUTION · quality 71/100

Flagged: install step in a code block runs a script from an untrusted host. 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.

anthropicatroposautomationautonomous-agentsclaude-aidevopshermes-agentincident-responsekubernetesllm-agent

Quick Facts

Stars70
Forks3
LanguagePython
CategoryCodex Skill
LicenseMIT
Quality Score71/100
Last Updated2026-09-21
Created2026-03-12
Platformsclaude-code, k8s, python
Est. Tokens~17k

About hermes-incident-commander

⚕ Hermes Incident Commander An autonomous SRE agent that detects, diagnoses, and heals production infrastructure - then learns from every incident it resolves. Originally built on Hermes Agent by NousResearch for the "Show us what Hermes Agent can do" hackathon.

Compatible Skills

These tools work well together with hermes-incident-commander for enhanced workflows:

  • holmesgpt — semantic(0.39)+complementary+shared_fw(anthropic)+rare_topics+same_lang+shared_platform (70%)
  • sibyl-research-system — semantic(0.22)+complementary+shared_fw(anthropic)+rare_topics+same_lang+similar_pop+shared_platform (70%)
  • AutoResearch-SibylSystem — semantic(0.22)+complementary+shared_fw(anthropic)+rare_topics+same_lang+similar_pop+shared_platform (70%)

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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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