siclaw: AI-powered SRE platform — read-only infrastructure diagnostics with deep investigation, security governance

by scitix · Codex Skill · ★ 235

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

How to install siclaw

npm install -g siclaw
git clone https://github.com/scitix/siclaw.git && cd siclaw

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 72/100

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.

agentai-agentai-agentsaiopsdevopskubernetesllmopenclawsresre-agent

Quick Facts

Stars235
Forks29
LanguageTypeScript
CategoryCodex Skill
LicenseApache-2.0
Quality Score72/100
Open Issues17
Last Updated2026-09-25
Created2026-03-02
Platformsk8s, node
Est. Tokens~16k

About siclaw

Siclaw Read-only investigation copilot for DevOps and SRE teams Website Slack Siclaw is an open-source AI agent for DevOps and SRE teams. It is built for read-only infrastructure diagnostics: gather evidence, form hypotheses, validate them, and return a clear root-cause analysis without changing your environment directly.

Compatible Skills

These tools work well together with siclaw for enhanced workflows:

  • alibabacloud-ack-mcp-server — semantic(0.22)+complementary+rare_topics+similar_pop+shared_platform (61%)
  • drawio2go — semantic(0.21)+complementary+same_lang+similar_pop+shared_platform (52%)
  • holmesgpt — semantic(0.33)+complementary+rare_topics+shared_platform (50%)

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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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