gameops-investigator: Evidence-first game operations incident investigation agent with read-only MCP tools and reproducible

by nickname21kmr · MCP Server · ★ 76

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

Security audit verdict: SAFE · quality 65/100

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.

anomaly-detectionclaude-codegame-analyticsllm-evaluationmcpsqlitestreamlittext-to-sql

Quick Facts

Stars76
Forks1
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score65/100
Last Updated2026-09-25
Created2026-08-30
Platformsclaude-code, mcp, python
Est. Tokens~14k

About gameops-investigator

GameOps Investigator English overview — An evidence-first game operations incident investigation agent built around metric contracts, read-only SQL, cohort comparison, anomaly testing, and citation-checked reports.

Compatible Skills

These tools work well together with gameops-investigator for enhanced workflows:

  • text2sql-framework — semantic(0.23)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • dex — semantic(0.22)+complementary+rare_topics+same_lang+similar_pop+shared_platform (62%)
  • ParanoiaSkills — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (61%)

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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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