hermes-dreaming: Staged self-improvement engine for Hermes-style memory, skill, and fact updates with explicit review and apply

by asimons81 · MCP Server · ★ 96

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

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.

ai-agentsclimcpmemoryopen-sourcepythonself-improvement

Quick Facts

Stars96
Forks10
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score72/100
Open Issues2
Last Updated2026-08-01
Created2026-05-26
Platformscli, mcp, python
Est. Tokens~15k

About hermes-dreaming

hermes-dreaming A standalone, open-source staged self-improvement engine for Hermes-style memory, user, skill, and fact updates. It scans explicit source inputs, stages proposed changes in a reviewable artifact directory, and only writes to live state after an explicit apply step. Hermes plugin This repo now ships as a proper Hermes plugin too.

Compatible Skills

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

  • no-no-debug — semantic(0.18)+complementary+rare_topics+same_lang+similar_pop+shared_platform (61%)
  • OSA — semantic(0.29)+complementary+same_lang+similar_pop+shared_platform (55%)
  • mnemo-cortex — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)
  • yantrikdb-hermes-plugin — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (52%)

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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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