reflex: A small open decision model: state + typed questions -> calibrated probabilities

by kshetrajna12 · AI Tool · ★ 172

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How to install reflex

git clone https://github.com/kshetrajna12/reflex

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 60/100

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.

Quick Facts

Stars172
Forks19
LanguagePython
CategoryAI Tool
LicenseMIT
Quality Score60/100
Open Issues4
Last Updated2026-10-06
Created2026-09-17
Platformspython
Est. Tokens~15k

About reflex

reflex A tiny "decision model" you can run on your own GPU. You give it some information (a support ticket, a document, a photo) and a list of questions with fixed answer options. It answers all the questions at once and tells you how sure it is about each option, as percentages. It never writes free text, so it can never make up an answer that isn't on your list.

Compatible Skills

These tools work well together with reflex for enhanced workflows:

  • openjev-sglang — semantic(0.27)+complementary+shared_fw(huggingface)+same_lang+similar_pop+shared_platform (67%)
  • pg-jev — semantic(0.24)+complementary+same_lang+similar_pop+shared_platform (58%)
  • mini-jev — semantic(0.44)+shared_fw(huggingface)+same_lang+similar_pop+shared_platform (58%)
  • jevmlx — semantic(0.20)+complementary+same_lang+similar_pop+shared_platform (57%)

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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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