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grok-bot-jev

Bodila51/grok-bot-jev · Homepage

Cheap Jev decision layer for Grok Bot before expensive work.

Agent Decisions0.73Runner-up: Classification & RoutingFunnel
Automated README summary

Automatically summarized from the README on 20 Sept 2026; not a run by jevbooks. Run instructions and author-reported outcomes still need verification.

What it is

A small open-source Python reference implementation that connects TypeSafe Jev to Grok Bot as a cheap decision layer, classifying requests before expensive research, browser, retry, or subagent work. For developers integrating Jev with Grok Bot.

How it uses Jev

Jev classifies the request state before expensive work. The router calls the TypeSafe SDK's system_one and returns actions like reuse_cache, stop_retry, run_deterministic, chat_only, research_capped, allow_subagent, or ask_human. In shadow mode decisions are logged and advisory; in active mode the Grok Bot skill honors route.action.

Primitives:choice

Technique worth stealing

Use a cheap classification call to gate expensive tool work, with shadow mode for measurement before active enforcement.

Try it

git clone the repo, install requirements, set TYPESAFE_API_KEY, then run scripts/dry_run.py.

View on GitHub

Start using Jev: API key and your first request →

Model judgments from README excerpts

Project signals use repository metadata, an existing summary and the first 700 README characters, not the full project. “Not found” means not detected in that material; it does not establish absence. These are the original judgments with recorded models and dates, not source audits or run results.

Models & evaluation dates

Inclusion gate
jev-1.13.0
Project signals
jev-1.13.0
Category
jev-1.13.0
Pattern tags
jev-1.13.0

Evidence

These signals use repository metadata, an existing summary and at most 700 cleaned README characters. ≥ 0.60 means reported in that excerpt; ≤ 0.40 means not found there. The middle band is inconclusive. None is a source-code audit or a test result. The runnable-tool question excludes research experiments; the recommendation question asks for a learning shortlist, not a quality verdict.

  • Jev is core to the projectFound in material0.84
  • Shows a System One patternFound in material0.64
  • Describes handling uncertaintynot found in the excerpt0.10
  • Reports measured numbersnot found in the excerpt0.06
  • Is a runnable toolFound in material0.87
  • Top-10 learning shortlist signalFound in material0.61
  • Is a replica of the modelnot found in the excerpt0.03
  • Solves a common problem (0–2)score on a 0–2 scale1.06 / 2
  • About JevFound in material0.98
Signals
Jev is core to the project0.84Shows a System One pattern0.64Describes handling uncertainty0.10Reports measured numbers0.06Is a runnable tool0.87Top-10 learning shortlist signal0.61Is a replica of the model0.0300.51Solves a common problem (0–2)1.06 / 2012

no-judgment band (0.40–0.60)

Design patterns
Funnel0.90Viewfinder0.52Check-up0.41Ghostwriter0.3400.51

no-judgment band (0.40–0.60)

The Jev model and date for each judgment stage are listed above. Card written by DeepSeek V4.1 Flash from the README on 20 Sept 2026.