jev-decision

Zero-dependency System 1 decision engine, calibrated guardrails, MCP server, and token optimization client for Jev (TypeSafe AI).

Based on the architecture by Diogo Almeida (@CompleteSkeptic) and validated against empirical benchmarks in arXiv:2609.29429.

• Zero External Dependencies: Pure standard library (urllib.request, json, math). Runs anywhere with zero pip bloat.
• Universal Multi-Harness Support:
  • Python: Drop-in client + guardrail helpers.
  • Model Context Protocol (MCP): Native stdio MCP server for Cursor, Claude Desktop, Antigravity, Windsurf, Cline.
  • TypeScript / Node.js: Zero-dependency TS package under ts/.
  • CLI: Fast terminal inspection command (jev guard, jev prune, jev verify).
• Disruptive Token & Latency Economics:
  • 70–300ms single forward-pass latency.
  • $0.042 per million input tokens, $0 output tokens.
  • Upstream context pruning saving 80%–92% of ongoing LLM prompt tokens.
• Calibrated Probabilities (RLCD):
  • Strict high-stakes threshold ($p \ge 0.95$ for destructive commands).
  • Balanced medium-stakes threshold ($p \ge 0.85$ for task verification).
  • Permissive low-stakes threshold ($p \ge 0.40$ for context pruning).
• Built-in Deterministic Offline Fallback: Operates 100% offline with zero network calls when no API key is provided.

pip install git+https://github.com/Coding-Dev-Tools/jev-decision.git

(Or clone locally and run pip install -e .)

cd ts && npm install && npm run build

Add to your claude_desktop_config.json, antigravity.json, or .cursor/mcp.json:

{
  "mcpServers": {
    "jev-decision": {
      "command": "jev-mcp",
      "env": {
        "TYPESAFE_API_KEY": "your-api-key"
      }
    }
  }
}

Exposes four high-speed tools to your agent:

1. jev_guard_command(command, cwd): Evaluates shell command safety in ~100ms.
2. jev_prune_output(raw_output, current_goal): Prunes verbose boilerplate from command outputs.
3. jev_verify_completion(goal, recent_actions, last_output): Verifies test proof before task completion.
4. jev_decide(state, questions): Arbitrary parallel evaluations.

# Evaluate bash command safety
jev guard "git status"
# [ALLOWED (Auto-Execute)] Category: read_only | Safety: 0.98

jev guard "rm -rf / --no-preserve-root"
# [BLOCKED (Requires Approval)] Category: destructive_or_leak | Safety: 0.01

# Token-prune large build or test logs
pytest | jev prune --goal "fix authentication bug" --stats

from jev_decision import JevClient, guard_bash_command, prune_tool_output, verify_turn_completion

client = JevClient()

# 1. Shell Safety Guard
safety = guard_bash_command("git status", cwd="/repo", client=client)
if safety["allow_auto"]:
    # Execute immediately without prompting user
    pass

# 2. Context Pruning
pruned, stats = prune_tool_output(huge_log, current_goal="fix auth endpoint", client=client)
print(f"Omitted {stats['saved_lines']} lines of boilerplate tokens!")

# 3. Task Completion Verification
check = verify_turn_completion(
    goal="Fix issue #42",
    recent_actions="edited file.py and ran pytest",
    last_output="100% green, 45 passed in 0.2s",
    client=client
)
if check["is_complete"]:
    # Safe to conclude session
    pass

To register the Jev skill with Claude Code or Antigravity:

# Claude Code:
npx skills add Coding-Dev-Tools/jev-decision

# Antigravity / Gemini:
cp SKILL.md ~/.gemini/antigravity/skills/jev-decision/SKILL.md

MIT © Coding-Dev-Tools