muse-skills

An agent that asks for a second opinion before it acts. This repo wires
TypeSafe Jev into an agent's decision loop through a shadow-mode router,
and ships the 23-skill library the router learns to choose between.

The core loop is small: before an expensive or irreversible step, the
agent asks Jev a typed question. Choice, Score, or Boolean, always with
probabilities. The answer is logged. In shadow mode the action proceeds
anyway; in active mode the verdict is enforced. You calibrate the
thresholds from your own logs, not from documentation.

60 seconds

One command. It clones the repo, installs the Jev decision layer and all
23 skills into ~/workspace/skills/, and leaves the demo and docs in
~/workspace/muse-skills/:

curl -fsSL https://raw.githubusercontent.com/theglitcharchitect/muse-skills/main/install.sh | bash
cd ~/workspace/muse-skills && python3 demo/run.py

No key, no network. The demo runs against a deterministic mock gateway and
walks through four real decisions using the actual gate scripts. To go
live, set VERCEL_AI_GATEWAY_KEY and run it again; the mock steps aside.
Full setup in docs/setup.md. Manual install: git clone
this repo and copy skills/* into your skills directory.

What the demo shows

ACT 1  Boolean: the safety gate on a risky command
  tool:    shell_exec
  args:    rm -rf ~/workspace/drafts
  risk score         2 / 2 (confidence 0.92)
  safe to run        p = 0.03
  Jev verdict        BLOCK

ACT 2  Choice: two plans, one decision
  Jev choice         PLAN B
  probabilities      plan_b 0.78, plan_a 0.22

ACT 3  Score: a draft against an evidence rubric
  evidence score     1 / 3 (confidence 0.68)
  weak rungs         1
  Jev verdict        REVISE

ACT 4  Router: shadow mode gates an expensive browser run
  Jev choice         escalate
  probabilities      escalate 0.81, proceed 0.12, skip 0.07
  Jev decision       ESCALATE
  Shadow mode: the run proceeds, the decision is logged.

Four typed evaluations, three real gate scripts, one log per decision.

How it fits togeth