plan-efe

name	plan-efe
description	Given a goal and a set of candidate next actions, rank them by Expected Free Energy (pragmatic + epistemic value) using past similar episodes, and pick the winner. Delegates scoring to the policy-selector subagent.

Upholds Invariant 3 of predictive-mind: pragmatic and epistemic value are scored separately. Use this skill when multiple candidate actions are on the table and the "first plausible one" is not obviously the best.

Typical trigger

• You have a task card but have not committed to a next action yet.
• The dreamer returned multiple candidates with comparable confidence.
• A PostToolUse hook reported surprise, and you need to choose between /probe, /reflect, or a new action.

Procedure

1. Enumerate candidates (2-5 is the sweet spot). Include, at minimum:
   • The action you are currently tempted to take.
   • One probe (a /probe call that would disambiguate an open hypothesis).
   • One "ask the user" option if anything material is unclear.
2. Delegate to the policy-selector subagent with:
   • The task card (goal + acceptance criteria + open hypotheses).
   • The task_id (so the subagent can call retrieve_beliefs).
   • The candidate list.
3. Report back the full ranked JSON output and the pick. Do NOT rewrite or summarize the scores — the raw scores are the accountability trail.
4. If the pick is ask-user, stop and ask. If the pick is probe-first, invoke /probe. Otherwise proceed with /predict then the chosen action.

What separates this from "just think about it"

• Similar-episode grounding: the policy-selector queries recall_similar_episodes for each candidate, so prior surprises actually discount similar-looking plans.
• Epistemic budget is explicit: a high-information-gain probe can legitimately beat a low-confidence direct attempt, even though the probe does not advance the goal.
• Scores are persisted: the ranked output becomes part of the session record for later review.

Arguments

$ARGUMENTS — the current goal (free text). The skill will ask you for the candidate list if you did not include it.

Output shape (from the policy-selector)

ranked: [ { action, pragmatic, epistemic, total, recommendation, ... } ]
pick:   { action, rationale }
notes_on_calibration: <honest caveats>

Use the pick's recommendation field to decide the next skill:

• execute → /predict then run the action
• probe-first → /probe
• dream-first → invoke the dreamer on the picked action before acting
• skip → drop this candidate, re-plan