dream

name	dream
description	Dream about a skill to find edge cases and improvements. Use when the user says 'dream about [skill]' or 'improve [skill] through dreaming'. Generates surreal scenarios and tests patches via eval loop.
context	fork
hooks	{"Stop":[{"matcher":"","hooks":"[Truncated]"}]}
allowed-tools	["Read","Write","Edit","Bash","Agent","Grep","Glob"]

Dream — Stage REM

The creative stage + the eval lab. Takes verified material from N3, generates surreal edge-case scenarios, then — critically — tests patches before proposing them, following the autoresearch pattern:

mutate → apply to copy → test via subagent → measure → keep/discard

Most dreams produce nothing. Patches that survive eval have evidence.

Modes

Pipeline mode (from orchestrator): Receives structural audit + fragments. Standalone mode (user invokes directly): Performs compressed N1+N2 internally.

Inputs (Pipeline)

structural_audit (from N3), surviving_fragments (from N2), target_skill (path), intensity (scales with cycle), cycle_number.

Procedure

1. Determine Dream Count

Cycle	Dreams	Notes
1	2-3	Early cycles are N3-heavy
2	4-5	Balanced
3	6-8	REM lengthens
4+	8-12	Extended REM

2. Generate Dream Scenarios

For each dream, select 1-3 fragments and apply 2-3 mutation strategies:

Strategy	Best For	Description
Scale warp	EDGE	Push quantities to extremes
Type swap	GAP	Change expected I/O type
Context shift	ADJ	Move skill to alien domain
Constraint inversion	Assumption failures	Flip a core assumption
Chimera	Cross-cycle	Combine 2+ unrelated fragments
Temporal warp	FRIC	Add time pressure
Corruption	EDGE	Malformed or partial input
Meta-recursion	Verified gaps	Skill on its own output
User chaos	FRIC	Ambiguous contradictory requests
Platform edge	Dependencies	Test environment limits

N3's analysis guides strategy selection:

• Verified gaps → Type swap, Context shift
• Assumption failures → Constraint inversion
• Friction → User chaos, Temporal warp
• Pruning candidates → Meta-recursion

Each scenario is a plausible (if weird) user message.

3. Mental Simulation (Quick Pass)

Trace how the skill would handle each scenario. Classify:

• ✅ Handled: Skill covers this → score 0, skip eval
• ⚠️ Degraded: Output with issues → score 1, candidate for eval
• ❌ Failed: No guidance → score 2+, run eval
• 💡 Surprising: Unexpected behavior → score 2+, run eval

Score 0-1 dreams are logged but don't enter the eval loop. This keeps eval costs focused on promising candidates.

4. The Eval Loop (Score 2+ Only)

This is the autoresearch-inspired core. For each promising dream:

4a. Draft the Patch

Write a specific, surgical patch (1-3 lines) that would make the skill handle this scenario. The patch must be a concrete diff — exact text to add/change/remove.

4b. Apply to a Copy

cp /path/to/SKILL.md /tmp/skill-patched.md

Apply the patch to the copy. The original is never touched.

4c. Test via Subagent

Spawn a subagent (using the Agent tool) with this prompt:

You are testing a skill. Here is the skill:
[contents of /tmp/skill-patched.md]

A user sends you this message:
[the dream scenario]

Follow the skill's instructions to handle this request.
Report whether you were able to handle it successfully,
what issues you encountered, and rate your confidence
in the output quality from 0-10.

Also spawn a subagent with the ORIGINAL skill and the same scenario. This gives us a before/after comparison.

4d. Measure

Compare the two subagent results:

Metric	How	Weight
Handled?	Did the patched version handle the scenario?	0.4
Quality delta	Confidence score difference (patched - original)	0.3
No regression	Does patched version still handle a normal scenario?	0.3

The regression check is critical. Run the patched skill against one normal, common-case scenario to verify the patch doesn't degrade typical usage.

4e. Keep or Discard

eval_score = handled * 0.4 + quality_delta * 0.3 + no_regression * 0.3

• eval_score > 0.5: KEEP — Patch has evidence of improvement. Promote to proposed patch.
• eval_score <= 0.5: DISCARD — Patch didn't measurably help. Log the attempt but don't propose.
• Regression detected: DISCARD immediately regardless of score.

5. Write Dream Journal

Create at [skill-name]-dreams/[timestamp].md (sibling to skill dir).

# Dream Journal: [Skill Name]
**Date**: [timestamp]
**Mode**: pipeline | standalone
**Cycle**: [N]
**Dreams**: [count] generated, [count] eval'd, [count] passed

## Summary
[1-2 sentences. Honest about null results.]

## Dreams

### Dream 1: [Evocative title]
**Scenario**: [The micro-prompt]
**Mutations**: [strategies used]
**Outcome**: [✅/⚠️/❌/💡]
**Score**: [0-3]
**Analysis**: [1-3 sentences]
**Eval**: [skipped | tested, eval_score: 0.XX, KEEP/DISCARD]

## Proposed Patches (Evidence-Backed)

### Patch 1: [Title]
**From dream**: [#]
**Eval score**: [0.XX]
**Original skill confidence**: [X/10]
**Patched skill confidence**: [Y/10]
**Regression check**: passed
**Diff**:
~~~
[exact patch]
~~~

## Discarded Patches (Tested, Failed)
[Patches that entered eval but didn't pass. Brief note on why.]

## Residual Thoughts
[Cross-session patterns. Themes that keep recurring.]

6. Append to results.tsv

For each dream:

timestamp	skill	dream_title	mutations	outcome	score	eval_attempted	eval_score	eval_result	notes

7. Report to Orchestrator

Return:

• Dreams generated, eval'd, passed
• Score distribution
• Proposed patches (with evidence)
• Updated insight_rate

---

Standalone Mode

When invoked directly (user says "dream about [skill]"):

1. Read target SKILL.md
2. Search past conversations for relevant fragments (compressed N1)
3. Quick classify (compressed N2)
4. Skip N3 structural audit — use heuristic assessment
5. Run dreams + eval loop as normal
6. Write journal

Standalone mode has lower signal quality (no N3 verification) so the eval loop is more important — it catches the bad patches that N3 would have filtered.

---

Patch Quality Rules

• 1-3 lines. If it's a paragraph, it's a feature request, not a patch.
• Must be a diff. Exact text, not vague suggestions.
• Must pass eval. No untested patches in the proposed section.
• Must not regress. Common-case regression = immediate discard.
• Never auto-apply. Present to user with evidence. Always.

---

Behavioral Rules

• Be creative. Weirdness is a feature in scenario generation.
• Be rigorous. Evidence is required for patches. No vibes.
• Don't force insights. Null sessions are successful sessions.
• Trust the eval. If a patch you felt good about fails eval, discard it. Your intuition is not evidence.
• Title dreams evocatively. "The Tower of Babel", "The Ouroboros", "The Infinite Spreadsheet." Titles make journals scannable.