Simmer

You wrote a prompt. It works. But is it good? Simmer runs your artifact through multiple rounds of criteria-driven refinement — each round, a panel of judges reads your code, understands the problem, and proposes specific improvements.

Read the story behind Simmer →

Iterative artifact refinement — take any artifact or workspace and hone it over multiple rounds using criteria-driven feedback.

Installation

/plugin marketplace add 2389-research/claude-plugins
/plugin install simmer@2389-research

What This Plugin Provides

One skill (simmer) with four subskills that run the refinement loop:

• Setup — identify the artifact (file or workspace), elicit 2-3 quality criteria, determine evaluation method
• Generator — produce an improved version based on the judge's ASI (Actionable Side Information — the single highest-leverage fix)
• Judge — score the candidate 1-10 per criterion, produce the ASI
• Reflect — record the trajectory, track the best candidate across iterations

Trigger Phrases

Say any of these to start a simmer loop:

"simmer this", "refine this", "hone this", "iterate on this", "make this better", "improve this over a few rounds", "polish this", "tighten this up", "optimize this pipeline", "find the best model for this task", "tune this configuration"

Any request to iteratively improve an artifact triggers simmer.

When to Use Simmer vs. Cookoff vs. Omakase-Off

Situation	Use	Why
You have an artifact and want it better	simmer	Iterative refinement against criteria
You want parallel code implementations competing	cookoff	Parallel generation + fixed criteria + pick winner
You don't know what you want yet	omakase-off	Parallel designs + react + discover preference

Rule of thumb: Simmer = serial iteration. Cookoff/omakase-off = parallel exploration.

Quick Example: Single File

User: "Simmer this" [pastes a pitch email]

Claude: I'm using the simmer skill to set up iterative refinement.

What does "better" mean for this? I'd suggest:
1. Value prop clarity
2. Tone match
3. Call to action strength

User: "Those work"

[Iteration 0: Judge scores seed — 4.0/10]
[Iteration 1: Generator fixes value prop → 5.3/10]
[Iteration 2: Generator fixes CTA → 6.3/10]
[Iteration 3: Generator fixes tone → 7.3/10]

Trajectory:
| Iter | Value Prop | Tone | CTA | Composite | Key Change |
|------|-----------|------|-----|-----------|------------|
| 0    | 4         | 5    | 3   | 4.0       | seed       |
| 1    | 7         | 5    | 4   | 5.3       | specific problem statement |
| 2    | 7         | 6    | 6   | 6.3       | low-friction CTA |
| 3    | 7         | 7    | 8   | 7.3       | peer-sharing tone |

Best candidate: iteration 3 (7.3/10)
3 iterations complete. Run 3 more?

Quick Example: Workspace with Evaluator

User: "Simmer this pipeline — find the best setup for this task"

Claude inspects the workspace, finds evaluate.sh and config.json,
and proposes criteria + evaluation method:

  Evaluator: ./evaluate.sh
  Criteria: accuracy, cost efficiency, latency
  Search space: models, prompt text, pipeline topology

User: "Looks good, coverage is the priority. 5 iterations."