Evo Search

Evo-Search

Runs a genetic-algorithm-style loop over candidate responses: generate a diverse initial population, score each against a rubric, then repeatedly select, crossover, and mutate to improve quality across generations. Output the top 3 final solutions.

---

Step 0: Problem Intake & Rubric

Identify problem type, constraints, audience, and scope. If ambiguous, ask one question.

Rubric Construction

Detect which mode applies:

| Mode | Trigger | Action | |------|---------|--------| | A: Auto | User gave only the problem | Generate 4–6 domain-appropriate criteria | | B: Guided | User hinted at priorities | Generate rubric, weight toward stated priorities | | C: Manual | User gave explicit criteria | Convert each into a scored rubric entry with anchors |

In all modes: augment vague criteria into scorable definitions, and always add: > Overall Fitness (30%): "Would a knowledgeable expert prefer this over a competent > but unremarkable response?" Scored holistically. Prevents narrow-criteria gaming.

Present the rubric to the user and wait for confirmation before proceeding.

Rubric format:

| Criterion      | Description              | Weight | Max |
|----------------|--------------------------|--------|-----|
| [Name]         | [Definition + anchors]   | X%     | 10  |
| Overall Fitness| Expert holistic score    | 30%    | 10  |
Weighted Total = Σ(score × weight)  [max = 10.00]

---

Step 1: Initial Population

Generate 6 candidates (default) using these diversity frames, one per candidate:

| # | Frame | |---|-------| | 1 | Conventional / mainstream | | 2 | Contrarian / challenges assumptions | | 3 | First-principles / bottom-up | | 4 | Analogy-led / draws from another domain | | 5 | Risk-focused / emphasizes what could go wrong | | 6 | Synthesis / combines multiple angles |

Generate all candidates before scoring any. Then score each and display:

GENERATION 0
| # | Frame        | Fitness | Strength       | Weakness      |
|---|--------------|---------|----------------|---------------|
| 1 | Conventional | X.X     | [one phrase]   | [one phrase]  |

---

Step 2: Evolution Loop

Default: 5 iterations. Repeat until stopping criteria are met.

2.1 Selection

Tournament selection: sample 2 candidates, keep the higher scorer. Repeat to get 4 parents. Elitism: carry the top 2 candidates forward untouched every generation.

2.2 Crossover

Produce 4 offspring (pair parents: 1+2, 3+4, 1+3, 2+4). For each pair, synthesize a child that inherits the key strength of each parent without their primary weaknesses. Rotate crossover strategies across iterations (trait synthesis → section splice → schema inheritance).

2.3 Mutation

Apply mutation to each offspring with probability 0.2 (~1 of 4 offspring per generation).

| Mode | When to use | What to do | |------|-------------|------------| | Exploitative (~60%) | Offspring has a diagnosable weak criterion | Target and fix that specific criterion | | Exploratory (~40%) | No clear weakness, or population is converging | Random perturbation: swap rhetorical stance, domain lens, level of abstraction, or target audience |

Multi-feature mutation: ~1 in 3 mutation events, mutate two features simultaneously. Use when the population has converged for 2+ generations, two criteria score equally low, or an exploratory mutation is being applied.

2.4 Evaluate & Merge

Score all offspring. Merge with current population. Keep top 6 by fitness score. Elites from 2.1 are guaranteed to survive.

2.5 Convergence Check

If top 3 candidates are within 0.4 fitness of each other AND content is substantially similar, inject 1–2 fresh randomly-framed candidates ("immigrants") before merging.

Display per-generation summary:

GENERATION [N]
| # | Origin        | Fitness | Δ   | Key Change         |
|---|---------------|---------|-----|--------------------|
| 1 | Elite         | X.X     | -   | -                  |
| 2 | Crossover A+B | X.X     | +Y  | [what changed]     |
Best: Candidate [N] (X.X)

---

Stopping Criteria

Stop early if any condition is met:

• Max iterations reached (default: 5)
• Best fitness ≥ 9.0
• Best fitness improved less than 0.2 over 2 consecutive generations

Note the reason when stopping early.

---

Step 3: Output

3.1 Contrast Table (show before full answers)

TOP 3 SOLUTIONS
| Rank | Score | Defining Strength    | Best Used When              |
|------|-------|----------------------|-----------------------------|
| #1   | X.X   | [one phrase]         | [context where #1 wins]     |
| #2   | X.X   | [one phrase]         | [context where #2 wins]     |
| #3   | X.X   | [one phrase]         | [context where #3 wins]     |

3.2 Full Solutions

Output the top top_k solutions in full (default: 3), labeled with rank and score.

3.3 Evolution Trace

EVOLUTION SUMMARY
| Generation | Best Fitness | Key Improvement     |
|------------|--------------|---------------------|
| 0 (init)   | X.X          | [note]              |
| ...        | ...          | ...                 |
Final: X.X / 10.0

3.4 Rubric Breakdown

Show per-criterion scores for the #1 solution only.

---

Parameters

| Parameter | Default | Range | Effect | |----------------|--------------|------------------------------------|-------------------------------------| | population | 6 | 4–10 | Candidates per generation | | iterations | 5 | 1–10 | Evolution cycles | | mutationrate | 0.2 | 0–1 | Fraction of offspring mutated | | mutationmode | mixed | exploitative / exploratory / mixed | Mutation strategy bias | | multimutation | occasional | never / occasional / frequent | How often 2 features mutate at once | | elites | 2 | 1–3 | Candidates preserved each generation| | topk | 3 | 1–5 | Final solutions shown in full | | rubricmode | auto | auto / guided / manual | How the rubric is built | | showtrace | true | true / false | Show evolution summary |

Users set parameters in plain language. Examples:

• "Quick run, 3 iterations" → iterations=3
• "Large population, thorough search" → population=8–10
• "Be more exploratory" → mutation_mode=exploratory
• "Just show me the winner" → top_k=1

---

Scoring Guidance

Score honestly. A mediocre-but-coherent answer scores 5-6. Inflated scores destroy the selection signal and the algorithm stops working. The crossover step is where the most value is created. Synthesize genuine strengths; don't concatenate text.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: smkalami
• Source: smkalami/skills
• License: MIT

Install and usage instructions live in the source repository linked above.