Agent Skills by Mostapha Kalami Heris

A collection of agent skills that give AI agents structured, reusable workflows for tackling complex problems. These skills go beyond simple prompting to deliver consistently higher-quality results.

More skills will be added over time.

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Table of Contents

• Agent Skills by Mostapha Kalami Heris
  • Table of Contents
  • Skills
    • evo-search
    • tournament-search
    • prompt-decorators
  • Installation
    • CLI Tools (Claude Code, Gemini CLI, VS Code, etc.)
    • Online AI Assistant Platforms (ChatGPT, Claude, Gemini, etc.)
  • How to Cite
  • License

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Skills

Note on token usage: Skills like evo-search and tournament-search generate and evaluate multiple candidate responses across several rounds. Certain decorators in prompt-decorators also add extra reasoning or refinement steps. This increases token consumption significantly compared to a single plain prompt. The benefit is higher response quality, but it is not free. If you are on a metered subscription, running these skills can cost noticeably more. Consider using lighter configurations (for example, fewer iterations, a smaller population, or simpler decorators) when your token budget is limited.

evo-search

Solves complex, open-ended problems using a genetic-algorithm-style search loop.

It generates a diverse initial population of candidate answers, scores each one against a weighted rubric, and then repeatedly applies selection, crossover, and mutation to produce stronger solutions. The final output includes the top 3 solutions with scores, a contrast table, and an evolution trace.

When this skill is triggered:

• The user asks for an "evolutionary search", "genetic algorithm", "evolve an answer", or "breed solutions"
• The user says "give me multiple approaches and refine the best one"
• The problem is complex or open-ended with no single correct answer, and the user wants a deeply explored result

Key parameters (set in plain language):

Parameter	Default	Effect
population	6	Candidates per generation
iterations	5	Evolution cycles
mutation_rate	0.2	Fraction of offspring mutated
top_k	3	Final solutions shown in full

Install:

• CLI tools: run in your terminal:

  npx skills@latest add smkalami/skills/evo-search
  

• AI assistant platforms: download and upload evo-search.skill

Example usage:

Use evo-search to invent a new programming paradigm that blends constraint propagation
with biological growth patterns. Something that does not exist yet but could realistically
be implemented. Define its core model, syntax philosophy, and a killer use case.

Evolve an answer: design a radically new way for human teams to make high-stakes decisions
under uncertainty, combining ideas from mechanism design, cognitive science, and swarm
intelligence, without relying on voting, consensus, or hierarchy.

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tournament-search

Solves complex, open-ended problems using a knockout bracket tournament.