Agent skill

find-skills

Helps users discover agent skills from the open ecosystem. Searches skills.sh and presents options for installation via the built-in skill_manager tool.

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Install this agent skill to your Project

npx add-skill https://github.com/EvoScientist/EvoScientist/tree/main/EvoScientist/skills/find-skills

SKILL.md

Find Skills

This skill helps you discover skills from the open agent skills ecosystem.

When to Use This Skill

Use this skill when the user:

  • Asks "how do I do X" where X might be a common task with an existing skill
  • Says "find a skill for X" or "is there a skill for X"
  • Wants to search for tools, templates, or workflows
  • Expresses interest in extending agent capabilities
  • Mentions they wish they had help with a specific domain (design, testing, deployment, etc.)

Step 1: Search for Skills

Use npx -y skills find with a relevant keyword to search the ecosystem:

bash
npx -y skills find [query]

Examples:

  • User asks "help me with React performance" → npx -y skills find react performance
  • User asks "is there a skill for PR reviews?" → npx -y skills find pr review
  • User asks "I need to create a changelog" → npx -y skills find changelog

The search results will show installable skills like:

vercel-labs/agent-skills@vercel-react-best-practices
└ https://skills.sh/vercel-labs/agent-skills/vercel-react-best-practices

Browse all available skills at: https://skills.sh/

Step 2: Present Options

When you find relevant skills, present them to the user with:

  1. The skill name and what it does
  2. A link to learn more on skills.sh

Ask the user which skill(s) they want to install.

Step 3: Install

Use the built-in skill_manager tool to install:

skill_manager(action="install", source="owner/repo@skill-name")

Common Skill Categories

Category Example Queries
Web Development react, nextjs, typescript, css, tailwind
Testing testing, jest, playwright, e2e
DevOps deploy, docker, kubernetes, ci-cd
Documentation docs, readme, changelog, api-docs
Code Quality review, lint, refactor, best-practices
Design ui, ux, design-system, accessibility
Productivity workflow, automation, git

When No Skills Are Found

If no relevant skills exist:

  1. Acknowledge that no existing skill was found
  2. Offer to help with the task directly using your general capabilities
  3. Mention the user could create their own skill with npx -y skills init

Expand your agent's capabilities with these related and highly-rated skills.

EvoScientist/EvoScientist

skill-creator

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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paper-writing

Guides writing academic papers section by section using an 11-step workflow with LaTeX templates and counterintuitive writing tactics. Covers Abstract, Introduction, Method, Experiments, Related Work, Conclusion, and Supplementary. Use when: user asks to write or draft a paper section, needs LaTeX templates, wants to improve academic writing quality, optimize novelty framing, or mentions 'write introduction', 'draft method', 'paper writing'. Do NOT use for pre-submission review (use paper-review), experiment execution (use experiment-pipeline), or paper planning/story design (use paper-planning).

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evo-memory

Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after idea-tournament), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing idea tournaments or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use idea-tournament).

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paper-navigator

End-to-end academic paper workflow: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), read with structured analysis (3-level strategy), and organize into literature maps or reports. Use when: finding papers, reading a paper, related work, literature survey, citation analysis, research trends, SOTA results, datasets, or literature reports. Do NOT use for writing a literature review section (use paper-writing), comparing research ideas (use idea-tournament), or planning paper structure (use paper-planning).

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EvoScientist/EvoSkills

paper-review

Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: 5-aspect checklist (contribution sufficiency, writing clarity, results quality, testing completeness, method design), counterintuitive protocol (reject-first simulation, delete unsupported claims, score trust, promote limitations, attack novelty), reverse-outlining, and figure/table quality checks. Use when: user wants to self-review or self-check their own paper draft before submission, stress-test their claims, prepare for reviewer criticism, or mentions 'self-review', 'check my draft', 'is my paper ready'. Do NOT use for writing a peer review of someone else's paper, and do NOT use after receiving actual reviews (use paper-rebuttal instead).

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experiment-craft

Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.

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