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.
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:
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:
- The skill name and what it does
- 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:
- Acknowledge that no existing skill was found
- Offer to help with the task directly using your general capabilities
- Mention the user could create their own skill with
npx -y skills init
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
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.
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).
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).
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).
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).
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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