Master AI Agent Skills: Find, Build, Design, and Debug with Vercel & Anthropic

Vercel find‑skills CLI

Vercel provides find‑skills, a command‑line manager for AI Agent Skills. It enables searching, installing, checking for updates, and bulk updating of Skills. npx skills find <keyword> – interactive search by keyword. npx skills add <package> – install a Skill. npx skills check – list available updates for installed Skills. npx skills update – update all installed Skills.

An online catalog is available at skills.sh for browsing the full Skill ecosystem.

https://github.com/vercel-labs/skills/blob/main/skills/find-skills/SKILL.md

Anthropic skill‑creator

When existing Skills do not satisfy a custom requirement, Anthropic’s skill‑creator defines a standard workflow for building “meta‑Skills” that extend Claude.

Standard directory layout

(skill-name)/
├── SKILL.md          # YAML metadata + Markdown commands
├── scripts/          # optional executables (Python, Bash, …)
├── references/       # optional documentation
└── assets/           # optional static assets

SKILL.md

must contain YAML metadata (e.g., name, description) and a Markdown body with the executable commands (max ≈ 5000 words).

Design principles

Progressive loading : load metadata → SKILL.md → referenced resources to stay within the model’s context window.

Freedom matching : tighter instructions for precise tasks, looser for flexible tasks.

Minimize redundancy : exclude non‑essential files; the packager automatically strips them.

Verifiability : built‑in validation of metadata completeness and script executability.

Helper scripts

init_skill.py

– scaffolds a new Skill directory with the standard template. package_skill.py – validates the structure and bundles the Skill into a distributable .skill file.

https://github.com/anthropics/skills/blob/main/skills/skill-creator/SKILL.md

Brainstorming Skill

The Brainstorming Skill enforces a mandatory design phase before any implementation, reducing rework.

Four‑step design workflow

Context clarification – review project files, documentation, and recent commits to establish the current baseline.

Requirement refinement – ask precise questions to define goals, constraints, and success criteria.

Alternative comparison – generate 2‑3 design proposals, list pros/cons, and recommend a preferred option.

Modular approval – confirm each module, then store the final design in docs/plans/.

https://github.com/obra/superpowers/blob/main/skills/brainstorming/SKILL.md

Systematic Debugging Skill

This Skill codifies a disciplined debugging methodology for AI Agents: do not attempt a fix until the root cause is identified.

Four‑stage debugging process

Root‑cause investigation – reproduce the error, examine recent changes, and add diagnostic logs at component boundaries.

Pattern analysis – compare the failing code with a known‑good version, map dependencies, and avoid premature assumptions.

Hypothesis testing – change a single variable per test; if the hypothesis fails, return to investigation.

Fix implementation – write a failing test first, apply the fix, verify the test passes, and abort after three consecutive failures to reassess architecture.

https://github.com/obra/superpowers/blob/main/skills/systematic-debugging/SKILL.md

Combining the Skills in a Project Workflow

The four Skills form a closed loop: find → develop → design → debug .

Daily use : run find‑skills to locate existing Skills and install them as needed.

Custom requirement : if no suitable Skill exists, use find‑skills → skill‑creator → Brainstorming to design and build a new Skill.

Full project lifecycle : start with Brainstorming to produce a design, execute the implementation, and apply Systematic Debugging for any issues.

Final takeaway

AI Agents are moving toward a modular architecture composed of many atomic Skills. Mastering the four Skills above enables rapid capability expansion, reproducible engineering processes, and scalable AI Agent development in the 2026 “scale‑up” era.