Video summary

How to build Claude Skills Better than 99% of People

Main summary

Key takeaways

Technology

Overview

The video argues that agent skills (i.e., skill engineering) will be a major differentiator in 2026. Even as AI agents improve, they still need the right guardrails, context, and SOPs to perform real business work—especially for non-deterministic, judgment-based tasks where human-in-the-loop is often required.

The speaker positions skills as a middle layer between:

  • fully isolated prompts / custom GPTs, and
  • hardcoded automation platforms

…making automation more scalable and sharable across an organization.

Why “skills” matter (vs other approaches)

AI agents are improving rapidly, but they require:

  • Guardrails
  • Context
  • SOPs tailored to how a specific business works

Prior approaches have limitations:

  • Projects / custom GPTs
    • often isolated with context limits
    • don’t self-improve
    • require hopping across windows to operate consistently
  • Automation / deterministic workflows
    • excellent for fully deterministic, human-less processes
    • but most real work is not deterministic

Skills as the middle ground

The video frames skills as:

  • Process instructions for an agent (often with human-in-the-loop)
  • Capable of self-improvement
  • Usable by one general agent that can access thousands of skills
  • Created/updated by prompting
  • Shareable across a business, supporting consistent onboarding and operations

Prediction: AI work may shift toward agents using skills as a core “single interface for doing work”, and skills could become monetizable like a software layer.

What “skills” are (core concept)

  • Agent skills = “folders of instructions, scripts, and resources” that agents use to complete tasks more accurately and efficiently.
  • Core component:
    • skill.md = the process “SOP” (analogous to a system prompt, cloud project, or custom GPT, but organized as a reusable skill)

Additional components that expand capability

A skill can include guidance and resources for:

  • when/how to use knowledge files
  • when/how to use tools
  • when/how to spin up sub-agents
  • when/how to use code execution

Types of reference/context files used by skills

The video describes several categories of attachable skill context:

  1. Text files (common examples)

    • Example outputs
    • Style guides
    • ICP/background context
    • Brand voice/personality
    • Reusable strategy documents (e.g., newsletter/YouTube strategy)

  2. MCP instructions files

    • Explain how to use a specific tool efficiently within that skill
    • The speaker claims agents can generate these MCP docs themselves

  3. Assets / non-text files

    • Images, presentations, videos, even binaries
    • Used for demonstrating good output/layout examples

  4. Code scripts

    • Python/JS functions that can perform actions (e.g., API calls, executing functions)
    • Example mentioned: an “infographic scale” script performing an API call (referred to as “nano banana Google API call” per subtitle)

How the system scales to “thousands of skills”: Progressive disclosure

To avoid context overload, the speaker explains progressive disclosure:

  • The agent initially stores only skill metadata in memory:
    • skill name and description
  • After a skill is triggered, it loads:
    • skill.md into the context window
  • Only when needed, it loads:
    • reference files (text/assets/scripts)

Result: one agent can manage thousands of potential skills without dumping everything into context at once.

Skills vs plugins (and plugin implications)

The video clarifies a distinction:

  • Plugins are “packaged/bundled sets” that can include:
    • multiple skills
    • commands/workflow triggers to run sequences
    • specialized agent teams
    • preset connectors
    • versioning capabilities (updates propagate across accounts)

Benefits of plugins

  • More functionality than individual skills (like software/SaaS bundles)
  • Easier sharing/division by department (e.g., a sales plugin vs marketing plugin)
  • Expected marketplace growth + SaaS vendors launching plugins

Important note

Even without plugins, skills remain accessible.

Marketplaces and customization strategy

Early ecosystem elements include:

  • marketplaces of skills (example names mentioned in subtitles: “Skills MP Smithy” and others)
  • third-party provider skills (e.g., “Entropic” mentioned in subtitles—possibly a subtitle error likely referring to another provider such as Anthropic)

The video suggests businesses should customize generic skills by adding:

  • company brand tone
  • ICP and business context
  • individual employee style differences (e.g., salesperson voice/copywriting style)

Individuals with strong domain expertise could monetize by publishing skills for the public.

How to build skills (two creation methods)

The speaker outlines two main approaches:

  1. Manual once → save as skill

    • Do the task manually once with an AI agent, then ask it to save as a skill

  2. Prompt the agent to build directly

    • Example flow described:
      • download/import a third-party “meta ad scale”
      • combine it with the speaker’s knowledge sources
      • prompt Claude to generate a new ad-building skill

Best-practice “skill engineering” guidance (framework)

The approach is compared to software engineering, but adapted for agents.

Design considerations

  • UX-like decisions for where/how human-in-the-loop happens
  • context engineering (what context to include to improve outcomes)
  • feature design, edge cases, and process correctness

Key best practices

  • Treat skill building as iterative “art”
  • Skills are never finished—update them via prompts
  • Reuse reference documents (business description/ICP/voice, writing frameworks)
  • Prepare good output examples (claimed to have a major impact on performance)

The “framework” for the first build: what to specify

  1. Name of the skill and trigger condition

    • Example trigger: “when a user asks to generate an infographic”

  2. Goal/objective

    • short statement of what it should accomplish (quality + brand style)

  3. Connectors/APIs/MCPs/tools it can use

    • where relevant, how to interact with those systems

  4. Step-by-step process For each step:

    • when to use human-in-the-loop and what type (checkboxes/open field/single select)
    • what additional context/files the step needs
    • expected output for that step

  5. Rules

    • anticipate failure modes and explicitly constrain behavior
    • make knowledge files obligatory steps where needed (to prevent skipping)
    • encourage multiple variations/options during human-in-the-loop steps (e.g., “give me five ways…”)

  6. Progressive updates / self-learning

    • update rules when new “don’ts” or constraints are discovered
    • save user-approved outputs as “good examples” so the agent improves

Demo: “infographic generator” skill lifecycle (as described)

  • The speaker demonstrates a slash command trigger in a “code tab”.
  • The skill iterates over multiple improvements:
    • asks what content to convert into an infographic
    • later adds a question about platform (LinkedIn) to format correctly
    • uses QA boxes / structured input UI
    • suggests what to visualize (e.g., “anatomy of a skill”) and generates multiple visualization variations
  • Human approval:
    • a “keep all / finalize” action saves outputs as good examples, feeding improvement

Improvement tactics mentioned

  • If the agent doesn’t follow the process, update skill.md
  • If it needs extra info, add/update reference files
  • For strict constraints (e.g., “never use black background”), add/adjust rules or knowledge files
  • If it struggles with tools/MCPs:
    • guide manually once
    • then have it create/update an MCP reference doc

Sharing/deployment

  • export as a zip file for sharing and upload into another account’s capabilities
  • deploy via GitHub (a link is promised in the description)

Scaling organization-wide

  • bundle multiple skills into a plugin
  • potentially create a plugin marketplace across departments (with cloud/code + GitHub release steps mentioned)

Access/community offers (non-technical but tied to the ecosystem)

  • The speaker promotes an “AI accelerator/community” that:
    • lists downloadable skills/plugins
    • runs weekly workshops and business blueprints
  • Demo note: access to the “infographic scale” and other skills is offered via that accelerator/community.

Main speakers/sources

  • Main speaker: the video creator (narrator) discussing Claude, cloud code, cloud co-work, skills, and plugins (exact name not provided in subtitles).
  • Primary system/entity referenced: Claude (Anthropic) and associated platforms/tools like cloud co-work / cloud code, plus third-party skill providers mentioned in subtitles (e.g., “Entropic” appears likely to be a subtitle error).

Original video