Beyond Deadwoodworks: How Claude Code Skills Super-charge MCP Servers and Everyday Workflows

You've probably heard about AI assistants that can "use tools." Maybe you've even set up an MCP server or two. But here's the question nobody's asking: why do they work perfectly one day and completely miss the mark the next?

I'll tell you why: you're giving Claude access to tools, but you're not giving it the playbook.

That's what skills do. They turn "Claude can technically do this if I phrase it right" into "Claude does this reliably, every single time, without me babysitting it."

This isn't just for developers—though if you are one, you'll love this. This is for anyone who's tired of re-explaining the same workflow, watching AI miss obvious steps, or wondering why yesterday's perfect prompt doesn't work today.

In this article, you'll learn what skills actually are, see real-world examples across different teams, understand the difference between skills and MCP servers (they're not the same thing), and build something you can use today. Screenshots and working code ahead—grab a coffee. ☕️

Why Skills Matter (And Why MCP Servers Alone Aren't Enough)

Here's the thing everyone gets wrong at first: they think MCP servers are the solution. You set them up, Claude can access your tools, problem solved.

Except it's not solved. You still have to explain what you want every single time. The phrasing matters. The order matters. Miss a step? Claude misses it too.

Without skills, you're a manager who has to micromanage every task.

With skills, you write the instructions once, and Claude follows them forever.

What skills actually give you:

Predictable, consistent behaviour → No more "why did it skip that step today?" moments

Reusable logic that scales → Build once, use everywhere

Safety and control → You define the guardrails, not hope for the best

Evolve without rewiring → Update the skill, not your entire stack

💡 The shift: From "Does Claude understand what I need?" to "Claude already knows what to do."

MCP Server vs Skill—What's Actually Happening Here

Alright, let's clear up the confusion. A lot of people think MCP servers and skills are the same thing. They're not. Here's the difference, and why it matters:

Component	What it does	When you need it
MCP server	Gives Claude access to your files, APIs, databases, external systems	When Claude needs to reach outside itself to get data or take actions
Skill	Defines how Claude uses those tools—what order, what validation, what to do when things go wrong	When you want Claude to follow a specific workflow reliably

Here's the mental model:

MCP server = "Claude, here are the keys to the filing cabinet."

Skill = "Claude, here's exactly how to organise what's inside."

The MCP Server provides access to tools, while the Skill orchestrates how those tools are used to deliver consistent, predictable results.

You need both. MCP gives the access. Skills give the logic.

What happens if you only have MCP?

Claude can technically access everything, but you're counting on it to figure out the right sequence every time. Sometimes it nails it. Sometimes it doesn't. You're rolling the dice.

What happens if you only have a skill?

Nothing. The skill is calling tools that don't exist. It's like writing a perfect recipe but having no ingredients.

What happens when you have both?

Magic. Claude knows what it can access and exactly how to use it. Every. Single. Time.

⚠️ Common mistake: People build an MCP server, see that Claude can use the tools, and think they're done. Then they wonder why results are inconsistent. The MCP server isn't the finish line—it's the starting line.

How Real Teams Are Actually Using This

Let's get out of theory and into what this looks like when people use it every day.

📸 Content & Marketing Teams

The workflow: Upload images, videos, docs → Skill auto-tags everything → Files are organised and searchable

What changed: No one's manually tagging 47 product photos anymore.

---

🛠️ Product & Engineering

The workflow: New tickets → Skill triages, generates test stubs, flags violations → Team focuses on real work

What changed: The skill is the teammate who never forgets.

---

💬 Customer Support & Success

The workflow: User report → Skill classifies, pulls context, drafts response or escalates

What changed: Support teams stop reacting and start operating.

---

⚙️ Internal Ops & Automation

The workflow: Audit logs, error reports, health checks → Skill handles routine maintenance

What changed: Your team focuses on actual problems, not routine checks.

---

What's the common thread?

You're not asking Claude to help you. You're giving Claude responsibility to do it without you.

That shift—from assistant to operator—is where the real value lives.

Let's Build Something: Auto-Tagging Files in Under 20 Minutes

Enough theory. Let's build a skill you can actually use.

This one's simple but meaningful: you point Claude at a folder, it reads your files, analyses what they are, and tags them automatically. Useful for any team. Any workflow.

By the end, you'll have something working that you can adapt to your own needs.

Example file upload interface showing various document types ready for automatic tagging and categorisation.

Step 1—Understand What Tools You Just Unlocked (1 minute)

With Claude Code's built-in filesystem MCP, you have these abilities:

Tool	What it does
list_directory	See what files are in a folder
read_file	Read the content of a file
write_file	Write to a file (we won't use this today)

That's it. Simple, focused, useful.

Claude Code's built-in filesystem tools for reading files and listing directories.

Step 2—Write the Skill (5 minutes)

Here's where you build the workflow. Skills live in a .claude folder in your project.

Your folder structure should look like this:

your-project/
├── .claude/
│   └── skills/
│       └── auto-tag-files/
│           └── SKILL.md
└── uploads/
    ├── file1.png
    ├── file2.pdf
    └── file3.mp4

Create a file at .claude/skills/auto-tag-files/SKILL.md and paste this:

---
name: auto-tag-files
description: Automatically tag and categorise files from the uploads folder. Use this when the user asks to tag files, organise uploads, categorise recent files, or analyse uploaded content.
allowed-tools: Bash, Read, Glob
---

# Auto-Tag Files

Automatically analyse and tag files from the uploads directory.

## Instructions

When this skill is activated:

1. Use Glob to find all files in the ./uploads directory
2. For each file found:
   - Use Read to examine the file content (for text/code files) or Read to view images/PDFs visually
   - Analyze the content to determine:
     - File type (image/document/video/code/data)
     - 3-5 meaningful keywords/tags based on the content
   - Create a structured summary with filename, type, and tags
3. Present all results in a clear, organised format (JSON or markdown table)

## Analysis Guidelines

- **Images**: Describe visual content, subjects, style, purpose
- **Documents**: Extract key topics, themes, purpose, audience
- **Videos**: Note subject matter, format, likely purpose
- **Code**: Identify language, frameworks, purpose, key functionality
- **Data**: Determine format, structure, domain, use case

## Output Format

Present results as a JSON array or markdown table:

[
  {
    "filename": "banner.png",
    "type": "image",
    "tags": ["branding", "header", "design", "marketing", "visual"]
  }
]

## Limits

- Process up to 10 files by default
- If the user specifies a limit, honor that number
- For large files, analyse a representative sample

What this does, step by step:

1. Frontmatter: Defines the skill name, description, and allowed tools (Bash, Read, Glob)
2. Instructions: Tells Claude exactly what to do when the skill is activated
3. Analysis Guidelines: Provides specific guidance for different file types
4. Output Format: Specifies how results should be structured (JSON or markdown table)
5. Limits: Sets reasonable boundaries (10 files by default, configurable)

This is your workflow, locked in. Claude follows these instructions every time, using the allowed tools to get consistent, predictable results.

Step 3—Use It Like a Human (30 seconds)

Open Claude and just type:

"Tag my last 3 uploads"

or

"Auto-tag my recent files"

That's it. Claude sees the skill is available and runs it. No JSON. No configuration. Just talk to it.

What happens behind the scenes:

1. Skill lists your 3 most recent files
2. Reads the content of each one
3. Claude analyses what type of file it is and what tags make sense
4. You get back a clean, structured list

Claude Code interface showing the auto-tag-files skill being called and displaying the structured tagging results.

Step 4—What You Actually Get Back

Here's what the output looks like:

[
  {
    "filename": "banner.png",
    "type": "image",
    "tags": ["marketing", "branding", "technology", "abstract-design", "header"]
  },
  {
    "filename": "pitch_deck.pdf",
    "type": "document",
    "tags": ["business", "presentation", "investment", "market-strategy", "startup"]
  },
  {
    "filename": "demo_video.mp4",
    "type": "video",
    "tags": ["demonstration", "product", "presentation", "marketing", "promotional"]
  }
]

Why this matters:

For non-technical users: "I just asked it to tag my files and it worked."

For developers: Clean, structured data you can pipe into other systems.

For teams: Everyone gets consistent tagging without manual work.

You're not just getting suggestions. You're getting reliable, repeatable results.

💡 Try this now: Drop 3 random files in your uploads folder and run the skill. See what happens. Then go tweak the skill to add more tags, change the format, or filter by file type. That's the power of having the logic in one place.

Handling Video Files with Scripts and Examples

⚙️ Advanced Technique

This section covers extending skills with custom scripts and examples files. If you're just getting started, the basic skill above works great—come back to this when you're ready to level up.

When you run the auto-tag skill on video files, you might notice something like this:

● Read(uploads/demo_video.mp4)
  ⎿  Error reading file

This is expected. Claude can't directly "read" binary video files the way it reads text documents or analyses images. Video files need special handling.

The Solution: Extract Frames First

Here's where skills get powerful. Instead of giving up on videos, we can:

1. Use a bash script to extract frames from videos (1 frame per second)
2. Store those frames in a folder matching the video name
3. Analyze the frames to understand what's in the video
4. Tag the video based on that analysis

This demonstrates two advanced skill features:

• Scripts (scripts/ folder) - External tools your skill can call
• Examples (EXAMPLES.md) - Reference documentation for complex workflows

Setting Up the Advanced Video Tagging

First, create the proper skill structure with scripts:

mkdir -p .claude/skills/auto-tag-files/scripts

Your folder structure will now look like:

your-project/
├── .claude/
│   └── skills/
│       └── auto-tag-files/
│           ├── SKILL.md
│           ├── EXAMPLES.md (new)
│           └── scripts/
│               └── extract_video_frames.sh (new)
└── uploads/
    ├── file1.png
    ├── file2.pdf
    └── demo_video.mp4

Create the Frame Extraction Script

Create .claude/skills/auto-tag-files/scripts/extract_video_frames.sh:

#!/usr/bin/env bash
set -euo pipefail

# Extract 1 frame per second from video files in uploads/
# Usage: ./scripts/extract_video_frames.sh <video_filename>

VIDEO_FILE="${1}"
VIDEO_NAME=$(basename "$VIDEO_FILE" | sed 's/\.[^.]*$//')
OUTPUT_DIR="uploads/$VIDEO_NAME"

if ! command -v ffmpeg >/dev/null 2>&1; then
  echo "ERROR: ffmpeg is not installed. Install with: brew install ffmpeg" >&2
  exit 1
fi

mkdir -p "$OUTPUT_DIR"

echo "Extracting frames from: $VIDEO_FILE → $OUTPUT_DIR"

# Extract 1 frame per second, max 10 frames
ffmpeg -y -i "$VIDEO_FILE" \
  -vf "fps=1,scale=640:-1" \
  -frames:v 10 \
  "$OUTPUT_DIR/frame_%02d.jpg" 2>/dev/null

echo "✓ Extracted frames to: $OUTPUT_DIR/"

Make it executable:

chmod +x .claude/skills/auto-tag-files/scripts/extract_video_frames.sh

Add the Examples File

Create .claude/skills/auto-tag-files/EXAMPLES.md:

# Auto-Tag Files Skill – Examples

## Example 1: Tagging Mixed Files (Including Videos)

When you have videos in your uploads folder:

**Step 1:** List all files in uploads/

ls uploads/
# demo_video.mp4, banner.png, pitch_deck.pdf

**Step 2:** For video files, extract frames first:

./scripts/extract_video_frames.sh uploads/demo_video.mp4

This creates: `uploads/demo_video/frame_01.jpg`, `frame_02.jpg`, etc.

**Step 3:** Analyze frames to understand video content:

- Read each extracted frame image
- Identify objects, actions, settings, style
- Generate tags based on visual analysis

**Step 4:** Tag the video based on frame analysis:

{
  "filename": "demo_video.mp4",
  "type": "video",
  "tags": ["product-demo", "software", "tutorial", "screen-recording", "presentation"]
}

## Example 2: Batch Processing with Videos

# Process all files, automatically handling videos
"Tag all my uploads including videos"

The skill will:
1. Detect video files by extension (.mp4, .mov, .avi, .mkv)
2. Run frame extraction script automatically
3. Analyze extracted frames
4. Tag video based on visual content
5. Clean up temporary frames after analysis

Update Your SKILL.md

Now update the main skill file to handle videos. Add this section after the "Analysis Guidelines":

## Video File Handling

Videos cannot be read directly. For video files:

1. Detect video files by extension (.mp4, .mov, .avi, .mkv)
2. Run the frame extraction script:
   `./scripts/extract_video_frames.sh uploads/[video_filename]`
3. Analyze the extracted frames from `uploads/[video_name]/frame_*.jpg`
4. Generate tags based on what you see across the frames
5. After tagging, optionally remove the frames folder to save space

**Note**: Requires `ffmpeg` to be installed. If not available, tag videos as "video-content-unknown" and note this in output.

How It Works in Practice

Now when you run the skill:

"Tag my files including the demo video"

Claude Code automatically detecting a video file, extracting frames, and generating meaningful tags based on the visual content analysis.

Claude will:

1. See demo_video.mp4 in uploads
2. Recognize it's a video file
3. Execute the bash script to extract frames
4. Read and analyse the extracted frames
5. Generate meaningful tags based on visual content
6. Return structured results including the video

Before (without scripts):

Error reading file: demo_video.mp4
Type: unknown

After (with scripts):

{
  "filename": "demo_video.mp4",
  "type": "video",
  "tags": ["product-demo", "smart-home", "aura-smart-hub", "presentation", "technology"]
}

Why This Matters

This isn't just about videos. You've now learned:

• Scripts extend what skills can do beyond Claude's built-in tools
• Examples files document complex workflows for future reference
• Skills can orchestrate external tools, not just use MCP servers
• The pattern scales to PDFs requiring OCR, audio transcription, data processing, etc.

You're not limited to what Claude can do natively. You're teaching it how to use the right tool for each job.

💡 Try this: Drop a video in your uploads folder and run the skill. Watch it extract frames, analyse them, and return structured tags. Then look in uploads/[video_name]/ to see the frames it analysed.

What Just Changed

You didn't just build a one-off automation. You gave Claude a reusable capability that works the same way every time you invoke it.

Before skills:

❌ Open Claude

❌ Explain what you want

❌ Hope it understands

❌ Check if it worked

❌ Repeat tomorrow

After skills:

✅ "Tag my files"

✅ Done

The difference between those two experiences? That's what makes skills worth understanding.

What's Next

Skills in Claude Code aren't a novelty—they're a different way of working with AI. Instead of asking "Can Claude help me with this?" you move to "Claude already knows how to do this."

You're not building throwaway scripts. You're creating persistent, reliable workflows. Whether you're in engineering, marketing, ops, or somewhere in between, this unlocks something real.

Now you've got the concept. You've seen the use-cases. You understand the MCP vs Skills distinction. And you've built something working that you can adapt.

Next step? Drop in your screenshots, tweak the skill for your actual workflow, and start building things your team will actually use.

Because the difference between "cool demo" and "thing I use every day" is reliability. And that's what skills give you.

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Read the full walkthrough and related posts on my blog: Beyond Deadwoodworks. It dives into Claude Code skills, MCP servers, and practical automations (auto-tagging, video handling) so you can move from brittle prompts to reliable workflows.

Connect on LinkedIn and share your perspective: what workflow would you hand over to Claude today?