How to Create In-Demand AI Skills for Claude Code and Other Agents

Mar 15, 2026

Purpose

I’ve watched developers build AI skills for months, hoping they’ll sell. Most fail because they build what they think people want, not what people actually ask for.

Then I found this insight from a successful skill creator: “Build to proven demand instead of guessing.”

This changed everything. The key is finding validated demand before writing a single line.

Phase 1: Find Proven Demand

Before building, I need to know people want what I’m making.

Check Skill Request Boards

Platforms like SkillMarket have request boards where buyers post needs. Some attach bounties. This is pre-validated demand.

A Reddit thread revealed what people actually want but can’t find:

“A proper Kubernetes debugging skill that knows how to read pod logs, check ingress configs, and trace networking issues”

“Next.js 15 + App Router + Prisma scaffolding. Or Rails 8. Or Django + HTMX.”

“A security audit skill written by someone who actually does penetration testing”

“Check against WCAG guidelines with real specifics, not just ‘add alt tags’”

These aren’t guesses. They’re requests from real users with real problems.

Monitor Community Forums

I look for repeated phrases like:

“I wish there was a skill for…”
“Why doesn’t anyone make…”
“I keep asking Claude to…”

Each repeated request is a potential skill.

Analyze Skill Gaps

I browse marketplaces for poorly-rated skills in high-demand areas. A 2-star Kubernetes skill with 500 downloads signals opportunity. People want it, but the current solution fails them.

Phase 2: Build the Skill

Once I find validated demand, I build using a proven structure.

Step 1: Define with Examples

Before writing, I clarify the scope with questions:

What specific functionality should this skill support?
Can I give 3-5 examples of how users would invoke it?
What would a user say to trigger this skill?

For a Kubernetes debugging skill, trigger phrases might be:

"Why is my pod stuck in CrashLoopBackOff?"
"Check the ingress configuration for my-service"
"Trace why my service can't connect to the database"

Step 2: Plan Bundled Resources

Not everything belongs in the SKILL.md file. I plan what to bundle separately:

Resource Type	When to Include	Example
`scripts/`	Code that gets rewritten repeatedly	`scripts/diagnose_pod.py`
`references/`	Documentation, schemas, patterns	`references/kubectl-cheatsheet.md`
`assets/`	Templates, config files	`assets/deployment-template.yaml`

Step 3: Create the Structure

Every effective skill follows this pattern:

skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter
│   │   ├── name: (clear, descriptive)
│   │   └── description: (triggers + use cases)
│   └── Markdown body
│       ├── Core workflow (sequential steps)
│       ├── Patterns and examples
│       └── Reference to bundled resources
└── Bundled Resources (optional)
    ├── scripts/
    ├── references/
    └── assets/

Step 4: Write the Frontmatter

The description is the PRIMARY triggering mechanism. I include what the skill does and specific trigger scenarios:

---
name: kubernetes-debugger
description: Comprehensive Kubernetes debugging with pod log analysis, ingress config validation, and network tracing. Use when debugging Kubernetes issues including: (1) Pod failures and CrashLoopBackOff, (2) Ingress and service connectivity issues, (3) ConfigMap and Secret problems, (4) Resource limits and scheduling failures, (5) Network policy debugging.
---

This description tells the AI exactly when to invoke the skill.

Step 5: Design Progressive Disclosure

Skills load in three levels:

Metadata (~100 words) - Always in context
SKILL.md body (<5k words) - When skill triggers
Bundled resources - As needed

I keep SKILL.md lean. Detailed references go in separate files:

## Advanced Debugging

- **Network tracing**: See [references/network-debugging.md](references/network-debugging.md)
- **Ingress troubleshooting**: See [references/ingress-issues.md](references/ingress-issues.md)
- **Pod lifecycle**: See [references/pod-states.md](references/pod-states.md)

Complete Example: Kubernetes Debugging Skill

kubernetes-debugger/
├── SKILL.md
├── scripts/
│   ├── diagnose_pod.sh
│   ├── trace_network.py
│   └── check_ingress.sh
├── references/
│   ├── pod-states.md
│   ├── network-debugging.md
│   ├── ingress-issues.md
│   └── common-errors.md
└── assets/
    ├── debug-pod-template.yaml
    └── network-policy-template.yaml

The SKILL.md body:

---
name: kubernetes-debugger
description: Comprehensive Kubernetes debugging with pod log analysis, ingress config validation, and network tracing. Use when debugging Kubernetes issues including: (1) Pod failures and CrashLoopBackOff, (2) Ingress and service connectivity issues, (3) ConfigMap and Secret problems, (4) Resource limits and scheduling failures, (5) Network policy debugging.
---

# Kubernetes Debugger

Debug Kubernetes issues with systematic pod log analysis, ingress validation, and network tracing.

## Workflow

Debugging Kubernetes involves these steps:

1. **Identify the failing component** - Check pod status, events, and logs
2. **Analyze pod state** - Determine if issue is startup, runtime, or resource-related
3. **Check networking** - Verify service, ingress, and network policies
4. **Trace the request path** - Follow traffic from ingress to pod
5. **Validate configurations** - Check ConfigMaps, Secrets, and environment

## Quick Start

```bash title="Terminal"
# Diagnose a failing pod
kubectl describe pod <pod-name> -n <namespace>

# Check recent events
kubectl get events -n <namespace> --sort-by='.lastTimestamp'

# View pod logs
kubectl logs <pod-name> -n <namespace> --previous

Common Patterns

Pattern 1: CrashLoopBackOff

Bad: Pod keeps restarting, no investigation

Good: Systematic diagnosis

# 1. Check pod status
kubectl get pod <name> -o wide

# 2. View termination reason
kubectl describe pod <name> | grep -A5 "Last State"

# 3. Check previous container logs
kubectl logs <name> --previous

# 4. Verify resource limits
kubectl describe pod <name> | grep -A10 "Limits"

Pattern 2: Service Connection Issues

Bad: Guessing network problems

Good: Trace the full path

# 1. Verify service exists
kubectl get svc <service-name>

# 2. Check service endpoints
kubectl get endpoints <service-name>

# 3. Test DNS resolution
kubectl run test-dns --rm -it --image=busybox -- nslookup <service-name>

# 4. Check ingress configuration
kubectl get ingress -o yaml

Advanced Usage

Network policy debugging: See references/network-debugging.md
Ingress troubleshooting: See references/ingress-issues.md
Pod state reference: See references/pod-states.md

Validation Checklist

Pod status identified and recorded
Logs analyzed for error patterns
Resource limits verified
Network path traced (ingress -> service -> pod)
ConfigMaps and Secrets validated
Solution documented for future reference

## Phase 3: Validate and Iterate

I test skills with real tasks before publishing:

1. **Test with real problems** - Run the skill on actual debugging scenarios
2. **Gather feedback** - What did users struggle with? What confused them?
3. **Iterate** - Update SKILL.md and resources based on usage

The best skills evolve through real-world use.

## Phase 4: Monetize

Once validated, I monetize through:

1. **Direct sales** - Package as `.skill` file on marketplaces like Agensi.io
2. **Bounty fulfillment** - Complete requests on Skill Request Boards
3. **Consulting upsell** - Offer custom skill development services
4. **Enterprise licensing** - Create team-specific skills

## Common Mistakes

**Building without demand**

I see developers build "cool" skills that nobody asked for. They don't sell.

**Vague frontmatter**

```yaml title="Frontmatter Comparison"
# BAD: Vague description
description: Kubernetes debugging skill

# GOOD: Specific triggers
description: Kubernetes debugging with pod log analysis, ingress validation. Use when: (1) Pod failures and CrashLoopBackOff, (2) Ingress connectivity issues, (3) ConfigMap problems.

Overloading SKILL.md

Putting everything in one file makes the skill slow to load. I use progressive disclosure.

No bundled resources

Users pay for complete solutions. Scripts, templates, and references add value.

Ignoring feedback

Skills that break or become outdated get bad reviews. I plan for maintenance.

Summary

In this post, I showed how to create AI skills that have proven demand. The key point is to validate demand before building, using Skill Request Boards and community feedback.

The process involves: (1) finding validated demand through request boards and forums, (2) building skills with clear workflows and bundled resources, (3) testing with real tasks, and (4) monetizing through marketplaces or bounties.

The skills people want but can’t find include Kubernetes debugging, stack-specific scaffolding, security audits, and accessibility reviews. These gaps represent real opportunities for developers who can translate their expertise into reusable skills.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Agensi.io Marketplace
👨‍💻 Reddit Discussion: Building AI Skills

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!