DEV Community: Dileep Kumar Pandiya

How to Know If Your Claude SKILL.md Actually Works

Dileep Kumar Pandiya — Mon, 13 Jul 2026 01:54:47 +0000

I spent today shipping a tool I've wanted for months.

If you build with Claude, you've probably written a SKILL.md file. And you've probably shipped it based on gut feel.

That changes today.

The problem nobody talks about
Skills are just system prompt injections. The honest question is: does this skill actually improve Claude's outputs, or does it just feel like it does?

Most teams answer this by eyeballing a few responses. That's not evaluation. That's vibes. Three things make vibes-based skill evaluation dangerous:

Position bias — if you ask Claude to compare its own outputs, it favors whichever it sees first

Silent regression — model updates, skill edits, and context changes can silently make a skill worse

No shared rubric— every engineer scores skills differently, so "this skill is good" means nothing

What I built
skilleval — a CLI that gives you a repeatable, objective score for any SKILL.md in under 2 minutes.

bash
npx @dileeppandiya/skilleval ./my-skill --tasks ./tasks.yaml

Real output from the sample skill in the repo:

━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
skilleval results - api-design - 2 tasks
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Skill effectiveness: +0.3 / 3
Tasks improved: 1 / 2 (50%)
Tasks hurt: 1 / 2 (50%)
Confidence: UNRATED (use --runs 3+ for confidence)
task-003 +2.5 Output A provides more robust API design...
task-004 -2.0 Output A is more comprehensive...
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Runner: claude-sonnet-4-6 | Judge: gemini-3.5-flash
Estimated API cost this run: $0.101
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Notice the mixed signal. The skill helped on task-003 but hurt on task-004. skilleval doesn't inflate scores to make skills look good. It reports what the judge actually found.

How it works
Blind A/B testing — each task runs twice concurrently, with the skill injected into the system prompt vs. raw context only.

Randomized judge — a Gemini Flash judge compares outputs. Which output gets labeled A or B is randomized per task with a seeded RNG, eliminating position bias completely.

Margin-based scoring — the judge returns a winner + margin (0–3): margin 3 gives 3.0/0.0, margin 0 gives a genuine tie at 1.5/1.5.

Honest confidence — single runs show UNRATED. One sample tells you nothing about stability. Real confidence (HIGH/MEDIUM/LOW) only appears at --runs 3+.

bash
skilleval ./my-skill --tasks ./tasks.yaml --runs 3

Five things that make it different

Deterministic assertions — not everything should be left to LLM opinion:

tasks:
  - id: login-endpoint
    prompt: "Design a login endpoint"
    assertions:
      must_contain:
        - "POST"
        - "401"
      must_not_contain:
        - "GET /login"
      min_length: 100

Assertion failures automatically count as hurt tasks, no LLM needed to know "missing POST method" is wrong.

Multi-turn conversation tasks — most real skills operate across turns, not single prompts. The skill injects into the system prompt for the full conversation, and the judge sees complete context when scoring.
Run history + regression detection — every run auto-saves to .skilleval/history/. After two runs:

bash
skilleval diff ./my-skill

── skilleval diff: api-design ──────────────────
vs previous run: 2026-07-11T14:30:00Z
Effectiveness: +0.3 → +0.8 (+0.5 ↑)
Tasks improved: 1 → 2 (+1 ↑)
Tasks hurt: 1 → 0 (-1 ↓)

This is "skill hell" prevention in practice — you can see the exact moment a skill started regressing.

Skill version comparison— test v1 vs v2 directly:

bash
skilleval ./skill-v1 --compare ./skill-v2 --tasks ./tasks.yaml

No more "I think v2 is better." Now you know.

One-line CI integration — block PRs that silently break skills:

on:
  pull_request:
    paths:
      - '**/SKILL.md'

jobs:
  skilleval:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: dileepkpandiya/skilleval@main
        with:
          skill-path: ./my-skill
          tasks: ./tasks/tasks.yaml
          fail-below: '0.3'
          fail-if-hurt-pct: '50'
          anthropic-api-key: ${{ secrets.ANTHROPIC_API_KEY }}
          gemini-api-key: ${{ secrets.GEMINI_API_KEY }}

plaintext
Exit code 0 = pass, 1 = gate failed, 2 = error.

Cost
Setup Cost
5 tasks, --runs 1, Gemini Flash judge ~$0.10
5 tasks, --runs 3 (real confidence) ~$0.30
10 tasks, --runs 3 ~$0.60

Use --cost to see an estimate before spending anything. Gemini Flash is the default judge, and the free tier handles casual iteration easily.

Quick start

bash

Try it immediately on the built-in sample

git clone https://github.com/dileepkpandiya/skilleval
cd skilleval
npx @dileeppandiya/skilleval ./samples/api-design \
  --tasks ./tasks/sample-tasks.yaml

Scaffold a new skill

skilleval --init ./my-new-skill

## Install globally
npm install -g @dileeppandiya/skilleval

You'll need ANTHROPIC_API_KEY for the Claude runner and GEMINI_API_KEY for the default judge.

What's still missing
Honest gaps in v0.3.0:

Tool-call evaluation — if your skill affects which tools Claude calls, text-output scoring misses that

Visual history dashboard — the diff command is CLI only, no charts yet

Local model judge support — no Ollama/local-model judging for fully offline eval yet

The repo
MIT licensed, open source, TypeScript. 38 unit tests, zero API calls needed to run the test suite, GitHub Action included.

👉 github.com/dileepkpandiya/skilleval

What are you using to evaluate your skills today? I'd genuinely love to know what's broken about this for your use case, you can file an issue or drop a comment below.

Cutting-Edge Strategies for Minimizing Latency

Dileep Kumar Pandiya — Wed, 10 Apr 2024 00:29:01 +0000

Latency is a critical performance metric in software engineering. Let’s dive deep into advanced techniques for reducing latency, providing practical examples and advice for software engineers.

Load Balancing
Load balancing is a technique used to distribute network traffic across multiple servers. For instance, if you’re developing a microservices-based application, you could use a load balancer like Nginx or HAProxy to distribute incoming requests evenly across your services. This prevents any single service from becoming a bottleneck, reducing latency.
Content Delivery Networks (CDNs)
A CDN is a network of servers distributed globally. When a user makes a request, the CDN redirects it to the nearest server, reducing latency. As a software engineer, you can leverage CDNs to serve static assets of your web applications. Services like Cloudflare and AWS CloudFront can help you set up a CDN for your application.
Database Optimization
Database queries can be a source of high latency. Techniques for optimization include indexing, query optimization, and denormalization.

For example, if you’re developing a web application with a user login feature, you could create an index on the username field in your users table. This would make the lookup of a user by username much faster, reducing latency.

Compression
Data compression reduces the size of the data to be transferred, reducing latency. For instance, you can use Gzip or Brotli compression in your web server to compress your HTML, CSS, and JavaScript files. This reduces the amount of data sent over the network, leading to faster load times and lower latency.
Caching
Caching involves storing copies of frequently accessed data in a fast-access cache. For example, if you’re developing a news website, you could cache the top news stories and serve them from the cache instead of generating them for each request. This could significantly reduce latency. Redis is a popular in-memory database used for caching in many applications.
HTTP/2 and HTTP/3
HTTP/2 and HTTP/3 are newer versions of the HTTP protocol with features for reducing latency. For example, HTTP/2 introduces multiplexing, allowing multiple requests and responses to be sent simultaneously over a single connection, reducing the overall latency. As a software engineer, you can enable HTTP/2 or HTTP/3 in your web server to take advantage of these features.
Using a Real-Time Operating System (RTOS)
For systems requiring real-time processing, an RTOS can help reduce latency. For example, if you’re developing embedded software for a drone, an RTOS could help you process sensor data in real-time with very low latency.

Final Thoughts
Reducing latency is a critical aspect of software engineering. By employing advanced techniques like load balancing, using CDNs, database optimization, compression, caching, and leveraging newer HTTP protocols, software engineers can significantly reduce latency, leading to a smoother and more responsive user experience.

The Evolution of Microservices (Past, Present, and Future)

Dileep Kumar Pandiya — Tue, 09 Apr 2024 21:32:59 +0000

The microservices architecture has become a cornerstone in the evolution of software development, revolutionizing how applications are conceived, developed, and deployed. This blog embarks on a detailed exploration of the journey of microservices from their inception to their current status and anticipates future trends that will shape their progression.

The Past: Monolithic Architectures and the Birth of Microservices
In the early days of software development, monolithic architectures were the norm. Applications were built as single, cohesive units where all components, including the user interface, business logic, and data access layers, were combined and deployed as a whole. This approach, while straightforward for smaller applications, presented significant challenges as systems grew more large and complex.

As enterprises expanded and their software needs evolved, the drawbacks of monolithic architectures became increasingly apparent like

Difficult to scale: Increasing the size of one part required scaling the whole application, leading to waste.
Hard to maintain and update: Every change needed a full redeployment, causing more work.
Higher error risk: With complete redeployment for each change, the chances of making mistakes went up.
Increased downtime: Frequent and significant downtime occurred because the whole system had to be restarted for updates.

The quest for a more flexible and scalable architecture led to the rise of RESTful services in the 2000s. Representational State Transfer (REST) advocated for a stateless, client-server communication model, laying the groundwork for more decoupled architectures. RESTful APIs facilitated communication between independent components, paving the way for the development of microservices.

The Rise of Microservices
Microservices architecture emerged as a response to the limitations of monolithic systems, advocating for dividing a large application into smaller, independently deployable services. Each service in a microservices architecture runs its own process and communicates with others through well-defined APIs, typically over HTTP.

This architectural style brought several advantages:

Scalability: Services can be scaled independently, allowing for more efficient resource use and the ability to handle varying loads effectively.
Flexibility: Independent development of services enables teams to work in parallel, reducing development time and facilitating continuous deployment and integration.
Technological Diversity: Teams can choose the best technology stack for each service based on its specific requirements, rather than being constrained to a single technology for the entire application.
Resilience: The failure of a single service has a limited impact, enhancing the overall stability of the application.

The adoption of microservices was further accelerated by advancements in cloud computing, containerization, and orchestration tools like Kubernetes, which solved many logistical challenges associated with deploying and managing a multitude of services.

The Present: Microservices in Today’s Software Landscape
Today, microservices are more than just an architectural trend; they are a fundamental part of the modern software development paradigm. They have been embraced across industries for their agility, modularity, and compatibility with the cloud-native approach.

Companies like Netflix, Amazon, and Spotify have become poster children for successful microservices implementations, showcasing the architecture’s ability to support rapid growth and dynamic scaling requirements. These organizations have demonstrated that microservices can facilitate a more agile development process, enabling faster iteration, innovation, and time-to-market.

The current landscape of microservices is characterized by an ecosystem rich with tools and frameworks designed to support the architecture. From Docker for containerization to Kubernetes for orchestration, and from Spring Boot for rapid application development to Prometheus for monitoring, the infrastructure around microservices has matured, making it more accessible and robust.

However, the adoption of microservices is not without its challenges. Issues such as service discovery, network latency, data consistency, and inter-service communication require careful planning and implementation. Moreover, the increased complexity of managing multiple services and the cultural shift needed within organizations to adopt a microservices mindset can be significant hurdles.

The Future: Where Are Microservices Heading?
Looking forward, the evolution of microservices is likely to be influenced by several key trends:

Serverless Architectures and Function-as-a-Service (FaaS): Building on the principles of microservices, serverless computing abstracts the infrastructure layer even further, allowing developers to focus solely on code. This can lead to even more granular and event-driven architectures, where functions are invoked in response to specific events, scaling automatically as needed.
AI and Machine Learning Integration: As artificial intelligence and machine learning capabilities advance, they are increasingly being integrated into microservices architectures. AI can be used to automate operational tasks, predict system failures, and dynamically optimize resource allocation.
Increased Automation and CI/CD: Continuous integration and continuous delivery (CI/CD) are integral to the microservices approach, and future developments will likely see even more automation of the deployment pipelines, reducing manual steps and speeding up the delivery process.
Edge Computing: As the Internet of Things (IoT) continues to grow, edge computing will become more prevalent. Microservices can be deployed closer to the source of data generation (the edge), reducing latency and bandwidth use and enabling more responsive and real-time services.

The journey of microservices from a novel architectural style to a mainstream development paradigm illustrates the industry’s constant quest for more efficient, scalable, and resilient software solutions. As we look to the future, it is clear that microservices will continue to evolve, influenced by emerging technologies and shifting business needs.

Microservices are not just a technical strategy but a foundational element that supports the agile, fast-paced, and innovative nature of modern digital enterprises. As this evolution progresses, the potential of microservices to adapt and integrate with new technologies will undoubtedly lead to even more sophisticated and agile software systems, shaping the future of software development in the digital age.