confluent-skill-creator

Files (16)

• .env.template
• .gitignore
• SKILL.md
• credentials.yaml.template
• evals.json · evals/
• confluent-best-practices.md · references/
• credential-templates.md · references/
• example-enrichment.md · references/
• plan-template.md · references/
• schema-guid-header.md · references/
• check_compute_pool.py · scripts/
• cleanup_resources.py · scripts/
• consume_and_verify.py · scripts/
• produce_data.py · scripts/
• register_schema.py · scripts/
• requirements.txt · scripts/

SKILL.md (first lines)

# Confluent Skill Creator

A specialized skill creator for building Confluent product skills that are tested against real Confluent environments (local, platform, cloud, or WarpStream).

This skill extends the base skill-creator workflow with Confluent-specific requirements:
- **Flexible scope**: Skills can be use-case focused (e.g., "CDC from PostgreSQL to Iceberg") or component-specific (e.g., "Flink SQL skill", "Schema Registry skill")
- **Platform targeting**: Skills either target a specific platform (named in the skill) or support all platforms with platform-specific reference files
- **Environment testing**: Real E2E tests against local Confluent, Confluent Platform, Confluent Cloud, or WarpStream
- **Safe CRUD operations**: Show full plan and get confirmation before creating/modifying Confluent resources
- **Credential management**: Proper .env file setup for all required credentials
- **Spec compliance**: Created skills validated against the [Agent Skills specification](https://agentskills.io/specification)
- **Smart defaults**: Schema Registry with JSON_SR (schema GUID in header) by default

## Prerequisites

- Python 3.9+
- `confluent-kafka[avro,json,protobuf]`, `requests`, `jsonschema`, `python-dotenv` Python packages (see `scripts/requirements.txt`)
- Access to a Confluent environment (Cloud, Platform, local Docker, or WarpStream) for E2E testing

## When to use this skill

Use this skill when a user wants to create a skill for external users that involves:
- Kafka topics, producers, consumers, or streaming applications
- Flink SQL or stream processing
- Connectors (Debezium CDC, sink connectors, etc.)
- Schema Registry (Avro, JSON Schema, Protobuf)
- Tableflow for data lake integration
- CDC pipelines from databases to data lakes
- Real-time data enrichment or transformation

## Core Workflow

1. **Understand scope and platform targeting** (Confluent-specific)
2. Capture intent and interview
3. Write the skill draft
4. **Validate spec compliance** (Confluent-specific)
5. Set up test environment and credentials (Confluent-specific)
6. Create test cases and run E2E tests (Confluent-specific)
7. Evaluate with viewer
8. Iterate until satisfied
9. Optimize description
10. Package and deliver

---

## Skill Anatomy and Progressive Disclosure

Apply these structural principles to **every skill you create**. They extend the base [skill-creator guidance](https://github.com/anthropics/skills/blob/main/skills/skill-creator/SKILL.md); the spec-compliance checks in [Step 4](#step-4-validate-spec-compliance) enforce the specifics, while this section is the mental model.

### Anatomy of a skill

```
skill-name/
├── SKILL.md (required)
│   ├── YAML frontmatter (name, description required)
│   └── Markdown instructions
└── Bundled resources (optional)
    ├── scripts/     — executable code for deterministic/repetitive tasks
    ├── references/  — docs loaded into context as needed
    └── assets/      — files used in output (templates, icons, fonts)
```

### Progressive disclosure — a three-level loading system

Skills load context in three tiers, so the model only pays for what it needs:

1. **Metadata** (`name` + `description`) — always in context (~100 words). This is the trigger; invest here.
2. **SKILL.md body** — loaded whenever the skill triggers (<500 lines ideal).
3. **Bundled resources** — loaded only as needed (unlimited; scripts can execute without being read into context).

Word counts are approximate — go longer when a skill genuinely needs it.

**Key patterns:**
- Keep SKILL.md under 500 lines. Approaching the limit? Add a layer of hierarchy and point clearly to where the model should look next.
- Reference files explicitly from SKILL.md with guidance on *when* to read each one — never have SKILL.md pull them all in upfront (see [Step 4](#step-4-validate-spec-compliance)).
- For large reference files (>300 lines), include a table of contents at the top.

### Domain organization

When a skill spans multiple domains/frameworks, organize by variant and let the model read only the relevant file — the same pattern as cross-platform Confluent skills (`references/confluent-cloud.md`, `references/warpstream.md`, …):

```
cloud-deploy/
├── SKILL.md          # workflow + variant selection
└── references/
    ├── aws.md
    ├── gcp.md
    └── azure.md
```

### Principle of lack of surprise

A skill's behavior must not surprise a user who has read its description. Skills must never contain malware, exploit code, or anything that could compromise system security, and must not facilitate unauthorized access, data exfiltration, or other malicious activity. Benign creative framing (e.g. "roleplay as X") is fine — misleading or malicious skills are not.

---

## Step 1: Understand Scope and Platform Targeting

### Skill scope

Understand what the user wants to build. Skills can be either **use-case focused** or **component-specific** — both are valid.

**Use-case focused** — solves a specific end-to-end problem:
- "Real-time customer order enrichment using Flink SQL"
- "CDC pipeline from PostgreSQL to Iceberg using Debezium and Tableflow"
- "Real-time anomaly detection with Kafka Streams"

**Component-specific** — covers a Confluent component's capabilities:
- "Flink SQL skill"
- "Kafka connector skill"
- "Schema Registry skill"
- "Tableflow skill"

If the user's request is ambiguous, ask:

> "What should this skill cover? It can be a specific use case (e.g., 'CDC pipeline from Postgres to Iceberg') or focused on a component (e.g., 'Flink SQL skill'). What's the scope you have in mind?"

### Platform targeting

From confluentinc/agent-skills, Apache-2.0.

More from confluentinc/agent-skills