Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add celticht32/Couchbase-Skills-for-Claude.ai --skill couchbase-kubernetesgit clone --depth 1 https://github.com/celticht32/Couchbase-Skills-for-Claude.aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-kubernetes)<a href="https://agentmods.dev/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-kubernetes"><img src="https://agentmods.dev/badge/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-kubernetes/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-kubernetes"><img src="https://agentmods.dev/badge/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-kubernetes.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00192 | $0.01303 |
| Opus 5 | $0.00096 | $0.00651 |
| Sonnet 5 | $0.00038 | $0.00261 |
| Haiku 4.5 | $0.00019 | $0.00130 |
Grade A, and why
couchbase-kubernetes scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Couchbase on Kubernetes (Autonomous Operator)
A skill for deploying and operating Couchbase on Kubernetes using the Couchbase Autonomous Operator (CAO). CAO is a Level 5 Kubernetes Operator — it manages the full lifecycle of a Couchbase cluster as a set of Kubernetes custom resources.
Distinct from:
couchbase-capella— fully managed Capella (no Kubernetes to manage)couchbase-upgrade— upgrading the Couchbase binary outside Kubernetes (or the planning layer for k8s upgrades)couchbase-sizing— the capacity math applies equally; this skill covers the k8s-specific mechanics
When this skill applies
- "How do I deploy Couchbase on Kubernetes?"
- "How do I install the Couchbase Autonomous Operator?"
- "How do I configure a CouchbaseCluster resource?"
- "How do I configure rack awareness / AZ-awareness in Kubernetes?"
- "How do I size persistent volumes for Couchbase pods?"
- "How do I do a rolling upgrade via the operator?"
- "How do I configure Prometheus monitoring with CAO?"
- "How does the operator manage buckets, users, and backups?"
- "How do I use the CAO Helm chart?"
Pick the right reference
| Question | Read |
|---|---|
| "Installing CAO — Helm chart, namespaces, RBAC, admission controller" | references/installation.md |
| "CouchbaseCluster CRD — node topology, services, server groups, resources, storage" | references/cluster-crd.md |
| "Buckets, users, backups, XDCR as Kubernetes resources" | references/supporting-crds.md |
| "Operations — rolling upgrades, scaling, AZ awareness, Prometheus" | references/operations.md |
Three core principles
Principle 1 — The operator reconciles, not you. Don't edit Couchbase directly through the UI or REST API when CAO is managing the cluster. CAO continuously reconciles the desired state (your CRDs) against the actual cluster state. Manual changes made outside the operator will be reverted on the next reconciliation cycle. All changes go through the CRD.
Principle 2 — Persistent volumes are the most important sizing decision.
Data, Index, and Analytics nodes need persistent volumes that survive pod restarts. Size them generously — you can expand a PV online (if your storage class supports it) but you cannot shrink it. Use storage classes with allowVolumeExpansion: true and volumeBindingMode: WaitForFirstConsumer for AZ-aware scheduling.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 92 lines · 192 tokens per session scan A 49e39d480698
couchbase-kubernetes is a skill published in the GitHub repository celticht32/Couchbase-Skills-for-Claude.ai (4 stars, last pushed 2mo ago), licensed MIT. It adds 192 tokens to every session and 1,303 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
data-engineer
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
chroma
Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source…
db-seed
Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed…
d1-drizzle-schema
Generate Drizzle ORM schemas for Cloudflare D1 databases with correct D1-specific patterns. Produces schema files, migration commands, type exports, and DATABASESCHEMA.md documentation. Handles D1 quirks: foreign keys always enforced, no native BOOLEAN/DATETIME types, 100 bound parameter limit, JSON stored as TEXT.…
docker-debugger
Debug Docker containers, fix Dockerfile issues, optimize images, and troubleshoot docker-compose. Use when having Docker problems, container issues, or optimizing Docker builds.
d1-migration
Cloudflare D1 migration workflow: generate with Drizzle, inspect SQL for gotchas, apply to local and remote, fix stuck migrations, handle partial failures. Use when running migrations, fixing migration errors, or setting up D1 schemas.