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-ai-applicationsgit 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-ai-applications)<a href="https://agentmods.dev/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-ai-applications"><img src="https://agentmods.dev/badge/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-ai-applications/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-ai-applications"><img src="https://agentmods.dev/badge/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-ai-applications.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 | $0.00202 | $0.01376 |
| Opus 5 | $0.00101 | $0.00688 |
| Sonnet 5 | $0.00040 | $0.00275 |
| Haiku 4.5 | $0.00020 | $0.00138 |
Grade A, and why
couchbase-ai-applications 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Couchbase AI Applications
A skill for designing AI-powered applications on Couchbase — RAG pipelines, vector search architecture, embedding strategies, and agent memory patterns. Covers the full stack from document design through embedding generation, index selection, retrieval, and LLM integration.
Distinct from:
couchbase-fts— FTS index mechanics and query syntax (the lower-level how); this skill is about the application-level what and whycouchbase-data-modeling— general document design; this skill covers AI-specific document patternscouchbase-app-integration— SDK patterns; this skill covers AI framework integration
If the conversation is "I'm building an AI feature / RAG pipeline / agent," this is the right skill.
When this skill applies
- "How do I build a RAG pipeline with Couchbase?"
- "Which vector index type should I use — HVI, CVI, or SVI?"
- "How do I store and search embeddings at scale?"
- "How do I combine vector search with keyword/metadata filters?"
- "How do I use Couchbase as memory for an AI agent?"
- "What's the difference between Hyperscale and Composite vector indexes?"
- "How do I integrate Couchbase with LangChain / LlamaIndex?"
- "How do I build a billion-scale vector search?"
- "How do I evaluate retrieval quality in my RAG pipeline?"
Pick the right reference
| Question | Read |
|---|---|
| "Which of the three vector index types should I use?" | references/vector-index-types.md |
| "How do I design my documents and data pipeline for AI?" | references/data-design.md |
| "How do I build a RAG pipeline end to end?" | references/rag-patterns.md |
| "LangChain / LlamaIndex / custom framework integration" | references/framework-integration.md |
Three core principles
Principle 1 — Choose the index type before writing any code.
Couchbase 8.0 has three vector index types with meaningfully different characteristics. Choosing wrong means an index rebuild. HVI (Hyperscale) is for billion-scale with low memory; CVI (Composite) is for filtered vector search; SVI (Search Vector Index, inside FTS) is for hybrid text+vector in one index. See references/vector-index-types.md before picking.
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 · 75 lines · 202 tokens per session scan A 6cefcb7d500c
couchbase-ai-applications is a skill published in the GitHub repository celticht32/Couchbase-Skills-for-Claude.ai (4 stars, last pushed 2mo ago), licensed MIT. It adds 202 tokens to every session and 1,376 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.
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