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 agentmods add skills/g1joshi/agent-skills/couchbasenpx skills add G1Joshi/Agent-Skills --skill couchbasegit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/couchbase)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/couchbase"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/couchbase.svg" alt="Measured on agentmods" 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 | $0.00018 | $0.00390 |
| Opus 5 | $0.00009 | $0.00195 |
| Sonnet 5 | $0.00004 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
couchbase 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 2d 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.
What it actually says
Couchbase
Couchbase works as a Key-Value store (managed memory cache) + Document Database. It is famous for its "memory-first" architecture and N1QL (SQL for JSON).
When to Use
- Caching + Persistence: When you need the speed of Redis but the persistence/querying of MongoDB.
- Mobile Sync: Couchbase Mobile / Sync Gateway provides robust offline-sync for mobile apps.
- SQL on JSON: N1QL allows using standard SQL (
SELECT * FROM users JOIN orders) on JSON documents.
Quick Start
-- N1QL Query
SELECT u.name, ARRAY_AGG(o.item) as orders
FROM `travel-sample`.inventory.users u
JOIN `travel-sample`.inventory.orders o ON u.id = o.user_id
WHERE u.city = "Paris"
GROUP BY u.name;
Core Concepts
Memory First
Writes go to memory first (microseconds), then disk. Reads serve from memory if hot.
Couchbase Capella
The fully managed Database-as-a-Service (DBaaS) version. Best for 2025 usage.
Buckets, Scopes, Collections
Hierarchy: Cluster -> Bucket -> Scope -> Collection -> Document. Maps roughly to Database -> Schema -> Table -> Row.
Best Practices (2025)
Do:
- Index N1QL queries: Like a relational DB, specific queries need specific GSI (Global Secondary Indexes).
- Use Vector Search: 2025 versions support Vector Search for AI apps.
Don't:
- Don't use Views: Old MapReduce Views are deprecated. Use N1QL.
References
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.
- 2d ago First seen · 55 lines · 18 tokens per session scan A 17a11fbfbadd
couchbase is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 18 tokens to every session and 390 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
redis-operations
Redis operational runbooks — memory management, eviction policy, persistence config, Sentinel/Cluster, K8s-hosted Redis ops.
redis-caching-and-pubsub
Architecting high-performance caching strategies, Cache-Aside/Write-Through/Write-Behind patterns, Redis Pub/Sub, Streams, eviction policies, distributed locking (Redlock), and memory optimization. Use when implementing Redis caching, real-time messaging, rate limiting, or session management.
deprecation-and-migration
Manages deprecation and migration. Use when removing old systems, APIs, or features. Use when migrating users from one implementation to another. Use when migrating a database schema in production, such as renaming or dropping a column without downtime (expand/contract). Use when deciding whether to maintain or sunset…
pinecone
Managed vector DB for production RAG and search.
firebase-database
Use when syncing real-time data, structuring JSON trees, reading/writing, creating listeners, enabling offline persistence, managing presence, sharding, or writing security rules.
qdrant-scaling-query-volume
Guides Qdrant query volume scaling. Use when someone asks 'query returns too many results', 'scroll performance', 'large limit values', 'paginating search results', 'fetching many vectors', or 'high cardinality results'.