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-magmagit 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-magma)<a href="https://agentmods.dev/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-magma"><img src="https://agentmods.dev/badge/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-magma/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-magma"><img src="https://agentmods.dev/badge/skills/celticht32/couchbase-skills-for-claude.ai/couchbase-magma.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 | $0.00158 | $0.01501 |
| Opus 5 | $0.00079 | $0.00750 |
| Sonnet 5 | $0.00032 | $0.00300 |
| Haiku 4.5 | $0.00016 | $0.00150 |
Grade A, and why
couchbase-magma 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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Couchbase Magma Storage Engine
A skill for understanding and tuning the Magma storage engine — Couchbase's LSM-tree-based storage backend optimized for large datasets per node.
When this skill applies
- "Should I use Magma or couchstore for my bucket?"
- "What changed with Magma being the default in 8.0?"
- "What does 128 vBuckets mean vs 1024?"
- "How does Magma handle compaction differently?"
- "How much memory does Magma need?"
- "My write performance is different after upgrading to 8.0"
- "How do I set the storage engine when creating a bucket?"
Magma vs couchstore at a glance
| Couchstore (classic) | Magma | |
|---|---|---|
| Architecture | B-tree per vBucket | LSM-tree per vBucket |
| Default vBuckets | 1024 | 128 (8.0 default) |
| RAM per node minimum | 100 MB per bucket (min); memory-to-data ratio 10% | 100 MB (128 vBucket) / 1 GiB (1024 vBucket); memory-to-data ratio 1% |
| Optimized for | Smaller datasets, high read ratio | Large datasets (>100M docs/node), high write rate |
| Write performance | Good at low-moderate write rates | Better at sustained high write rates (LSM absorbs bursts) |
| Read performance | Excellent (direct B-tree lookup) | Good (may require multi-level lookup on cold data) |
| Compaction | Explicit compaction cycle | Continuous background compaction (no manual trigger needed) |
| Disk space efficiency | Good after compaction | Good continuously (LSM merges in background) |
| Available | CE and EE | EE only |
When to use Magma
Use Magma when:
- Dataset exceeds ~100M documents per node
- Write rate is high and sustained (> 50K writes/sec per node)
- Memory is constrained (Magma's minimum per-bucket RAM is lower)
- You're using fullEviction (Magma pairs well with fullEviction's design)
Stick with couchstore when:
- Dataset is small-to-medium (< 50M documents per node)
- Read performance is critical and the working set fits in RAM
- You're on Community Edition
- You need to stay on 1024 vBuckets for an existing operational reason
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 · 114 lines · 158 tokens per session scan A 880ccb115383
couchbase-magma is a skill published in the GitHub repository celticht32/Couchbase-Skills-for-Claude.ai (4 stars, last pushed 2mo ago), licensed MIT. It adds 158 tokens to every session and 1,501 once invoked, about $0.0008 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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