DevOps & Security Agent Skills is a knowledge base of agent-readable skills covering software delivery, cloud infrastructure, security, compliance, AI operations, and incident response. It is for SRE, platform, infrastructure, and security teams using agents to work with systems such as Kubernetes, Terraform, and major cloud platforms. The catalogue provides a selection of the project's skills for agents including Claude Code, Cursor, and Codex.
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 BagelHole/DevOps-Security-Agent-Skills --skill vector-database-opsgit clone --depth 1 https://github.com/BagelHole/DevOps-Security-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/bagelhole/devops-security-agent-skills/vector-database-ops)<a href="https://agentmods.dev/skills/bagelhole/devops-security-agent-skills/vector-database-ops"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/vector-database-ops/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/bagelhole/devops-security-agent-skills/vector-database-ops"><img src="https://agentmods.dev/badge/skills/bagelhole/devops-security-agent-skills/vector-database-ops.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 37 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 45 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium MCP Rug Pull · line 187 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- medium Data Exfiltration · line 228 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00054 | $0.02150 |
| Opus 5.5 | $0.00022 | $0.00860 |
| Sonnet 5.5 | $0.00011 | $0.00430 |
| Haiku 4.5 | $0.00005 | $0.00215 |
Grade A, and why
vector-database-ops scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "http://localhost:6333/collections/documents/snapshots" How it starts
The opening of the file, as written. The whole thing — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Vector Database Operations
Run production vector databases for AI-powered search, RAG, and recommendation systems.
When to Use This Skill
Use this skill when:
- Setting up a vector database for a RAG or semantic search application
- Choosing between Qdrant, Weaviate, pgvector, or Pinecone
- Managing collections, indexes, and data migrations
- Optimizing query performance and indexing for production loads
- Implementing multi-tenant vector search with namespace isolation
Vector Database Comparison
| Database | Best For | Hosting | Filtering | Scale |
|---|---|---|---|---|
| Qdrant | High-performance, rich filtering, self-hosted | Self / Cloud | Excellent | Very High |
| Weaviate | Schema-first, hybrid search, multi-modal | Self / Cloud | Good | High |
| pgvector | Already on Postgres, simple use cases | Self | Good | Medium |
| Pinecone | Zero-ops managed, serverless | Managed only | Good | Very High |
| Chroma | Local dev, prototyping | Self only | Basic | Low-Medium |
Qdrant — Production Deployment
# Docker (single node)
docker run -d \
--name qdrant \
-p 6333:6333 \
-p 6334:6334 \
-v $(pwd)/qdrant-data:/qdrant/storage \
qdrant/qdrant:latest
# With custom config
docker run -d \
--name qdrant \
-p 6333:6333 \
-v $(pwd)/qdrant-data:/qdrant/storage \
-v $(pwd)/qdrant-config.yaml:/qdrant/config/production.yaml \
qdrant/qdrant:latest
# qdrant-config.yaml
storage:
storage_path: /qdrant/storage
on_disk_payload: true # store payload on disk (saves RAM)
service:
max_request_size_mb: 32
hnsw_index:
m: 16 # graph connections per node
ef_construct: 100 # accuracy vs build time trade-off
full_scan_threshold: 10000 # switch to brute force below this
quantization:
scalar:
type: int8
quantile: 0.99
always_ram: true # keep quantized index in RAM
telemetry_disabled: true
Qdrant Collection Management
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.
- 1mo ago First seen · 286 lines · 54 tokens per session scan A c9e8f6f8a31e
vector-database-ops is a skill published in the GitHub repository BagelHole/DevOps-Security-Agent-Skills (1,173 stars, last pushed 5mo ago), licensed MIT. It adds 54 tokens to every session and 2,150 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
bedrock
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
building-agents
Use when building or restructuring an LLM agent — provider adapter, tool calling, structured output, RAG, agent loop, eval gate, cost routing, tracing, MCP server — model-agnostic across OpenAI/Anthropic/Gemini/OSS so a model swap is a config change. NOT vector-store SQL alone (that is postgresdb) or service…
chroma-integration
Chroma local vector database setup and operations for development and production.
milvus-integration
Milvus distributed vector database configuration for large-scale RAG applications.
pinecone-integration
Pinecone vector database setup, configuration, and operations for RAG applications.