Datris Platform Oss — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited Datris Platform Oss (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
datris.ai · Documentation · MCP Registry · PyPI
Ingest, validate, transform, store, and retrieve your data — whether you're an AI agent talking through MCP or a developer writing config. One platform for both.
You only need Docker. This pulls pre-built images and runtime files, seeds a .env, and starts the stack into ./datris — no git checkout required:
curl -fsSL https://get.datris.ai/install.sh | shThe install.sh installer is a POSIX shell script (macOS/Linux). On Windows, run it from WSL2 or Git Bash, or use the single-file Compose option below, which works natively in PowerShell.<details> <summary>Single file, no installer (works on Windows)</summary>
A fully self-contained Compose file — the init scripts and config are inlined, so nothing else is needed (requires Docker Compose ≥ 2.23):
# macOS / Linux
curl -O https://get.datris.ai/docker-compose.standalone.yml
ANTHROPIC_API_KEY=sk-ant-... docker compose -f docker-compose.standalone.yml up -d# Windows (PowerShell) — use curl.exe, and set the key with $env:
curl.exe -O https://get.datris.ai/docker-compose.standalone.yml
$env:ANTHROPIC_API_KEY="sk-ant-..."
docker compose -f docker-compose.standalone.yml up -d</details>
<details> <summary>From a git clone</summary>
git clone https://github.com/datris/datris-platform-oss.git
cd datris-platform-oss
cp .env.example .env # Add your ANTHROPIC_API_KEY and/or OPENAI_API_KEY
docker compose up -d</details>
UI: http://localhost:4200 · API: http://localhost:8080
Add to your MCP client config (Claude Desktop, Claude Code, Cursor, etc.). With the Docker stack running, the npx mcp-remote stdio bridge connects to the bundled MCP server on port 3000 — your client appears in the Datris UI Agent Monitor tab with live tool-call streaming:
{
"mcpServers": {
"datris": {
"command": "npx",
"args": ["-y", "mcp-remote", "http://localhost:3000/sse", "--transport", "sse-only"]
}
}
}Paste-and-go for the default local setup — no API key required when USE_API_KEYS=false (the OSS default). If your instance enables auth (USE_API_KEYS=true or hosted/multi-tenant), append "--header", "x-api-key:<your-key>" to the args array. The Configuration → Connect Your Agent page generates the snippet for you and adds the header automatically when you paste your key.
Requires Node.js on your PATH (brew install node). For a stdio alternative without Docker, or full Claude Desktop / Claude Code / Cursor walkthroughs, see Configuring Claude.
brew tap datris/tap
brew install datris
datris ingest data.csv --dest postgres
datris ingest sales.csv --ai-validate "prices > 0" --ai-transform "convert dates to YYYY/MM/DD"
datris query "SELECT * FROM sales"
datris search "quarterly revenue" --store pgvector
datris tap create "Fetch S&P 500 daily prices from yfinance" --pipeline stocks
datris tapsSource (File Upload / MinIO Event / Database Pull / Kafka)
→ Preprocessor (optional REST endpoint)
→ Data Quality (AI rules, header validation, schema validation)
→ Transformation (AI transformation, destination schema)
→ Destinations (in parallel):
PostgreSQL, MongoDB, MinIO (Parquet/ORC), Kafka, ActiveMQ,
REST Endpoint, Qdrant, Weaviate, Milvus, Chroma, pgvector
→ Notifications (ActiveMQ topic)| Feature | Description |
|---|---|
| MCP Server | 47 tools for AI agents — pipeline CRUD, upload, query, search, profiling, taps |
| AI Data Quality | Plain English validation rules — AI generates and runs a validation script |
| AI Transformation | Plain English transformations — AI generates and runs a transformation script |
| AI Schema Generation | Upload a file, get a complete pipeline config |
| AI Data Profiling | Upload a file, get statistics + suggested validation rules |
| AI Error Explanation | Job failures explained in plain English |
| Natural Language Query | Ask questions in English, get SQL results |
| RAG Pipeline | Chunk, embed, and search across 5 vector databases |
CSV, JSON, XML, Excel, PDF, Word (DOCX), plain text
Anthropic Claude (Sonnet 4.6 default, Opus 4.8 for CodeGen) · OpenAI (GPT-5.5) · Ollama (local models, optional). Embeddings via TEI sidecar (BAAI/bge-m3) when using Anthropic, or text-embedding-3-small when using OpenAI.
| Service | Purpose |
|---|---|
| MinIO | S3-compatible object store for file staging and data output |
| PostgreSQL | Default structured destination, also hosts pgvector for RAG |
| MongoDB | Configuration store, job status tracking, metadata |
| ActiveMQ | File notification queue, pipeline event notifications |
| HashiCorp Vault | Secrets management (database credentials, API keys) |
| TEI | Text Embeddings Inference sidecar (BAAI/bge-m3) for vector embeddings without an OpenAI key |
| Apache Kafka | Optional streaming source and destination |
| Apache Spark | Local Spark for writing Parquet/ORC to MinIO |
Full documentation at docs.datris.ai or locally at docs/.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.