eden-ai — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited eden-ai (Agent Skill) and scored it 96/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 1 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 1 flagged
The text {match} tells the agent to skip the normal "ask the user first" gate. Used adversarially it removes the human-in-the-loop check before destructive or sensitive actions, turning a normally-gated agent into a fire-and-forget executor.
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.
Eden AI is an AI API hub that allows users to access and compare different AI models from various providers through a single platform. It's used by developers and businesses looking to integrate AI capabilities into their applications without dealing with the complexities of managing multiple AI APIs directly.
Official docs: https://docs.edenai.co/
This skill uses the Membrane CLI to interact with Eden AI. Membrane handles authentication and credentials refresh automatically — so you can focus on the integration logic rather than auth plumbing.
Install the Membrane CLI so you can run membrane from the terminal:
npm install -g @membranehq/cli@latestmembrane login --tenant --clientName=<agentType>This will either open a browser for authentication or print an authorization URL to the console, depending on whether interactive mode is available.
Headless environments: The command will print an authorization URL. Ask the user to open it in a browser. When they see a code after completing login, finish with:
membrane login complete <code>Add --json to any command for machine-readable JSON output.
Agent Types : claude, openclaw, codex, warp, windsurf, etc. Those will be used to adjust tooling to be used best with your harness
Use membrane connection ensure to find or create a connection by app URL or domain:
membrane connection ensure "https://www.edenai.co/" --jsonThe user completes authentication in the browser. The output contains the new connection id.
This is the fastest way to get a connection. The URL is normalized to a domain and matched against known apps. If no app is found, one is created and a connector is built automatically.
If the returned connection has state: "READY", skip to Step 2.
#### 1b. Wait for the connection to be ready
If the connection is in BUILDING state, poll until it's ready:
npx @membranehq/cli connection get <id> --wait --jsonThe --wait flag long-polls (up to --timeout seconds, default 30) until the state changes. Keep polling until state is no longer BUILDING.
The resulting state tells you what to do next:
clientAction object describes the required action:clientAction.type — the kind of action needed:"connect" — user needs to authenticate (OAuth, API key, etc.). This covers initial authentication and re-authentication for disconnected connections."provide-input" — more information is needed (e.g. which app to connect to).clientAction.description — human-readable explanation of what's needed.clientAction.uiUrl (optional) — URL to a pre-built UI where the user can complete the action. Show this to the user when present.clientAction.agentInstructions (optional) — instructions for the AI agent on how to proceed programmatically.After the user completes the action (e.g. authenticates in the browser), poll again with membrane connection get <id> --json to check if the state moved to READY.
error field for details.Search using a natural language description of what you want to do:
membrane action list --connectionId=CONNECTION_ID --intent "QUERY" --limit 10 --jsonYou should always search for actions in the context of a specific connection.
Each result includes id, name, description, inputSchema (what parameters the action accepts), and outputSchema (what it returns).
| Name | Key | Description |
|---|---|---|
| Detect Emotions in Text | detect-emotions | Detect emotions expressed in text (joy, sadness, anger, fear, etc.). |
| Parse Resume | parse-resume | Extract structured information from resume/CV documents. |
| Detect Explicit Content in Image | detect-explicit-content | Detect explicit, adult, or inappropriate content in images. |
| Answer Question About Image | answer-image-question | Ask questions about the content of an image and get AI-generated answers. |
| Detect Objects in Image | detect-objects-in-image | Detect and identify objects within an image. |
| Generate Code | generate-code | Generate code based on natural language instructions. |
| Check Spelling | check-spelling | Check text for spelling errors and get correction suggestions. |
| Extract Keywords | extract-keywords | Extract important keywords and key phrases from text. |
| Moderate Text Content | moderate-text | Analyze text for harmful, inappropriate, or policy-violating content. |
| Extract Text from Image (OCR) | extract-text-from-image | Extract text from images using optical character recognition (OCR). |
| Text to Speech | text-to-speech | Convert text to spoken audio using AI text-to-speech providers. |
| Generate Image | generate-image | Generate images from text descriptions using AI image generation providers. |
| Generate Text Embeddings | generate-embeddings | Generate vector embeddings for text, useful for semantic search and similarity comparisons. |
| Detect Language | detect-language | Detect the language of the provided text. |
| Translate Text | translate-text | Translate text from one language to another using AI translation providers. |
| Extract Named Entities | extract-entities | Extract named entities (people, organizations, locations, etc.) from text. |
| Analyze Sentiment | analyze-sentiment | Analyze the sentiment of text to determine if it's positive, negative, or neutral. |
| Summarize Text | summarize-text | Generate a summary of the provided text using AI providers. |
| LLM Chat (OpenAI Compatible) | llm-chat | Send messages to an LLM using the OpenAI-compatible API format. |
| Chat | chat | Send a message to an AI chatbot and get a response. |
membrane action run <actionId> --connectionId=CONNECTION_ID --jsonTo pass JSON parameters:
membrane action run <actionId> --connectionId=CONNECTION_ID --input '{"key": "value"}' --jsonThe result is in the output field of the response.
When the available actions don't cover your use case, you can send requests directly to the Eden AI API through Membrane's proxy. Membrane automatically appends the base URL to the path you provide and injects the correct authentication headers — including transparent credential refresh if they expire.
membrane request CONNECTION_ID /path/to/endpointCommon options:
| Flag | Description |
|---|---|
-X, --method | HTTP method (GET, POST, PUT, PATCH, DELETE). Defaults to GET |
-H, --header | Add a request header (repeatable), e.g. -H "Accept: application/json" |
-d, --data | Request body (string) |
--json | Shorthand to send a JSON body and set Content-Type: application/json |
--rawData | Send the body as-is without any processing |
--query | Query-string parameter (repeatable), e.g. --query "limit=10" |
--pathParam | Path parameter (repeatable), e.g. --pathParam "id=123" |
membrane action list --intent=QUERY (replace QUERY with your intent) to find existing actions before writing custom API calls. Pre-built actions handle pagination, field mapping, and edge cases that raw API calls miss.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.