Roboflow Cloud Storage logo

Roboflow Cloud Storage

Organization
roboflow
roboflow-cloud-storage

Use when connecting cloud storage (AWS S3 / S3-compatible or Google Cloud Storage) to Roboflow to mirror images into a workspace — creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs.

Overview

Publisherroboflow
Repositorycomputer-vision-skills
Skill nameroboflow-cloud-storage
Stars
50
Forks
11
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by roboflow on GitHub. Read the source before you install it.

Installation

Install the Roboflow Cloud Storage AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/roboflow/computer-vision-skills/tree/main/skills/roboflow-cloud-storage
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/roboflow/computer-vision-skills.git /tmp/computer-vision-skills
mkdir -p .claude/skills
cp -r /tmp/computer-vision-skills/skills/roboflow-cloud-storage .claude/skills/roboflow-cloud-storage
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Roboflow Cloud Storage in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Roboflow Cloud Storage on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Roboflow Cloud Storage is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

For agents: This skill is authored in roboflow/computer-vision-skills. If your client already has it loaded as a skill (for example from a Roboflow plugin), use that copy. The skill://roboflow/<name>/SKILL.md MCP resources serve the same skills for clients that don't have them loaded.

Cloud Storage (Datasources & Credentials)

Mirror a cloud bucket's contents into a Roboflow workspace so new objects flow in automatically. Two pieces work together:

  • Credential — the secret Roboflow uses to reach the bucket (stored, masked server-side, reusable across datasources).
  • Datasource — a bucket-mirror configuration: which bucket (via a credential), which objects (glob rules), and how they sync. "Datasource" is the user-facing name; the platform stores it as a bucket-mirror config.

Imports land at the workspace level. The datasource API cannot target a specific project/dataset today.

Only want to run a model/Workflow over the files and collect the outputs, without importing anything into Roboflow? That is batch processing — see the roboflow-batch-processing skill. Mirroring composes with it too: imported files become Asset Library images, and bulk predictions over them run as Asset Library batch processing jobs (same skill).

Fast path — one call

connect_cloud_storage does the whole flow in one shot: (optionally) create a credential, create the datasource, validate access, and start the first mirror run. Prefer it for new setups.

  • Reuse a credential: pass credential_id (from credentials_list).
  • Create a new credential: pass credential_name + credential_type (the secret is collected securely out-of-band — see Security).
  • Describe the datasource so it can be created first: bucket_type (s3/gcs), bucket_name, region, plus optional mirror_configs glob rules (see Datasources). The datasource must exist before anything can be validated or triggered — there is nothing to mirror without it.
  • trigger (default true) starts a mirror run only if validation passes; triggering consumes credits.
  • Returns {credentialId, credentialCreated, datasourceId, validation, triggered, batchIds?}. If a new credential was created but a later step failed, you get status: "partial" with the ids so you can clean up with credentials_delete / datasource_delete.

Credentials

Security — secrets never enter the chat

The secret (keys, service-account JSON, password) is collected out-of-band via MCP elicitation, never as a tool argument and never in the transcript or model context. credentials_list returns only masked, non-secret fields. If your client can't elicit securely, the tool returns manual_entry_required with a URL to create the credential in the app (app.roboflow.com/<workspace>/settings/datasources), then call credentials_list to get its id.

Credential types

credential_typeProviderSecret fields collected
IAMAWS S3accessKeyId, secretAccessKey, sessionToken?
AssumeRoleAWS S3roleArn, externalId, sessionName?
AssumeRoleWithWebIdentityAWS S3roleArn, webIdentityAudience, webIdentityRoleArn, externalId, sessionName?
gcsGoogle Cloud StorageprojectId, credentialFile (full service-account JSON)
usernamePasswordS3-compatibleusername, password
apiKeyS3-compatibleapiKey

AWS PrivateLink (require_vpce) requires an AssumeRoleWithWebIdentity credential; the server rejects the datasource otherwise.

Datasources

Bucket

  • bucket_type: s3 or gcs
  • bucket_name (no scheme/path), region (e.g. us-east-1)
  • endpoint: custom URL for S3-compatible providers (S3 only)

Mirror rules (mirror_configs)

Optional list; omit to mirror the whole bucket with default settings. Each rule selects a subset and controls sync behavior:

  • glob_patterns (list) — the key field. Selects objects, e.g. ["images/**/*.jpg", "batch-*/**"]. Set it to avoid importing unwanted files or to split a bucket into multiple rules. Omit only when you truly want everything.
  • glob_file_path — alternative: path to a manifest file in the bucket whose lines list object paths to import.
  • id — rule id (auto-generated; pass an existing id on update to edit in place).
  • settings (omitted keys use server defaults):
    • removeOrphanedSourcesWhenDisappeared (default true) — delete mirrored images when the source object is removed.
    • namingStrategy (fullPath|fileName|eTag|metadata, default fullPath); namingStrategyMetadataKey required when metadata.
    • updateImageWhenNewer (true) / updateImageStrategy (overwrite).
    • updateMetadataWhenNewer (true) / updateMetadataStrategy (mergeBucketWins default; also overwrite|merge|mergeUserWins| untilFirstChange|append).
    • runScheduledEvery (hours, default 0 = off) — >0 enables a recurring mirror (fixed 24h cadence today; only on/off is honored).

Lifecycle & validation

  1. Create (datasource_create, or connect_cloud_storage). Nothing is mirrored yet.
  2. Validate (datasource_validate) — checks listFiles, headFile, getFile, and per-rule headGlobFile; returns {checks, errors}. Run it to self-diagnose a bad credential/bucket before triggering.
  3. Trigger (datasource_trigger) — starts the mirror job; consumes compute/storage credits. Returns {batchIds}.
  4. Poll (datasource_job_get with a batchId) — status (running/completed/failed), per-stage counters, errors, timestamps.

Update (datasource_update): only fields you pass change; to change the bucket pass bucket_type + bucket_name + region together. Delete (datasource_delete): removes the config, not already-mirrored images. Deleting a credential still referenced by a datasource makes its runs fail.

MCP Tools Available

ToolPurpose
connect_cloud_storageEnd-to-end: credential + datasource + validate + first run
credentials_listList masked cloud-storage credentials
get_aws_storage_credential_setup_linkGet a Roboflow app link for AWS storage credential setup; enter secrets in the app
credentials_deleteDelete a credential
datasources_list / datasource_getList / inspect datasource configs
datasource_create / datasource_update / datasource_deleteManage a datasource
datasource_validateCheck bucket access before a run
datasource_triggerStart a mirror run (consumes credits)
datasource_job_getPoll a mirror run's status and counters

Related Pages

  • skill://roboflow/roboflow-data-management/SKILL.md — what to do with the images once mirrored (tags, splits, versions, search)
  • skill://roboflow/roboflow-product-navigation/SKILL.md — the app's settings/datasources page

Frequently asked questions

What does the Roboflow Cloud Storage AI skill do?

Use when connecting cloud storage (AWS S3 / S3-compatible or Google Cloud Storage) to Roboflow to mirror images into a workspace — creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs.

Why use Roboflow Cloud Storage on TypingMind?

Because you install it once and use it with any model. Roboflow Cloud Storage is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Roboflow Cloud Storage in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/roboflow/computer-vision-skills/tree/main/skills/roboflow-cloud-storage. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Roboflow Cloud Storage?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Roboflow Cloud Storage?

As many as you like. As long as a model supports skills, you can use Roboflow Cloud Storage with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Roboflow Cloud Storage AI skill free?

Yes. It is published on GitHub by roboflow under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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