Vault Secrets logo

Vault Secrets

Organization
vstorm-co
vault-secrets

Store, read or rotate a credential — a provider API key, a channel bot token, an MCP token, a third-party service key — or add a new secret kind. Use whenever a change touches a credential at rest, whenever something needs to be encrypted, and whenever reviewing code that could leak a plaintext key into a response, a log line or an audit entry. Everything seals through app/core/vault.py; there is deliberately no second mechanism.

Overview

Publishervstorm-co
Repositoryagenticos
Skill namevault-secrets
Stars
137
Forks
27
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 vstorm-co on GitHub. Read the source before you install it.

Installation

Install the Vault Secrets 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/vstorm-co/agenticos/tree/main/.claude/skills/vault-secrets
  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/vstorm-co/agenticos.git /tmp/agenticos
mkdir -p .claude/skills
cp -r /tmp/agenticos/.claude/skills/vault-secrets .claude/skills/vault-secrets
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Vault Secrets 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 Vault Secrets 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 Vault Secrets 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.

The vault — one mechanism, bound to an owner

Read docs/secrets.md. The module docstrings in app/core/vault.py and app/core/secret_kinds.py carry the reasoning.

The rule

Every provider key, channel bot token, MCP credential and third-party API key seals through app/core/vault.py. There is no second mechanism, and adding one is the mistake this design exists to prevent.

CHANNEL_ENCRYPTION_KEY and the deployment-wide Fernet keys are gone, removed before the migration chain was squashed into 0001_baseline. If you see them referenced anywhere — code, docs, a skill, an .env example — that reference is stale. Three mechanisms used to hold secrets and only one bound a ciphertext to its owner; a Slack token could be copied from one organization's row into another's and it decrypted.

Envelope, and what it buys

Each secret is sealed with its own random data key. That data key is sealed with a key derived from the master key and the scope that owns the secret.

  • A ciphertext cannot move between owners. A row copied from org A into org B fails to unwrap. Tenant isolation here is cryptographic, not a WHERE clause somebody might forget.
  • The master key is rotatable. It never encrypts payloads, only data keys, so rotation re-wraps one small blob per secret. key_version records which master key sealed an envelope — that column is what makes a staged rotation possible.

Scope is an organization, or the member for a personal MCP connection (which has no organization, and whose owner may belong to several).

VAULT_MASTER_KEY, falling back to SECRET_KEY so a fresh checkout runs. An environment validator refuses the default SECRET_KEY outside development.

Kinds

A secret is not always a string, and the kind decides which fields exist: none, api_key, azure_openai, aws_credentials, gcp_service_account.

StorableSecret is what a person can save; SecretValue adds none, which the runtime can hold but nobody can store. That difference keeps "a secret with no value" out of the API schema. The vault refuses to seal an empty value.

Adding a kind means: a new model in secret_kinds.py, a branch in model_resolver._build_provider if a provider needs it, a Builder form field, and a migration only if a stored shape changes. Validate on the way in — the failure mode of a malformed service account is an auth error hours later with nothing pointing back at the paste that caused it.

Four things that must stay true

  1. No API response returns a plaintext. There is no endpoint for it. Plaintext leaves the vault only through the few OrganizationSecretService readers its module docstring lists - resolve_for_bindings for the runner, and narrow ones that hand one value straight to a provider (listing_key, transcription_key, oauth_app). None returns it to a caller who could keep it. Do not add another without that shape.
  2. No log line or audit entry contains one. Every secret-bearing field is a SecretStr, which is what makes the carrying dataclasses mask themselves in a repr — the usual way a key escapes.
  3. No spec carries one. A binding stores secret_id; specs are exported to clients' git repositories.
  4. A capability never learns where its credential came from, and the model never sees it.

tests/api/test_no_secret_escapes.py pins these. If it fails, the leak is real.

Declaring a need, not an instance

A capability declares a SecretRequirement — a kind. Code says "I need an API key"; a binding's secret_id says which one. needs_secret(config) is consulted by publish validation and by the build, so the two cannot disagree.

A secret deleted after publish makes the run refuse, not degrade: a capability whose whole job is calling an authenticated API does not do half of it, and a run that quietly dropped the call would answer as if the API had said no.

Access

secrets:view / secrets:edit (resource permissions, so they carry a scope), mcp:manage, connections:manage. A secret can be shared to one member or agent with a grant. See the permissions-rbac skill.

Test

Cross-tenant unwrap must fail. That is an integration test — a mock cannot tell you the binding holds.

Frequently asked questions

What does the Vault Secrets AI skill do?

Store, read or rotate a credential — a provider API key, a channel bot token, an MCP token, a third-party service key — or add a new secret kind. Use whenever a change touches a credential at rest, whenever something needs to be encrypted, and whenever reviewing code that could leak a plaintext key into a response, a log line or an audit entry. Everything seals through app/core/vault.py; there is deliberately no second mechanism.

Why use Vault Secrets on TypingMind?

Because you install it once and use it with any model. Vault Secrets 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 Vault Secrets in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vstorm-co/agenticos/tree/main/.claude/skills/vault-secrets. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Vault Secrets?

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 Vault Secrets?

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

Is the Vault Secrets AI skill free?

Yes. It is published on GitHub by vstorm-co 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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