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Matlab Secure Credentials

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matlab
matlab-secure-credentials

Store, retrieve, and pass credentials securely in MATLAB using the built-in MATLAB Vault (setSecret, getSecret, importSecrets, secretID) instead of hardcoding. Handles connections to any authenticated service: REST APIs, databases, cloud storage (S3/Azure/GCS), SFTP, and others. Covers API keys, tokens, passwords, SSH passphrases, CI/batch/scheduled jobs, and "keep credentials out of code" requests. Does NOT cover third-party secret managers (HashiCorp Vault, AWS Secrets Manager), OS-level key management, or the connection/query logic itself.

Overview

Publishermatlab
Repositorymatlab-agentic-toolkit
Skill namematlab-secure-credentials
Stars
1.2K
Forks
138
Bundled files
1
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Matlab Secure Credentials 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/matlab/matlab-agentic-toolkit/tree/main/skills-catalog/matlab-data-import-and-analysis/matlab-secure-credentials
  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/matlab/matlab-agentic-toolkit.git /tmp/matlab-agentic-toolkit
mkdir -p .claude/skills
cp -r /tmp/matlab-agentic-toolkit/skills-catalog/matlab-data-import-and-analysis/matlab-secure-credentials .claude/skills/matlab-secure-credentials
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Matlab Secure Credentials 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 Matlab Secure Credentials 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 Matlab Secure Credentials 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.

Secure Credentials in MATLAB

Handle API keys, tokens, passwords, and passphrases with the MATLAB Vault, an encrypted store built into MATLAB, instead of hardcoding them. This skill covers the credential — storing it, retrieving it, and passing it into a connection to any authenticated service.

When to Use

  • Connecting to any authenticated service or connection from MATLAB — a REST API, database, cloud storage, SFTP/FTP server, message queue, or any other.
  • Any code that handles an API key, bearer token, password, or SSH key passphrase.
  • Writing CI / batch / scheduled MATLAB that needs credentials with no user present.
  • Refactoring credential-handling code, or persisting a config that includes a secret.
  • A request to "keep credentials out of code," "don't commit secrets," or "do this the secure way."

When NOT to Use

  • Writing the connection/query/transfer logic itself when no credential is involved (e.g. a public API, a local file), or cleaning/transforming/aggregating data once it is in a table or timetable — use the relevant data-import skill (e.g. matlab-analyze-data, matlab-use-database).
  • Integrating a third-party secret manager (HashiCorp Vault, AWS Secrets Manager, Azure Key Vault) — out of scope.
  • OS-level or non-MATLAB key management.

Decision Guide: Which Mechanism

Pick the mechanism by how the credential is supplied, not by habit.

SituationUseNot
Credential is stored and reused across sessionssetSecret once, then getSecret at useHardcoding; a hand-rolled config file
You need to load a set of credentials at once (interactive setup, or non-interactive / CI / headless / scheduled)importSecrets to populate the vault from a secrets file, then getSecret; or, in CI, getenv for a runner-injected valuesetSecret in a headless job — it is only supported interactively
A function accepts credentials from a callersecretID (a reference to the secret, not its value)Storing the value in a struct field or argument
Credential is genuinely a process environment variable — a CI-injected secret, or a cloud SDK convention like AWS_ACCESS_KEY_IDgetenv — this is correctDuplicating it into the vault for no reason

The rule is don't hardcode secrets — not "never use environment variables." getenv is the right tool when the secret is already a process env var; the vault is the right default for credentials you store.

Workflow

  1. Decide the mechanism using the Decision Guide above.
  2. Store or populate the credential:
    • Single secret, interactively: setSecret("MyApiToken") (MATLAB prompts for the value — never pass it as an argument; it takes only the name).
    • A set of secrets at once: importSecrets("secrets.env") loads names+values into the vault with no prompt — handy both for interactive setup and for non-interactive/CI.
  3. Retrieve at point of use with getSecret("MyApiToken"), or hand a secretID("MyApiToken") to APIs that accept one (they resolve it at call time, so the value never lives in a variable).
  4. Wire it into the connection — see Patterns below.
  5. Verify with isSecret("MyApiToken") before reading or removing, and confirm no secret value appears in the script, logs, or any saved file.

Key Functions

FunctionPurposeAvailable From
setSecretAdd a secret to the vault; interactive — prompts for the value, takes only the name (Overwrite=true to update)R2024a
getSecretRetrieve a secret value (returns a string scalar)R2024a
isSecretCheck whether a named secret existsR2024a
listSecretsList the names of stored secretsR2024a
removeSecretDelete a secret from the vault (there is no deleteSecret)R2024a
setSecretMetadataAttach metadata (e.g. an expiry date, owner) to a secretR2024a
getSecretMetadataRead a secret's metadata as a dictionaryR2024a
secretIDA reference object carrying a secret's name, not its value; accepted by weboptions and matlab.net.http.CredentialsR2025a
importSecretsLoad a set of secrets from a file into the vault (no prompt)R2026a

Patterns

Store once, retrieve at use

matlab
% One-time, at the MATLAB prompt (prompts for the value — do NOT type the secret in code):
setSecret("MyApiToken");

% In your script, read it only where needed:
token = getSecret("MyApiToken");

Optionally record metadata such as an expiry so callers can check freshness before use:

matlab
% Metadata values are stored in a dictionary; wrap each value in a cell:
setSecretMetadata("MyApiToken", dictionary("Expires", {datetime(2026,12,31)}));

md = getSecretMetadata("MyApiToken");
expiry = md{"Expires"};            % {} indexing returns the stored value
if expiry < datetime("today")
    error("MyApiToken expired on %s — rotate it with setSecret(...,Overwrite=true).", expiry);
end
token = getSecret("MyApiToken");

Rotate or update a secret

setSecret(...,Overwrite=true) replaces the value of an existing secret — the normal way to rotate a credential (replace it with a new value, e.g. periodically or after expiry). Without Overwrite, setSecret errors on a name that already exists.

matlab
setSecret("MyApiToken", Overwrite=true);   % prompts for the new value

REST call with a bearer token

Fetch the token from the vault and set it in the Authorization header.

matlab
token = getSecret("MyApiToken");
opts = weboptions( ...
    HeaderFields = ["Authorization", "Bearer " + token], ...
    ContentType  = "json");
data = webread("https://api.example.com/v1/data", opts);

For basic auth, hand weboptions a secretID so the value is resolved at request time and never sits in a variable:

matlab
username = getenv("API_USER");   % REPLACE: your service-account user name
opts = weboptions(Username=username, Password=secretID("MyApiPassword"));
data = webread("https://api.example.com/v1/data", opts);

For lower-level requests, matlab.net.http.Credentials also accepts a secretID:

matlab
cred = matlab.net.http.Credentials(Password=secretID("MyApiPassword"));

SFTP with a passphrase-protected key

matlab
keyFile = fullfile(userpath, "id_rsa");   % REPLACE: path to your private key
s = sftp("sftp.example.com", "reportuser", ...
    PrivateKeyFile       = keyFile, ...
    PrivateKeyPassphrase = getSecret("SftpKeyPassphrase"));
c = onCleanup(@() close(s));
localPaths  = mget(s, "/reports/nightly.csv", tempdir);
reportTable = readtable(localPaths{1});

Password auth instead of a key:

matlab
s = sftp("sftp.example.com", "reportuser", Password=getSecret("SftpPassword"));

Database connection

Store the password in the vault and read it at connect time. Keep host/port/database in code; keep the credential out.

matlab
conn = postgresql("svc-account", getSecret("PgPassword"), ...
    Server       = "db-prod-01", ...
    PortNumber   = 5432, ...
    DatabaseName = "analytics");
c = onCleanup(@() close(conn));
tables = sqlfind(conn, "");

For a reusable, shareable setup, save a data source once (via the Database Explorer app or databaseConnectionOptions + saveAsDataSource) and connect by name, supplying the password from the vault:

matlab
conn = postgresql("analyticsDataSource", "svc-account", getSecret("PgPassword"));

Cloud storage (S3 / Azure Blob / GCS)

MATLAB's file I/O (readtable, datastore, etc.) reads cloud URIs directly and picks up credentials from the SDK's environment variables. When you hold explicit keys, source them from the vault and set the env vars the SDK expects; when running on cloud infrastructure, prefer an attached IAM role and set nothing.

matlab
% Explicit keys held in the vault -> set the SDK env vars from getSecret:
setenv("AWS_ACCESS_KEY_ID",     getSecret("AwsAccessKeyId"));
setenv("AWS_SECRET_ACCESS_KEY", getSecret("AwsSecretAccessKey"));
setenv("AWS_DEFAULT_REGION",    "us-east-1");   % REPLACE: bucket region
T = readtable("s3://acme-data/prices/latest.csv");

If the code runs on an EC2 instance / role-enabled environment, skip the keys entirely — the IAM role supplies credentials and no secret needs to live anywhere.

Passing a credential into a function

When a function accepts credentials from its caller, carry a secretID (a reference), never the value. It resolves to the secret only where getSecret is called.

Build the config — it carries a reference, not the token:

matlab
config = struct( ...
    "BaseUrl", "https://api.example.com/v1", ...
    "Token",   secretID("MyApiToken"));

Use the config inside the function — resolve the secret only at the point of use:

matlab
function report = fetchReport(config)
    arguments
        config (1,1) struct
    end
    token = getSecret(config.Token.Name);
    opts  = weboptions(HeaderFields = ["Authorization", "Bearer " + token]);
    report = webread(config.BaseUrl + "/report", opts);
end

Load a set of secrets at once (setup or CI)

importSecrets loads names+values from a secrets file into the vault with no prompt — useful both for one-shot interactive setup and for headless jobs (where setSecret cannot be used, since it is interactive-only).

matlab
% secrets.env — a dotenv file (e.g. from CI secret storage), never committed — with lines like:
%   MyApiToken=abc123
%   PgPassword=hunter2
importSecrets("secrets.env");     % FileType="auto" (default) detects the dotenv format
token = getSecret("MyApiToken");

If the runner injects a credential directly as a process environment variable rather than a file, getenv("MY_TOKEN") is the correct read — no vault needed.

Conventions

  • Always: reach for the vault (setSecret/getSecret) as the default for credentials you store; guard removeSecret with isSecret.
  • Always: to load several secrets at once, or in any headless/CI context, use importSecrets, not setSecret.
  • Never: hardcode a secret in a .m file, or write a secret value into a struct or a log — pass a secretID reference instead.
  • Prefer: passing secretID(...) to weboptions/Credentials over materializing the value with getSecret when the API accepts a reference.
  • Env vars are fine when the credential is genuinely a process env var (CI secret, cloud SDK convention) — don't over-correct into the vault where it adds nothing.

Common Mistakes

MistakeWhy It's WrongCorrect Approach
Hardcoding a token/password in the .m fileLeaks into version control; rotates badlygetSecret("Name") from the vault
Writing a secret value into a struct field or argument a function passes aroundPlaintext secret leaves the vaultCarry a secretID reference; resolve with getSecret at use
setSecret in a CI/batch/scheduled jobIt is only supported interactively — no value can be enteredimportSecrets populates the vault non-interactively
Calling deleteSecretNo such function existsremoveSecret("Name"), guarded by isSecret
Saving a credential to a .mat file with save() and reading it back with load()Writes the plaintext secret to disk, unencrypted and often committedKeep the value in the vault; persist only a secretID reference and resolve with getSecret at use

Errors and What They Mean

Error identifierWhen it occursFix
MATLAB:authnz:secretapis:KeyAlreadyExists — "A secret named 'X' already exists. Set 'Overwrite' to true…"setSecret("X") when X is already in the vaultTo rotate/update, call setSecret("X", Overwrite=true); otherwise pick a new name
MATLAB:authnz:secretapis:SecretValueNotFound — "No secret value found for secret name 'X'…"getSecret("X") when X was never stored (or the name is misspelled/wrong case)Store it first (setSecret/importSecrets); guard reads with isSecret("X"). Secret names are case-sensitive
MATLAB:authnz:secretapis:RemoveSecretFailed — "…No secret found for secret name 'X'."removeSecret("X") when X is not in the vaultGuard with if isSecret("X"); removeSecret("X"); end

Copyright 2026 The MathWorks, Inc.


Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Matlab Secure Credentials AI skill do?

Store, retrieve, and pass credentials securely in MATLAB using the built-in MATLAB Vault (setSecret, getSecret, importSecrets, secretID) instead of hardcoding. Handles connections to any authenticated service: REST APIs, databases, cloud storage (S3/Azure/GCS), SFTP, and others. Covers API keys, tokens, passwords, SSH passphrases, CI/batch/scheduled jobs, and "keep credentials out of code" requests. Does NOT cover third-party secret managers (HashiCorp Vault, AWS Secrets Manager), OS-level key management, or the connection/query logic itself.

Why use Matlab Secure Credentials on TypingMind?

Because you install it once and use it with any model. Matlab Secure Credentials 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 Matlab Secure Credentials in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/matlab/matlab-agentic-toolkit/tree/main/skills-catalog/matlab-data-import-and-analysis/matlab-secure-credentials. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Matlab Secure Credentials?

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 Matlab Secure Credentials?

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

Is the Matlab Secure Credentials AI skill free?

It is published on GitHub by matlab. Check the repository for licensing terms. 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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