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Secrets & Setup Instructions

Give AI agents access to external services, API keys, and environment configuration without hardcoding credentials in task prompts.

Watchfire lets you provide agents with instructions for accessing external services — API keys, CLI tools, environment variables, and authentication details. These are injected into the agent's system prompt automatically.

How It Works

Secrets are stored in a plain Markdown file at:

.watchfire/secrets/instructions.md

When an agent session starts, Watchfire reads this file and injects its contents into the agent's system prompt under a "Secrets & Setup Instructions" section. The agent can then use this information to authenticate with services, set up environment variables, or use pre-configured CLI tools.

Setup

The secrets file is created automatically when you run watchfire init. Edit it with your project-specific instructions:

## CLI Tools

- Firebase CLI is authenticated. Use `firebase deploy` directly.
- AWS CLI is configured with the staging profile: `aws --profile staging`.

## Environment Variables

- `DATABASE_URL` is set in `.env.local` — do not commit this file.
- `STRIPE_TEST_KEY` is `sk_test_abc123` — use for all payment integration.

## API Keys

- OpenAI API key: `sk-proj-...` (org: my-org)
- Use the test Stripe key above for checkout features.

## Notes

- Always use the staging environment for testing.
- Never deploy to production without explicit approval.

Security

  • The .watchfire/ directory is gitignored by default — secrets never enter version control.
  • The sandbox blocks access to ~/.ssh, ~/.aws, ~/.gnupg, and .env files. Use the secrets file to tell agents what they need instead of relying on system-level credentials.
  • Secrets are only injected into the agent's system prompt at session start. They are not written to disk inside the worktree.

GUI Support

The Watchfire GUI includes a Secrets tab where you can edit instructions.md directly. Changes are saved automatically.

See also

  • Security — how secret storage fits into the broader threat model, alongside the sandbox, signature verification, and network exposure.

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