Claude-skill-registry doppler-secret-validation
Validate and test Doppler secrets. TRIGGERS - add to Doppler, store secret, validate token, test credentials.
git clone https://github.com/majiayu000/claude-skill-registry
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/doppler-secret-validation" ~/.claude/skills/majiayu000-claude-skill-registry-doppler-secret-validation && rm -rf "$T"
skills/data/doppler-secret-validation/SKILL.mdDoppler Secret Validation
Overview
Workflow for securely adding, validating, and testing API tokens and credentials in Doppler secrets management.
When to Use This Skill
Use this skill when:
- User provides API tokens or credentials (PyPI, GitHub, AWS, etc.)
- User mentions "add to Doppler", "store secret", "validate token"
- User wants to test authentication before production use
- User needs to verify secret storage and retrieval
Workflow
Step 1: Test Token Format (Before Adding to Doppler)
Before storing in Doppler, validate token format:
# Check token format, length, prefix python3 -c "token = 'TOKEN_VALUE'; print(f'Prefix: {token[:20]}...'); print(f'Length: {len(token)}')"
Common token formats:
- PyPI:
(179 chars)pypi-... - GitHub:
(40+ chars)ghp_... - AWS: 20-char access key + 40-char secret
Step 2: Add Secret to Doppler
doppler secrets set SECRET_NAME="value" --project PROJECT --config CONFIG
Example:
doppler secrets set PYPI_TOKEN="pypi-AgEI..." \ --project claude-config --config prd
Important: CLI doesn't support
--note. Add notes via dashboard:
- https://dashboard.doppler.com
- Navigate: PROJECT → CONFIG → SECRET_NAME
- Edit → Add descriptive note
Step 3: Validate Storage
Use the bundled validation script:
/usr/bin/env bash << 'VALIDATE_EOF' cd ${CLAUDE_PLUGIN_ROOT}/skills/doppler-secret-validation uv run scripts/validate_secret.py \ --project PROJECT \ --config CONFIG \ --secret SECRET_NAME VALIDATE_EOF
This validates:
- Secret exists in Doppler
- Secret retrieval works
- Environment injection works via
doppler run
Example:
uv run scripts/validate_secret.py \ --project claude-config \ --config prd \ --secret PYPI_TOKEN
Step 4: Test API Authentication
Use the bundled auth test script (adapt test_api_authentication() for specific API):
/usr/bin/env bash << 'CONFIG_EOF' cd ${CLAUDE_PLUGIN_ROOT}/skills/doppler-secret-validation doppler run --project PROJECT --config CONFIG -- \ uv run scripts/test_api_auth.py \ --secret SECRET_NAME \ --api-url API_ENDPOINT CONFIG_EOF
Example (PyPI):
doppler run --project claude-config --config prd -- \ uv run scripts/test_api_auth.py \ --secret PYPI_TOKEN \ --api-url https://upload.pypi.org/legacy/
Step 5: Document Usage
After validation, document the usage pattern for the user:
/usr/bin/env bash << 'CONFIG_EOF_2' # Pattern 1: Doppler run (recommended for CI/scripts) doppler run --project PROJECT --config CONFIG -- COMMAND # Pattern 2: Manual export (for troubleshooting) export SECRET_NAME=$(doppler secrets get SECRET_NAME \ --project PROJECT --config CONFIG --plain) CONFIG_EOF_2
Step 5b: mise [env] Integration (Recommended for Local Development)
For multi-account GitHub setups or per-directory credential needs, integrate Doppler secrets with mise
[env]:
# .mise.toml [env] # Option A: Direct Doppler CLI fetch (slower, always fresh) GH_TOKEN = "{{ exec(command='doppler secrets get GH_TOKEN --project myproject --config prd --plain') }}" GITHUB_TOKEN = "{{ exec(command='doppler secrets get GH_TOKEN --project myproject --config prd --plain') }}" # Option B: Cache for performance (1 hour cache) GH_TOKEN = "{{ cache(key='gh_token', duration='1h', run='doppler secrets get GH_TOKEN --project myproject --config prd --plain') }}" GITHUB_TOKEN = "{{ cache(key='gh_token', duration='1h', run='doppler secrets get GH_TOKEN --project myproject --config prd --plain') }}"
Note: Set BOTH
GH_TOKEN and GITHUB_TOKEN - different tools check different variable names (gh CLI vs npm scripts).
Why mise [env]? Doppler
doppler run is session-scoped; mise [env] provides directory-scoped credentials that persist across commands.
See
skill for complete patterns.mise-configuration
Common Patterns
Multiple Configs (dev, stg, prd)
Add secret to multiple environments:
# Production doppler secrets set TOKEN="prod-value" --project foo --config prd # Development doppler secrets set TOKEN="dev-value" --project foo --config dev
Verify Secret Across Configs
/usr/bin/env bash << 'CONFIG_EOF_3' for config in dev stg prd; do echo "=== $config ===" doppler secrets get TOKEN --project foo --config $config --plain | head -c 20 echo "..." done CONFIG_EOF_3
Security Guidelines
- Never log full secrets: Use
masking${SECRET:0:20}... - Prefer doppler run: Scopes secrets to single command
- Use --plain only for piping: Human-readable view masks secrets
- Separate configs per environment: dev/stg/prd isolation
Bundled Resources
- scripts/validate_secret.py - Complete validation suite (existence, retrieval, injection)
- scripts/test_api_auth.py - Template for API authentication testing
- references/doppler-patterns.md - Common CLI patterns and examples
Reference
- Doppler docs: https://docs.doppler.com/docs
- CLI install:
brew install dopplerhq/cli/doppler - See doppler-patterns.md for comprehensive patterns