doppler-secret-validation

name	doppler-secret-validation
description	Validate and test Doppler secrets. TRIGGERS - add to Doppler, store secret, validate token, test credentials.
allowed-tools	Read, Bash

Doppler Secret Validation

Self-Evolving Skill: This skill improves through use. If instructions are wrong, parameters drifted, or a workaround was needed — fix this file immediately, don't defer. Only update for real, reproducible issues.

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: pypi-... (179 chars)
• GitHub: ghp_... (40+ chars)
• 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:

1. https://dashboard.doppler.com
2. Navigate: PROJECT → CONFIG → SECRET_NAME
3. Edit → Add descriptive note

Step 3: Validate Storage

Use the bundled validation script:

/usr/bin/env bash << 'VALIDATE_EOF'
ROOT="$(cc-plugin-root devops-tools)"
cd "$ROOT/skills/doppler-secret-validation"
uv run scripts/validate_secret.py \
  --project PROJECT \
  --config CONFIG \
  --secret SECRET_NAME
VALIDATE_EOF

This validates:

1. Secret exists in Doppler
2. Secret retrieval works
3. 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'
ROOT="$(cc-plugin-root devops-tools)"
doppler run --project PROJECT --config CONFIG -- \
  "$ROOT/skills/doppler-secret-validation/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 mise-configuration skill for complete patterns.

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

1. Never log full secrets: Use ${SECRET:0:20}... masking
2. Prefer doppler run: Scopes secrets to single command
3. Use --plain only for piping: Human-readable view masks secrets
4. 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

---

Troubleshooting

Issue	Cause	Solution
Secret not found	Wrong project/config specified	Verify with doppler secrets ls --project X --config
Auth test fails with 401	Token expired or invalid	Regenerate token, re-add to Doppler
doppler run hangs	CLI waiting for input	Add --no-interactive flag
Token prefix mismatch	Wrong token type used	Check expected format (pypi-, ghp-, AKIA, etc.)
Validation script not found	Wrong directory context	Use cc-plugin-root devops-tools to find the plugin root, then cd to the skill directory
Secret retrieval empty	Secret name typo	List secrets: doppler secrets ls --project X
mise cache stale	Duration expired	Clear cache or reduce duration setting
Multiple configs confusion	Secrets differ across envs	Use explicit --config flag for each command

Post-Execution Reflection

After this skill completes, reflect before closing the task:

0. Locate yourself. — Find this SKILL.md's canonical path before editing.
1. What failed? — Fix the instruction that caused it.
2. What worked better than expected? — Promote to recommended practice.
3. What drifted? — Fix any script, reference, or dependency that no longer matches reality.
4. Log it. — Evolution-log entry with trigger, fix, and evidence.

Do NOT defer. The next invocation inherits whatever you leave behind.