# PDD Internal Onboarding

> PDD employees use Infisical for centralized secrets management instead of local .env files for API keys.

- Skill: `tools-only/pdd-internal-onboarding` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/pdd-internal-onboarding`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/pdd-internal-onboarding/raw
- Safety review: pending (external: skill-scanner WARNING, skillspector CAUTION)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/pdd-internal-onboarding

---

# PDD Internal Onboarding

For PDD employees with access to shared infrastructure.

> **Note**: This guide supplements the main [ONBOARDING.md](ONBOARDING.md). Complete steps 1-6 from that guide first, then return here for secrets management setup.

## Infisical Setup

PDD employees use Infisical for centralized secrets management instead of local `.env` files for API keys.

### 1. Accept the Invitation

Check your email for an Infisical invitation from the PDD team. Accept it and verify you can see the project in your Infisical dashboard.

### 2. Install the Infisical CLI

Choose the command for your operating system:

**macOS:**
```bash
brew install infisical
```

**Windows (PowerShell):**
```powershell
winget install infisical
```

**Linux:**
```bash
curl -1sLf 'https://artifacts-cli.infisical.com/setup.deb.sh' | sudo -E bash
```

### 3. Authenticate and Link Your Repository

From the root of the `pdd` repository:

```bash
# Log in to your Infisical account
infisical login

# Link your local repo to the Infisical project
infisical init
```

## Local Configuration

Even with Infisical, you need one local setting in your `.env` file:

```bash
# Add to .env (file path can't be stored in Infisical)
VERTEX_CREDENTIALS=/path/to/service-account.json
```

**To get the service account JSON:**
- Ask your team lead for access to the shared GCP service account, or
- Create your own following the Vertex AI setup steps in ONBOARDING.md

## Running Commands

Always use the `infisical run --` prefix to inject secrets:

```bash
# Run tests
infisical run -- make test

# Generate code
infisical run -- pdd generate module_name

# Sync a module
infisical run -- pdd sync module_name
```

## Cloud Testing

The full test suite takes 40+ minutes locally (sequential, single machine). Cloud testing runs the same suite across 64 spot VMs in parallel and finishes in ~7 minutes — while keeping your laptop free.

### Daily Use

```bash
# Smart build + run: auto-rebuilds Docker image if deps changed
make cloud-test

# Skip image rebuild (typical workflow once image is built)
make cloud-test-quick
```

### One-Time Setup

**Prerequisites:** Docker and `gcloud` CLI authenticated to `prompt-driven-development-stg`.

```bash
# Provision GCP infrastructure (Artifact Registry, GCS bucket, IAM)
make cloud-test-setup

# Build and push the Docker image
make cloud-test-build
make cloud-test-push
```

After initial setup, `make cloud-test` handles image rebuilds automatically when dependencies change.

### Results

A summary prints to the terminal when the job finishes. The full report (pass/fail per shard, failure logs) is saved to `test-results/cloud-batch-results.md`.

## What's in Infisical

The following secrets are managed centrally:

| Category | Variables |
|----------|-----------|
| LLM API Keys | `OPENAI_API_KEY`, `ANTHROPIC_API_KEY`, `GOOGLE_API_KEY`, etc. |
| Vertex AI | `VERTEX_PROJECT`, `VERTEX_LOCATION` |
| GCS Caching | `GCS_BUCKET_NAME`, `GCS_HMAC_ACCESS_KEY_ID`, `GCS_HMAC_SECRET_ACCESS_KEY` |

**Not in Infisical** (must be set locally):
- `VERTEX_CREDENTIALS` - path to your local service account JSON file
- `PDD_PATH` - path to your local PDD installation

## Troubleshooting

**"Secret not found" errors:**
- Ensure you ran `infisical init` in the repo root
- Verify you have access to the project in your Infisical dashboard

**"API key appears too short" warnings:**
- This usually means Infisical secrets aren't being loaded
- Check that you're using `infisical run --` prefix

**Conflicts with local `.env`:**
- Infisical values take precedence over `.env` when there's a name conflict
- Keep only `VERTEX_CREDENTIALS` and `PDD_PATH` in your local `.env`

## Cost-Efficient CLI Usage

PDD has Google Cloud credits and limited Claude Max seats. Use AI CLI tools in this order to maximize value:

| Tier | Model | CLI Tool | Cost |
|------|-------|----------|------|
| 1st | Gemini Flash 3.0 | Gemini CLI | Free (GCP credits) |
| 2nd | Gemini Pro 3.0 | Gemini CLI | Free (GCP credits) |
| 3rd | Claude Opus 4.5 | Claude Code CLI | Limited (Max subscription) |

### Try and Escalate

1. **Start with Gemini Flash 3.0** for all tasks
2. **Escalate to Gemini Pro 3.0** if Flash struggles or produces poor results
3. **Use Claude Opus 4.5** only for truly complex problems that Gemini can't handle

This approach leverages our GCP credits while conserving limited Claude Max usage for when it's really needed.

## Agentic Workflows (Recommended)

For most development tasks, use the agentic commands that automate the entire workflow.

**Prerequisites:**
- GitHub CLI: `brew install gh && gh auth login`
- One Agentic CLI (install at least one):
  - **Claude Code**: `npm install -g @anthropic-ai/claude-code`
  - **Gemini CLI**: `npm install -g @google/gemini-cli`
  - **Codex CLI**: `npm install -g @openai/codex`

API keys are managed via Infisical (no need to set them locally).

### Web Interface (Easiest)

```bash
infisical run -- pdd connect
```

This opens a browser-based interface where you can run all PDD commands visually.

### Bug Fixes (CLI)

```bash
# Step 1: Analyze bug and create failing tests automatically
infisical run -- pdd bug https://github.com/gltanaka/pdd/issues/XXX

# Step 2: Fix the failing tests
infisical run -- pdd fix https://github.com/gltanaka/pdd/issues/XXX
```

The `pdd bug` command:
- Checks for duplicates
- Triages and reproduces the bug
- Creates failing unit tests
- Opens a draft PR

The `pdd fix` command:
- Iteratively fixes code until tests pass
- Updates the PR automatically

### Feature Requests (CLI)

```bash
# Implements the feature end-to-end
infisical run -- pdd change https://github.com/gltanaka/pdd/issues/XXX
```

This runs a 12-step workflow that researches, clarifies requirements, implements changes, and creates a PR.

### When to Use Manual Workflows

Fall back to manual workflows (below) when:
- The agentic workflow fails repeatedly
- You need fine-grained control over individual steps
- Debugging why agentic workflow isn't working

## Manual Workflows (Advanced/Fallback)

PDD has different workflows for **bug fixes** vs **new features**. The key principle: *bug fixes start with tests; features start with prompts*.

### Bug Fix vs Feature: Which Workflow?

| Task Type | Start With | Why |
|-----------|------------|-----|
| **Bug Fix** | Test file | Bugs represent *missed intent*—capture as a failing test first |
| **New Feature** | Prompt | Features represent *new intent*—define in the prompt first |

---

### Bug Fix Workflow

```mermaid
flowchart TD
    A[Bug reported] --> B[Create GitHub issue]
    B --> C[Write failing test]
    C --> D{Test fails?}

    D -->|No| E[Fix test - AI may have mocked it]
    E --> C
    D -->|Yes| F{Need prompt clarification?}

    F -->|Yes| G[Update prompt]
    G --> H[pdd fix module_name]
    F -->|No| H

    H --> I{Tests pass?}

    I -->|No, after retries| J{Can pdd fix solve it?}
    J -->|No| K[Manual fix or new issue]
    K --> H
    J -->|Yes| H

    I -->|Yes| L{Code changed, not just test?}
    L -->|No| M[AI cheated - fix test or prompt]
    M --> H
    L -->|Yes| N[pdd sync module_name]

    N --> O[Manual verification]
    O --> P{Actually works?}
    P -->|No| M
    P -->|Yes| Q[pdd update module_name]
    Q --> R[Update GitHub issue]
    R --> S[Create PR]
```

#### Steps

1. **Create GitHub issue** (enables parallel debugging):
   ```bash
   # Have Claude Code summarize the bug and create an issue
   # This lets you farm out multiple bugs to debug in parallel
   ```

2. **Write a failing test** (manually or via Claude Code):
   ```bash
   # NOTE: For most cases, use `pdd bug <issue-url>` instead (see Agentic Workflows above)
   # Use manual test writing only when agentic workflow fails
   # Add test to tests/test_module_name.py

   # Run the test to confirm it FAILS
   infisical run -- pytest -vv tests/test_module_name.py::test_specific_bug
   ```

   > **Why must it fail?** AI loves to mock things. If the test passes immediately, it's probably mocked up and not actually testing anything. A failing test proves you've pinpointed the real issue.

3. **Clarify the prompt** (if the bug reveals ambiguity):
   ```bash
   # Only if the prompt conflicts with expected behavior
   # Edit prompts/module_name.prompt directly
   # This is important because pdd fix reads the prompt
   ```

4. **Fix the code**:
   ```bash
   infisical run -- pdd fix module_name
   ```

5. **Verify the fix is real** (critical step!):
   ```bash
   # Check that CODE changed, not just the test
   git diff

   # If only the test changed and it passes - AI cheated!
   # The AI tends to do the easiest thing, which is often
   # modifying the test to pass rather than fixing the code
   ```

6. **Sync to regenerate example**:
   ```bash
   # Sync regenerates code and example (bug fix may add new inputs/outputs)
   infisical run -- pdd sync module_name
   ```

7. **Manual verification ("touch grass")**:
   ```bash
   # Run the actual command end-to-end
   # AI often works around problems rather than fixing them
   # You need to verify it actually works in reality
   ```

8. **Check if prompt needs updating**:
   ```bash
   # Run pdd update to see if prompt should change
   # Usually not needed for bug fixes, but check anyway
   infisical run -- pdd update module_name
   ```

9. **Update the GitHub issue and create PR**:
   ```bash
   # Have Claude Code:
   # 1. Summarize what was done
   # 2. Update the GitHub issue with findings
   # 3. Create PR linked to the issue
   ```

#### What if `pdd fix` can't fix it?

If `pdd fix` fails repeatedly even with agentic mode:
1. Analyze the scenario (multi-module issue? import problem?)
2. Try manual intervention
3. Create a new issue - it's likely a bug in `pdd fix` itself

---

### New Feature / Module Update Workflow

For adding features or maintaining modules, follow this workflow from the team discussion:

```mermaid
flowchart TD
    A[Feature request] --> B[Identify all affected modules]
    B --> C[pdd update on ALL modules]
    C --> D[pdd test until coverage >= 80%]
    D --> E{Coverage >= 80%?}

    E -->|No, keep trying| D
    E -->|No, unreachable| E2[Document justification + get approval]
    E2 --> F
    E -->|Yes| F[pdd fix --auto-submit for grounding]

    F --> G[Sync related docs: API, architecture]
    G --> H[Update prompts with new requirements]
    H --> I[pdd sync on all modules]

    I --> J[Run tests]
    J --> K{Tests pass?}

    K -->|No| L[pdd fix module_name]
    L --> I

    K -->|Yes| M[Write new tests for feature]
    M --> N[Manual verification]
    N --> O[Create PR]
```

#### Why This Order Matters

The preparation phase ensures:
- **Grounding**: Uploading existing code to PDD cloud means regenerations are grounded in proven patterns, not random
- **Prompt sync**: Prompts often drift from code; syncing first prevents conflicts
- **Test coverage**: Higher coverage = more reliable regeneration (the mold has more walls)
- **Document sync**: API docs define interfaces; including them in prompts ensures consistency across CLI and cloud

> **Critical**: Never update prompts with new requirements before syncing existing prompts to current code. Prompts that are out of sync with code produce unreliable regenerations.

#### Steps

1. **Identify affected modules** - List all modules that will change for this feature:
   - Check `docs/python_architecture.csv` for module dependencies
   - Search for imports of the modules you're changing
   - Look at which tests exercise the feature area

2. **Sync existing prompts** to current code state:
   ```bash
   # For EACH affected module
   infisical run -- pdd update module_name
   ```

3. **Increase test coverage** to at least 80%:
   ```bash
   infisical run -- pdd test module_name
   infisical run -- make coverage
   ```
   > High coverage = more mold walls = more reliable regeneration.

   **If 80% is unreachable:** Document why in the PR (e.g., external APIs, integration code) and get explicit approval before proceeding. Consider refactoring to improve testability.

4. **Upload to PDD cloud** for grounding:
   ```bash
   # --auto-submit uploads to vector database when tests pass
   infisical run -- pdd fix module_name --auto-submit
   ```
   > This ensures regeneration is grounded in proven patterns, not random.

5. **Sync related documents** (API docs, architecture files):
   - Update `docs/api.md` if interfaces change (required for CLI/cloud consistency)
   - Update `docs/python_architecture.csv` if module structure changes
   - Consider adding `<include>` tags in prompts to reference API docs

6. **Update prompts with new requirements**:
   ```bash
   # Edit prompts directly OR use pdd update interactively
   # Spend quality time here - prompts are the source of truth
   ```

7. **Regenerate all affected modules**:
   ```bash
   infisical run -- pdd sync module_name
   ```

8. **Fix any failures** and iterate:
   ```bash
   infisical run -- pdd fix module_name
   ```

9. **Write new tests** for the feature (using Claude Code or manually)

10. **Manual verification** - Touch grass, verify end-to-end

11. **Create PR** with test, prompt changes, and regenerated code

> **Note**: Modules with <50% coverage produce unreliable regenerations. Target 80% for reliable generation.

---

### PR Requirements Checklist

Before merging a PR, ensure it contains:

| Required | Item | Notes |
|----------|------|-------|
| ✅ | **Test** | Immediate rejection if missing - proves the bug existed |
| ✅ | **"All regression passed"** | So reviewer knows it's safe to merge |
| ⚠️ | **Prompt change** | Or comment: "prompt update not needed" |
| ⚠️ | **Example regenerated** | If prompt changed, example likely needs regeneration |
| ✅ | **Linked to issue** | PR and issue should reference each other |

---

### Quick Reference

| Command | Purpose | When to Use |
|---------|---------|-------------|
| `pdd connect` | **[RECOMMENDED]** Launch web interface | Visual PDD interaction |
| `pdd bug <url>` | **[RECOMMENDED]** Analyze bug and create failing tests | Bug fixes from GitHub issues |
| `pdd fix <url>` | **[RECOMMENDED]** Fix failing tests from pdd bug | After pdd bug creates tests |
| `pdd change <url>` | **[RECOMMENDED]** Implement feature request | New features from GitHub issues |
| `pdd fix` (manual) | Fix code to pass failing tests | Fallback when agentic fails |
| `pdd update` | Check if prompt needs changes | After fix, before PR |
| `pdd test` | Generate tests to increase coverage | Before regeneration (target: 80%) |
| `pdd sync` | Regenerate code, example & update few-shot DB | After fixes succeed |
| `make cloud-test` | Run full test suite on Cloud Batch (auto-rebuilds image) | CI validation before merging |
| `make cloud-test-quick` | Run tests on Cloud Batch (skip image rebuild) | Typical day-to-day test runs |

### Important Notes

- **Tests are permanent, code is ephemeral**: Tests accumulate over time; code is regenerated
- **Verify AI didn't cheat**: After `pdd fix`, check that CODE changed, not just the test
- **Touch grass**: Always manually verify the fix actually works end-to-end
- **Parallel debugging**: Create GitHub issues so multiple bugs can be debugged simultaneously
- **Few-shot learning**: Each successful sync updates the few-shot database, improving future generations
- **Coverage matters**: Modules with <50% coverage produce unreliable regenerations; target 80% for reliable generation