Hol Bug

Step 2: Select Injection Points

Choose injection points that meet ALL criteria:

Must Have

• Spans 2-3 files — the root cause is in one file, but the symptoms show in another
• No syntax errors — the app starts and mostly works
• Visible symptoms — at least one existing test fails, or API returns wrong data
• Diagnosable — a developer using Claude Code can trace from symptom to cause in ~15 min
• Satisfying "aha" — the root cause makes you go "oh, I see" not "that's random"
• Testable — a regression test can be written to catch it

Must NOT

• Break app startup (import errors, missing tables)
• Cause data corruption or security issues
• Be findable by just reading one file
• Be a typo or syntax error
• Require domain expertise to understand

Additional constraint for stretch bugs

A stretch bug must be safe to leave unfixed if a participant runs out of time. It must NOT block Acts 3-4 — meaning the feature build exercise must still be completable with the stretch bug present. In practice this means the stretch bug should affect a different data flow or endpoint than the one used by the recommended feature.

Step 3: Bug Pattern Catalog

Choose from these patterns (adapt to the specific codebase):

Pattern A: Wrong Tier/Category Logic

Where: Service layer applies wrong business logic based on a tier, category, or status field. Example: Gold-tier guests get Silver-tier pricing because the service checks tier == "silver" instead of tier == "gold", or uses <= instead of < on a threshold. Spans: services/*.py (root cause) + routers/*.py (wrong response) + models.py (tier definition) Symptoms: Tests comparing expected vs actual pricing/discounts fail. API returns wrong values for specific tier.

Pattern B: Query Filter Error

Where: A database query uses the wrong comparison operator or misses a condition. Example: Date range filter uses < instead of <=, excluding the last day. Or a filter applies to the wrong field. Spans: services/*.py (root cause in query) + routers/*.py (missing results in response) Symptoms: Search/filter endpoint returns fewer results than expected. Edge-case test fails.

Pattern C: Schema Default Override

Where: A Pydantic schema has a wrong default value that silently overrides user input or database values. Example: discount_percent: float = 0.0 when it should be Optional[float] = None, causing all records to show 0% discount unless explicitly set. Spans: schemas.py (root cause) + services/*.py (uses schema) + routers/*.py (serves wrong data) Symptoms: API returns unexpected defaults. Tests checking specific field values fail.

Pattern D: Wrong Aggregation/Sort

Where: A computation in the service layer uses the wrong field, wrong order, or wrong aggregation. Example: Sorting by created_at instead of departure_date, or summing base_price instead of total_price. Spans: services/*.py (root cause) + routers/*.py (wrong order/total in response) Symptoms: List endpoints return data in unexpected order. Summary endpoints show wrong totals.

Pattern E: Relationship Traversal Error

Where: Code follows the wrong relationship or misses a join condition. Example: Fetching a guest's bookings but filtering by guest_id on the wrong table, or missing an eager load causing empty nested data. Spans: services/*.py or models.py (root cause) + routers/*.py (incomplete response) Symptoms: Nested data missing or showing wrong related records.

Step 4: Implement Bugs

For each bug:

1. Make the code change — a small, targeted edit (typically 1-3 lines changed per file)
2. Verify the app still starts — uv run uvicorn main:app --reload should work
3. Verify symptoms are visible — run uv run pytest and confirm the right tests fail with useful (not cryptic) error messages
4. Document the answer key

Step 5: Write Answer Keys

Create .hol/bugs/answers.md:

# Bug Answer Keys — {App Name}

FACILITATOR ONLY — Do not share with participants.

---

## Bug 1: {Short Name}

**Difficulty:** Standard
**Symptom:** {What the participant sees — which test fails, what API returns wrong}

**Root Cause:** {1-2 sentence explanation}

**Affected Files:**
- `{file1}` line {N} — {what's wrong here}
- `{file2}` line {N} — {how it manifests here}

**The Fix:**
```diff
--- a/{file1}
+++ b/{file1}
@@ ...
- {buggy line}
+ {fixed line}

Regression Test:

def test_{bug_name}_regression():
    # This test would have caught the bug
    ...

Facilitator Hints (if participant is stuck):

• After 10 min: "Look at which test is failing and what value it expected vs got"
• After 15 min: "The issue is in the {service/schema/query} layer"

Repeat for each bug. If planting 2 bugs, label the second as:

```markdown
## Bug 2: {Short Name} *(Stretch)*

**Difficulty:** Stretch

Mark stretch bugs clearly so facilitators can skip them for time-constrained or less-experienced audiences. The first bug should always be the primary one that fits within the 20-min Act 2 window.

Step 6: Verify Final State

uv run pytest  # Some tests should fail — document which ones
uv run uvicorn main:app --reload  # App should still start

Report to the user:

• How many bugs planted
• Which tests fail (these are the symptoms participants will start from)
• Brief description of each bug (without spoiling the "aha")