GearBox/.planning/research/PITFALLS.md

Pitfalls Research

Domain: Adding search/filter, weight classification, weight distribution charts, candidate status tracking, and weight unit selection to an existing gear management app (GearBox v1.2) Researched: 2026-03-16 Confidence: HIGH (pitfalls derived from direct codebase analysis + domain-specific patterns from gear tracking community + React/SQLite ecosystem knowledge)

Critical Pitfalls

Pitfall 1: Weight Unit Conversion Rounding Accumulation

What goes wrong: GearBox stores weight as real("weight_grams") (a floating-point column in SQLite). When adding unit selection (g, oz, lb, kg), the naive approach is to convert on display and let users input in their preferred unit, converting back to grams on save. The problem: repeated round-trip conversions accumulate rounding errors. A user enters 5.3 oz, which converts to 150.253...g, gets stored as 150.253, then displayed back as 5.30 oz (fine so far). But if the user opens the edit form (which shows 5.30 oz), makes no changes, and saves, the value reconverts from the displayed 5.30 to 150.2535g -- a different value from what was stored. Over multiple edit cycles, weights drift. More critically, the existing SUM(items.weight_grams) aggregates in setup.service.ts and totals.service.ts will accumulate these micro-errors across dozens of items, producing totals that visibly disagree with manual addition of displayed values. A setup showing items of "5.3 oz + 2.1 oz" but a total of "7.41 oz" (instead of 7.40 oz) erodes trust in the app's core value proposition.

Why it happens: The conversion factor between grams and ounces (28.3495) is irrational enough that floating-point representation always involves truncation. Combined with SQLite's REAL type (8-byte IEEE 754 float, ~15 digits of precision), individual items are accurate enough, but the accumulation across conversions and summation surfaces visible errors.

How to avoid:

1. Store weights in grams as the canonical unit -- this is already done. Good.
2. Convert only at the display boundary (the formatWeight function in lib/formatters.ts). Never convert grams to another unit, let the user edit, and convert back.
3. When the user inputs in oz/lb/kg, convert to grams once on save and store. The edit form should always load the stored grams value and re-convert for display, never re-convert from a previously displayed value.
4. Round only at the final display step, not during storage. Use Number(value.toFixed(1)) for display, never for the stored value.
5. For totals, compute SUM(weight_grams) in SQL (already done), then convert the total to display units once. Do not sum converted per-item display values.
6. Consider changing weight_grams from real to integer to store milligrams (or tenths of grams) for sub-gram precision without floating-point issues. This is a larger migration but eliminates the class of errors entirely.

Warning signs:

• Edit form pre-fills with a converted value and saves by reconverting that value
• formatWeight is called before summation rather than after
• Unit conversion is done in multiple places (client and server) with different rounding
• Tests compare floating-point totals with === instead of tolerance-based comparison

Phase to address: Phase 1 (Weight unit selection) -- the conversion layer must be designed correctly from the start. Getting this wrong poisons every downstream feature (charts, setup totals, classification breakdowns).

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Pitfall 2: Weight Classification Stored at Wrong Level

What goes wrong: Weight classification (base weight / worn / consumable) seems like a property of the item itself -- "my rain jacket is always worn weight." So the developer adds a classification column to the items table. But this is wrong: the same item can be classified differently in different setups. A rain jacket is "worn" in a summer bikepacking setup but "base weight" (packed in the bag) in a winter setup where you wear a heavier outer shell. By putting classification on the item, users cannot accurately model multiple setups with the same gear, which is the entire point of the setup feature.

Why it happens: LighterPack and similar tools model classification at the list level (each list has its own classification per item), but when you look at the GearBox schema, the setup_items join table only has (id, setup_id, item_id). It feels more natural to add a column to the item itself rather than to a join table, especially since the current setup_items table is minimal. The single-user context also makes it feel like "my items have fixed classifications."

How to avoid: Add classification TEXT DEFAULT 'base' to the setup_items table, not to items. This means:

• The same item can have different classifications in different setups
• Classification is optional and defaults to "base" (the most common case)
• The items table stays generic -- classification is a setup-level concern
• Existing setup_items rows get a sensible default via the migration
• SQL aggregates for setup totals can easily group by classification: SUM(CASE WHEN setup_items.classification = 'base' THEN items.weight_grams ELSE 0 END)

If classification is also useful outside of setups (e.g., in the collection view for a general breakdown), add it as an optional defaultClassification on items that serves as a hint when adding items to setups, but the authoritative classification is always on setup_items.

Warning signs:

• classification column added to items table
• Setup detail view shows classification but cannot be different per setup
• Weight breakdown chart shows the same classification for an item across all setups
• No way to classify an item as "worn" in one setup and "base" in another

Phase to address: Phase 2 (Weight classification) -- this is the single most important schema decision in v1.2. Getting it wrong requires migrating data out of the items table into setup_items later, which means reconciling possibly-different classifications that users already set.

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Pitfall 3: Search/Filter Implemented Server-Side for a Client-Side Dataset

What goes wrong: The developer adds a GET /api/items?search=tent&category=3 endpoint, sending filtered results from the server. This means:

• Every keystroke fires an API request (or requires debouncing, adding latency)
• The client's React Query cache for ["items"] now contains different data depending on filter params, causing stale/inconsistent state
• Category grouping in CollectionView breaks because the full list is no longer available
• The existing useTotals() hook returns totals for all items, but the list shows filtered items -- a confusing mismatch

Why it happens: Server-side filtering is the "correct" pattern at scale, and most tutorials teach it that way. But GearBox is a single-user app where the entire collection fits comfortably in memory. The existing useItems() hook already fetches all items in one call and the collection view groups them client-side.

How to avoid: Implement search and filter entirely on the client side:

1. Keep useItems() fetching the full list (it already does)
2. Add filter state (search query, category ID) as URL search params or React state in the collection page
3. Filter the items array in the component using Array.filter() before grouping and rendering
4. The totals bar should continue to show collection totals (unfiltered), not filtered totals -- or show both ("showing 12 of 47 items")
5. Only move to server-side filtering if the collection exceeds ~500 items, which is far beyond typical for a single-user gear app

This preserves the existing caching behavior, requires zero API changes, and gives instant feedback on every keystroke.

Warning signs:

• New query parameters added to GET /api/items endpoint
• useItems hook accepts filter params, creating multiple cache entries
• Search input has a debounce delay
• Filtered view totals disagree with dashboard totals

Phase to address: Phase 1 (Search/filter) -- the decision to filter client-side vs server-side affects where state lives and must be decided before building the UI.

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Pitfall 4: Candidate Status Transition Without Validation

What goes wrong: The existing thread system has a simple status: "active" | "resolved" on threads and no status on candidates. Adding candidate status tracking (researching -> ordered -> arrived) as a simple text column without transition validation allows impossible states: a candidate marked "arrived" in a thread that was already "resolved," or a candidate going from "arrived" back to "researching." Worse, the existing resolveThread function in thread.service.ts copies candidate data to create a collection item -- but does not check or update candidate status, so a "researching" candidate can be resolved as the winner (logically wrong, though the data flow works).

Why it happens: The current codebase uses plain strings for thread status with no validation layer. The developer follows the same pattern for candidate status: just a text column with no constraints. SQLite does not enforce enum values, so any string is accepted.

How to avoid:

1. Define valid candidate statuses as a union type: "researching" | "ordered" | "arrived" in schemas.ts
2. Add Zod validation for the status field with .refine() or z.enum() to reject invalid values at the API level
3. Define valid transitions: researching -> ordered -> arrived (and optionally * -> dropped)
4. In the service layer, validate that the requested status transition is valid before applying it (e.g., cannot go from "arrived" to "researching")
5. When resolving a thread, do NOT require a specific candidate status -- the user may resolve with a "researching" candidate if they decide to buy it outright. But DO update all non-winner candidates to a terminal state like "dropped" in the same transaction.
6. Add a check in resolveThread: if the thread is already resolved, reject the operation (this check already exists in the current code -- good)

Warning signs:

• Candidate status is a plain text() column with no Zod enum validation
• No transition validation in the update candidate service
• resolveThread does not update non-winner candidate statuses
• UI allows arbitrary status changes via a dropdown with no constraints

Phase to address: Phase 3 (Candidate status tracking) -- must be designed with awareness of the existing thread resolution flow in thread.service.ts. The status field and transition logic should be added together, not incrementally.

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Pitfall 5: Weight Distribution Chart Diverges from Displayed Totals

What goes wrong: The weight distribution chart (e.g., a donut chart showing weight by category or by classification) computes its data from one source, while the totals bar and setup detail page compute from another. The chart might use client-side summation of displayed (rounded) values while the totals use SQL SUM(). Or the chart uses the useTotals() hook data while the setup page computes totals inline (as $setupId.tsx currently does on lines 53-61). These different computation paths produce different numbers for the same data, and when a chart slice says "Shelter: 2,450g" but the category header says "Shelter: 2,451g," users lose trust.

Why it happens: The codebase already has two computation paths for totals: totals.service.ts computes via SQL aggregates, and the setup detail page computes via JavaScript reduce on the client. These happen to agree now because there is no unit conversion, but adding unit display and classification filtering creates more opportunities for divergence.

How to avoid:

1. Establish a single source of truth for all weight computations: the SQL aggregate in the service layer.
2. For chart data, create a dedicated endpoint or extend GET /api/totals to return breakdowns by category AND by classification (for setups). Do not recompute in the chart component.
3. For setup-specific charts, extend getSetupWithItems to return pre-computed breakdowns, or compute them from the setup's item list using a shared utility function that is used by both the totals display and the chart.
4. Unit conversion happens once, at the display layer, using the same formatWeight function everywhere.
5. Write a test that compares the chart data source against the totals data source and asserts they agree.

Warning signs:

• Chart component does its own reduce() on item data instead of using the same data as the totals display
• Two different API endpoints return weight totals for the same scope and the values differ by small amounts
• Chart labels show different precision than text displays (chart: "2.4 kg", header: "2,451 g")
• No shared utility function for weight summation

Phase to address: Phase 3 (Weight distribution visualization) -- but the single-source-of-truth pattern should be established in Phase 1 when refactoring formatters for unit selection.

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Pitfall 6: Schema Migration Breaks Test Helper

What goes wrong: GearBox's test infrastructure uses a manual createTestDb() function in tests/helpers/db.ts that creates tables with raw SQL CREATE TABLE statements instead of using Drizzle's migration system. When adding new columns (e.g., classification to setup_items, status to thread_candidates, weight_unit to settings), the developer updates src/db/schema.ts and runs bun run db:generate + bun run db:push, but forgets to update the test helper's CREATE TABLE statements. All tests pass in the test database (which has the old schema) while the real database has the new schema -- or worse, tests fail with cryptic column-not-found errors and the developer wastes time debugging the wrong thing.

Why it happens: The test helper duplicates the schema definition in raw SQL rather than deriving it from the Drizzle schema. This is a known pattern in the codebase (documented in CLAUDE.md: "When adding schema columns, update both src/db/schema.ts and the test helper's CREATE TABLE statements"). But under the pressure of adding multiple schema changes across several features, it is easy to miss one table or one column.

How to avoid:

1. Every schema change PR must include the corresponding test helper update. Add this as a checklist item in the development workflow.
2. Consider writing a simple validation test that compares the columns in createTestDb() tables against the Drizzle schema definition, failing if they diverge. This catches the problem automatically.
3. For v1.2, since multiple schema changes are landing (classification on setup_items, status on candidates, possibly weight_unit in settings), batch the test helper update and verify all changes in one pass.
4. Long-term: investigate using Drizzle's migrate() with in-memory SQLite to eliminate the duplication entirely.

Warning signs:

• Schema column added to schema.ts but not to tests/helpers/db.ts
• Tests pass locally but queries fail at runtime
• New service function works in the app but throws in tests
• Test database has fewer columns than production database

Phase to address: Every phase that touches the schema. Must be addressed in Phase 1 (unit settings), Phase 2 (classification on setup_items), and Phase 3 (candidate status). Each phase should verify test helper parity as a completion criterion.

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Pitfall 7: Weight Unit Preference Stored Wrong, Applied Wrong

What goes wrong: The developer stores the user's preferred weight unit as a setting (using the existing settings table with key-value pairs). But then applies it inconsistently: the collection page shows grams, the setup page shows ounces, the chart shows kilograms. Or the setting is read once on page load and cached in Zustand, so changing the preference requires a page refresh. Or the setting is read on every render, causing a flash of "g" before the "oz" preference loads.

Why it happens: The settings table is a key-value store with no type safety. The preference is a string like "oz" that must be parsed and applied in many places: formatWeight in formatters, chart labels, totals bar, setup detail, item cards, category headers. Missing any one of these locations creates an inconsistency.

How to avoid:

1. Store the preference in the settings table as { key: "weightUnit", value: "g" | "oz" | "lb" | "kg" }.
2. Create a useWeightUnit() hook that wraps useSettings() and returns the parsed unit with a fallback to "g".
3. Modify formatWeight to accept a unit parameter: formatWeight(grams, unit). This is a single function used everywhere, so changing it propagates automatically.
4. Do NOT store converted values anywhere -- always store grams, convert at display time.
5. Use React Query for the settings fetch so the preference is cached and shared across components. When the user changes their preference, invalidate ["settings"] and all displays update simultaneously via React Query's reactivity.
6. Handle the loading state: show raw grams (or a loading skeleton) until the preference is loaded. Do not flash a different unit.

Warning signs:

• formatWeight does not accept a unit parameter -- it is hardcoded to "g"
• Weight unit preference is stored in Zustand instead of React Query (settings endpoint)
• Some components use formatWeight and some inline their own formatting
• Changing the unit preference does not update all visible weights without a page refresh

Phase to address: Phase 1 (Weight unit selection) -- this is foundational infrastructure. The formatWeight refactor and useWeightUnit hook must exist before building any other feature that displays weight.

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Technical Debt Patterns

Shortcuts that seem reasonable but create long-term problems.

Shortcut	Immediate Benefit	Long-term Cost	When Acceptable
Adding classification to items instead of setup_items	Simpler schema, no join table changes	Cannot have different classifications per setup; future migration required	Never -- the per-setup model is the correct one
Client-side unit conversion on both read and write paths	Simple bidirectional conversion	Rounding drift over edit cycles, inconsistent totals	Never -- convert once on write, display-only on read
Separate chart data computation from totals computation	Faster chart development, no API changes	Numbers disagree between chart and text displays	Only if a shared utility function ensures identical computation
Hardcoding chart library colors per category	Quick to implement	Colors collide when user adds categories; no dark mode support	MVP only if using a predictable color generation function is planned
Adding candidate status without transition validation	Faster to implement	Invalid states accumulate, resolve logic has edge cases	Only if validation is added before the feature ships to production
Debouncing search instead of client-side filter	Familiar pattern from server-filtered apps	Unnecessary latency, complex cache management	Never for this app's scale (sub-500 items)

Integration Gotchas

Since v1.2 is adding features to an existing system rather than integrating external services, these are internal integration points where new features interact with existing ones.

Integration Point	Common Mistake	Correct Approach
Search/filter + category grouping	Filtering items breaks the existing category-grouped layout because the group headers disappear when no items match	Filter within groups: show category headers only for groups with matching items. Empty groups should hide, not show "no items."
Weight classification + existing setup totals	Adding classification changes how totals are computed (base weight vs total weight), but existing setup list cards show totalWeight which was previously "everything"	Keep totalWeight as the sum of all items. Add baseWeight as a new computed field (sum of items where classification = 'base'). Show both in the setup detail view.
Candidate status + thread resolution	Adding status to candidates but not updating resolveThread to handle it	The resolveThread transaction must set winner status to a terminal state and non-winners to "dropped." New candidates added to an already-resolved thread should be rejected.
Unit selection + React Query cache	Changing the weight unit preference does not invalidate the items cache because items are stored in grams regardless	The unit preference is a display concern, not a data concern. Do NOT invalidate items/totals on unit change. Just re-render with the new unit. Ensure formatWeight is called reactively, not cached.
Weight chart + empty/null weights	Chart component crashes or shows misleading data when items have null weight	Filter out items with null weight from chart data. Show a note like "3 items excluded (no weight recorded)." Never treat null as 0 in a chart -- that makes the chart lie.

Performance Traps

Patterns that work at small scale but fail as usage grows.

Trap	Symptoms	Prevention	When It Breaks
Re-rendering entire collection on search keystroke	UI jank on every character typed in search box	Use useMemo to memoize the filtered list; ensure ItemCard is memoized with React.memo	100+ items with images
Chart re-renders on every parent state change	Chart animation restarts on unrelated state updates (e.g., opening a panel)	Memoize chart data computation with useMemo; wrap chart component in React.memo; use isAnimationActive={false} after initial render	Any chart library with entrance animations
Recharts SVG rendering with many category slices	Donut chart becomes sluggish with 20+ categories, each with a tooltip and label	Limit chart to top N categories by weight, group the rest into "Other." Recharts is SVG-based, so keep segments under ~15.	20+ categories (unlikely for single user, but possible)
Fetching settings on every component that displays weight	Waterfall of settings requests, or flash of unconverted weights	Use React Query with staleTime: Infinity for settings (they change rarely). Prefetch settings at app root.	First load of any page with weights
Computing classification breakdown per-render	Expensive reduce operations on every render cycle	Compute once in useMemo keyed on the items array and classification data	Setups with 50+ items (common for full bikepacking lists)

Security Mistakes

Domain-specific security issues beyond general web security.

Mistake	Risk	Prevention
Candidate status field accepts arbitrary strings	SQLite text column accepts anything; UI may display unexpected values or XSS payloads in status badges	Validate status against enum in Zod schema. Reject unknown values at API level. Use z.enum(["researching", "ordered", "arrived"]).
Search query used in raw SQL LIKE	SQL injection if search string is interpolated into query (unlikely with Drizzle ORM but possible in raw SQL aggregates)	Use Drizzle's like() or ilike() operators which parameterize automatically. Never use template literals in sql\`` with unsanitized user input.
Unit preference allows arbitrary values	Settings table stores any string; a crafted value could break formatWeight or cause display issues	Validate unit against z.enum(["g", "oz", "lb", "kg"]) both on read and write. Use a typed constant for the allowed values.

UX Pitfalls

Common user experience mistakes in this domain.

Pitfall	User Impact	Better Approach
Search clears when switching tabs (gear/planning/setups)	User searches for "tent," switches to planning to check threads, switches back and search is gone	Persist search query as a URL search parameter (?tab=gear&q=tent). TanStack Router already handles tab via search params.
Unit selection buried in settings page	User cannot quickly toggle between g and oz when comparing products listed in different units	Add a unit toggle/selector directly in the weight display area (e.g., in the TotalsBar or a small dropdown next to weight values). Keep global preference in settings, but allow quick access.
Classification picker adds friction to setup composition	User must classify every item when adding it to a setup, turning a quick "add to loadout" into a tedious process	Default all items to "base" classification. Allow bulk reclassification. Show classification as an optional second step after composing the setup.
Chart with no actionable insight	A pie chart showing "Shelter: 40%, Sleep: 25%, Cooking: 20%" is pretty but does not help the user make decisions	Pair the chart with a list sorted by weight. Highlight the heaviest category. If possible, show how the breakdown compares to "typical" or to other setups. At minimum, make chart segments clickable to filter to that category.
Status badges with no timestamps	User sees "ordered" but cannot remember when they ordered, or whether it has been suspiciously long	Store status change timestamps. Show relative time ("ordered 3 days ago"). Highlight statuses that have been stale too long ("ordered 30+ days ago -- still waiting?").
Filter resets feel destructive	User applies multiple filters (category + search), then accidentally clears one and loses the other	Show active filters as dismissible chips/pills above the list. Each filter is independently clearable. A "clear all" button resets everything.

"Looks Done But Isn't" Checklist

Things that appear complete but are missing critical pieces.

• Search/filter: Often missing keyboard shortcut (Cmd/Ctrl+K to focus search) -- verify search is easily accessible without mouse
• Search/filter: Often missing empty state for "no results" -- verify a helpful message appears when search matches nothing, distinct from "collection is empty"
• Weight classification: Often missing the per-setup model -- verify the same item can have different classifications in different setups
• Weight classification: Often missing "unclassified" handling -- verify items with no classification default to "base" in all computations
• Weight chart: Often missing null-weight items -- verify items without weight data are excluded from chart with a visible note, not silently treated as 0g
• Weight chart: Often missing responsiveness -- verify chart renders correctly on mobile widths (Recharts needs ResponsiveContainer wrapper)
• Candidate status: Often missing transition validation -- verify a candidate cannot go from "arrived" back to "researching"
• Candidate status: Often missing integration with thread resolution -- verify resolving a thread updates all candidate statuses appropriately
• Unit selection: Often missing consistent application -- verify every weight display in the app (cards, headers, totals, charts, setup detail, item picker) uses the selected unit
• Unit selection: Often missing the edit form -- verify the item/candidate edit form shows weight in the selected unit and converts correctly on save
• Unit selection: Often missing chart axis labels -- verify the chart shows the correct unit in labels and tooltips

Recovery Strategies

When pitfalls occur despite prevention, how to recover.

Pitfall	Recovery Cost	Recovery Steps
Classification on items instead of setup_items	HIGH	Add classification column to setup_items. Write migration to copy item classification to all setup_item rows referencing it. Remove classification from items. Review all service queries.
Rounding drift from bidirectional conversion	MEDIUM	Audit all items for drift (compare stored grams to expected values). Fix formatWeight to convert only at display. One-time data cleanup for items with suspicious fractional grams.
Chart data disagrees with totals	LOW	Refactor chart to use the same data source as totals. Create shared utility. No data migration needed.
Test helper out of sync with schema	LOW	Update CREATE TABLE statements in test helper. Run all tests. Fix any that relied on the old schema.
Server-side search causing cache issues	MEDIUM	Revert to client-side filtering. Remove query params from useItems. May need to clear stale React Query cache entries with different keys.
Candidate status without transitions	MEDIUM	Add transition validation to update endpoint. Audit existing candidates for invalid states. Write cleanup migration if needed.
Unit preference inconsistently applied	LOW	Audit all weight display points. Ensure all use formatWeight with unit parameter. No data changes needed.

Pitfall-to-Phase Mapping

How roadmap phases should address these pitfalls.

Pitfall	Prevention Phase	Verification
Rounding accumulation	Phase 1: Weight unit selection	formatWeight converts grams to display unit. Edit forms load grams from API, not from displayed value. Write test: edit an item 10 times without changes, weight stays identical.
Classification at wrong level	Phase 2: Weight classification	classification column exists on setup_items, not items. Test: same item in two setups has different classifications.
Server-side search for client data	Phase 1: Search/filter	No new API parameters on GET /api/items. Filter logic lives in CollectionView component. Test: search works instantly without network requests.
Status without transition validation	Phase 3: Candidate status	Zod enum validates status values. Service rejects invalid transitions. Test: updating "arrived" to "researching" returns 400 error.
Chart/totals divergence	Phase 3: Weight visualization	Chart data and totals bar use same computation path. Test: sum of chart segment values equals displayed total.
Test helper desync	Every schema-changing phase	Each phase's PR includes updated test helper. CI test suite catches column mismatches.
Unit preference inconsistency	Phase 1: Weight unit selection	All weight displays use formatWeight(grams, unit). Test: change unit preference, verify all visible weights update without refresh.

Sources

• Weight conversion precision and rounding best practices -- authoritative source on conversion factor precision
• Base weight classification definitions and community debates -- real-world examples of classification ambiguity
• LighterPack user classification errors -- LighterPack's approach to base/worn/consumable
• Avoiding common mistakes with TanStack Query -- anti-patterns with React Query caching
• TanStack Query discussions on filtering with cache -- community patterns for client-side vs server-side filtering
• Recharts performance and limitations -- SVG rendering pitfalls, ResponsiveContainer requirement
• Drizzle ORM SQLite migration pitfalls -- data loss bug with push + add column
• State machine anti-patterns -- importance of explicit transition validation
• Ultralight gear tracker leaving LighterPack -- community frustrations with existing tools
• Direct codebase analysis of GearBox v1.1 (schema.ts, services, hooks, routes) -- existing patterns and integration points

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Pitfalls research for: GearBox v1.2 -- Collection Power-Ups (search/filter, weight classification, charts, candidate status, unit selection) Researched: 2026-03-16