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.trellis/spec/guides/code-reuse-thinking-guide.md
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# Code Reuse Thinking Guide
# 代码复用思考指南
> **Purpose**: Stop and think before creating new code - does it already exist?
## 什么时候先搜索
---
## The Problem
**Duplicated code is the #1 source of inconsistency bugs.**
When you copy-paste or rewrite existing logic:
- Bug fixes don't propagate
- Behavior diverges over time
- Codebase becomes harder to understand
---
## Before Writing New Code
### Step 1: Search First
在创建 helper、常量、转换函数或新的 UI primitive 前，先搜索这些本地模式：
```bash
# Search for similar function names
grep -r "functionName" .
# Search for similar logic
grep -r "keyword" .
rg "requestJson|ApiError" zhixing-web/src
rg "QueryKey|queryKey|useQuery" zhixing-web/src
rg "className|cva\(|cn\(" zhixing-web/src/shared zhixing-web/src/features
rg "create_app|response_model|Depends\(" zhixing-server/src zhixing-server/tests
```
### Step 2: Ask These Questions
当前仓库已经有明确的复用点：
| Question | If Yes... |
|----------|-----------|
| Does a similar function exist? | Use or extend it |
| Is this pattern used elsewhere? | Follow the existing pattern |
| Could this be a shared utility? | Create it in the right place |
| Am I copying code from another file? | **STOP** - extract to shared |
- 网络 JSON 请求统一经 `zhixing-web/src/shared/api/request-json.ts`，不要在页面重新实现 `fetch` 和错误判断。
- Tailwind class 合并统一经 `zhixing-web/src/shared/ui/utils.ts:cn`；有限变体用 `cva`，参照 `button.tsx` 和 `badge.tsx`。
- 服务端应用组合统一经 `zhixing-server/src/zhixing_server/bootstrap/app.py:create_app`，测试也调用真实工厂。
- React Query key 以 feature 内 `as const` 常量维护，参照 `system.query.ts`。
---
## 复用与边界
## Common Duplication Patterns
- 只有跨 feature、无业务所有权的能力才进入 `shared/`；一次性页面逻辑留在 feature。
- 看到两处相似代码时，先确认输入、错误语义和生命周期是否真的相同，再抽象；不要为了消除两行重复创建泛化框架。
- API 类型和领域概念不能因为“看起来相同”就自动合并。后端 Pydantic 模型、feature TypeScript 类型和 UI view model 各自承担边界责任。
### Pattern 1: Copy-Paste Functions
## 验证问题
**Bad**: Copying a validation function to another file
**Good**: Extract to shared utilities, import where needed
### Pattern 2: Similar Components
**Bad**: Creating a new component that's 80% similar to existing
**Good**: Extend existing component with props/variants
### Pattern 3: Repeated Constants
**Bad**: Defining the same constant in multiple files
**Good**: Single source of truth, import everywhere
### Pattern 4: Repeated Payload Field Extraction
**Bad**: Multiple consumers cast the same JSON/event fields locally:
```typescript
const description = (ev as { description?: string }).description;
const context = (ev as { context?: ContextEntry[] }).context;
```
This is duplicated contract logic even when the code is only two lines. Each
consumer now has its own definition of what a valid payload means.
**Good**: Put the decoder, type guard, or projection next to the data owner:
```typescript
if (isThreadEvent(ev)) {
renderThreadEvent(ev);
}
```
**Rule**: If the same untyped payload field is read in 2+ places, create a
shared type guard / normalizer / projection before adding a third reader.
---
## When to Abstract
**Abstract when**:
- Same code appears 3+ times
- Logic is complex enough to have bugs
- Multiple people might need this
**Don't abstract when**:
- Only used once
- Trivial one-liner
- Abstraction would be more complex than duplication
---
## After Batch Modifications
When you've made similar changes to multiple files:
1. **Review**: Did you catch all instances?
2. **Search**: Run grep to find any missed
3. **Consider**: Should this be abstracted?
### Reducers Should Use Exhaustive Structure
When state is derived from action-like values (`action`, `kind`, `status`,
`phase`), prefer a reducer with one `switch` over scattered `if/else` updates.
```typescript
// BAD - action-specific state transitions are hard to audit
if (action === "opened") { ... }
else if (action === "comment") { ... }
else if (action === "status") { ... }
// GOOD - one reducer owns the transition table
switch (event.action) {
case "opened":
...
return;
case "comment":
...
return;
}
```
This matters when the event log is the source of truth. A reducer is the
documented replay model; display code and commands should not duplicate pieces
of that replay model.
---
## Checklist Before Commit
- [ ] Searched for existing similar code
- [ ] No copy-pasted logic that should be shared
- [ ] No repeated untyped payload field extraction outside a shared decoder
- [ ] Constants defined in one place
- [ ] Similar patterns follow same structure
- [ ] Reducer/action transitions live in one reducer or command dispatcher
---
## Gotcha: Python if/elif/else Exhaustive Check
**Problem**: Python's if/elif/else chains have no compile-time exhaustive check. When you add a new value to a `Literal` type (e.g., `Platform`), existing if/elif/else chains silently fall through to `else` with wrong defaults.
**Symptom**: New platform works partially — some methods return Claude defaults instead of platform-specific values. No error is raised.
**Example** (`cli_adapter.py`):
```python
# BAD: "gemini" falls through to else, returns "claude"
@property
def cli_name(self) -> str:
if self.platform == "opencode":
return "opencode"
else:
return "claude" # gemini silently gets "claude"!
# GOOD: explicit branch for every platform
@property
def cli_name(self) -> str:
if self.platform == "opencode":
return "opencode"
elif self.platform == "gemini":
return "gemini"
else:
return "claude"
```
**Prevention**: When adding a new value to a Python `Literal` type, search for ALL if/elif/else chains that switch on that type and add explicit branches. Don't rely on `else` being correct for new values.
---
## Gotcha: Asymmetric Mechanisms Producing Same Output
**Problem**: When two different mechanisms must produce the same file set (e.g., recursive directory copy for init vs. manual `files.set()` for update), structural changes (renaming, moving, adding subdirectories) only propagate through the automatic mechanism. The manual one silently drifts.
**Symptom**: Init works perfectly, but update creates files at wrong paths or misses files entirely.
**Prevention**:
- **Best**: Eliminate the asymmetry — have the manual path call the automatic one (e.g., `collectTemplateFiles()` calls `getAllScripts()` instead of maintaining its own list)
- **If asymmetry is unavoidable**: Add a regression test that compares outputs from both mechanisms
- When migrating directory structures, search for ALL code paths that reference the old structure
**Real example**: `trellis update` had a manual `files.set()` list for 11 scripts that `getAllScripts()` already tracked. Fix: replaced the manual list with a `for..of getAllScripts()` loop. See `update.ts` refactor in v0.4.0-beta.3.
---
## Template File Registration (Trellis-specific)
When adding new files to `src/templates/trellis/scripts/`:
**Single registration point**: `src/templates/trellis/index.ts`
1. Add `export const xxxScript = readTemplate("scripts/path/file.py");`
2. Add to `getAllScripts()` Map
That's it. `commands/update.ts` uses `getAllScripts()` directly — no manual sync needed.
**Why this matters**: Without registration in `getAllScripts()`, `trellis update` won't sync the file to user projects. Bug fixes and features won't propagate.
**History**: Before v0.4.0-beta.3, `update.ts` had its own hand-maintained file list that frequently fell out of sync with `getAllScripts()`. This caused 11 Python files to be silently skipped during `trellis update`. The fix was to eliminate the duplicate list and use `getAllScripts()` as the single source of truth.
### Quick Checklist for New Scripts
```bash
# After adding a new .py file, verify it's in getAllScripts():
grep -l "newFileName" src/templates/trellis/index.ts # Should match
```
### Template Sync Convention
`.trellis/scripts/` (dogfooded) and `packages/cli/src/templates/trellis/scripts/` (template) must stay identical. After editing `.trellis/scripts/`, always sync:
```bash
rsync -av --delete --exclude='__pycache__' .trellis/scripts/ packages/cli/src/templates/trellis/scripts/
```
**Gotcha**: Running rsync with wrong source/destination paths can create nested garbage directories (e.g., `.trellis/scripts/packages/cli/...`). Always double-check paths before running.
- 新 helper 是否能被现有测试直接覆盖？
- 抽象后是否让导入方向更清楚，而不是引入 shared → feature 反向依赖？
- 是否保留了 `AbortSignal`、错误状态、可访问性和类型约束等原有行为？
.trellis/spec/guides/cross-layer-thinking-guide.md
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# Cross-Layer Thinking Guide
# 跨层契约思考指南
> **Purpose**: Think through data flow across layers before implementing.
本仓库的最小链路是：
---
## The Problem
**Most bugs happen at layer boundaries**, not within layers.
Common cross-layer bugs:
- API returns format A, frontend expects format B
- Database stores X, service transforms to Y, but loses data
- Multiple layers implement the same logic differently
---
## Before Implementing Cross-Layer Features
### Step 1: Map the Data Flow
Draw out how data moves:
```
Source → Transform → Store → Retrieve → Transform → Display
```text
FastAPI 路由
→ Pydantic response model
→ 同源 /api/v1 代理
→ requestJson<T>
→ feature *.types.ts / *.api.ts
→ React Query hook
→ 页面加载、错误、成功状态
```
For each arrow, ask:
以系统状态为例，链路对应：
- What format is the data in?
- What could go wrong?
- Who is responsible for validation?
- 后端：`interfaces/http/router.py` 挂载 `/api/v1/system/status`，`interfaces/http/system.py` 返回 `SystemStatusResponse`。
- 后端测试：`tests/test_system_http.py` 断言状态码和完整 JSON。
- 前端：`features/system/api/system.api.ts` 使用 `/api/v1/system/status`，`system.types.ts` 描述字段，`system.query.ts` 管理缓存。
- 页面测试：`system-status-page.test.tsx` 断言“运行正常”和环境文本。
### Step 2: Identify Boundaries
## 修改 HTTP 字段前
| Boundary | Common Issues |
| --------------------- | --------------------------------- |
| API ↔ Service | Type mismatches, missing fields |
| Service ↔ Database | Format conversions, null handling |
| Backend ↔ Frontend | Serialization, date formats |
| Component ↔ Component | Props shape changes |
1. 找到后端响应模型、路由和现有契约测试。
2. 找到 feature API 函数、TypeScript 类型、query hook 和页面分支。
3. 确认开发 Vite 代理和生产 Nginx 仍覆盖该路径；浏览器代码保持同源路径。
4. 同步更新后端 HTTP 测试和前端行为测试，再运行两端质量命令。
### Step 3: Define Contracts
## 常见跨层遗漏
For each boundary:
- 只修改 Pydantic 字段，没有修改 `*.types.ts`，导致 UI 仍读取旧形状。
- 把 `/api/v1` 改成后端绝对 URL，绕过 `vite.config.ts` 和 Nginx 的同源代理。
- 在页面内直接 `fetch`，绕过 `requestJson` 的 `ApiError` 和 `AbortSignal`。
- 用 Zustand 缓存服务器响应，造成 React Query 缓存与全局 store 的双重事实来源。
- 只测试请求成功，没有测试 `isPending`、`isError` 或字段缺失时的安全展示。
- What is the exact input format?
- What is the exact output format?
- What errors can occur?
## 边界验证
---
## Common Cross-Layer Mistakes
### Mistake 1: Implicit Format Assumptions
**Bad**: Assuming date format without checking
**Good**: Explicit format conversion at boundaries
### Mistake 2: Scattered Validation
**Bad**: Validating the same thing in multiple layers
**Good**: Validate once at the entry point
### Mistake 3: Leaky Abstractions
**Bad**: Component knows about database schema
**Good**: Each layer only knows its neighbors
### Mistake 4: Every Consumer Parses The Same Payload
**Bad**: A command reads JSONL events and casts fields inline:
```typescript
const thread = (ev as { thread?: string }).thread;
const labels = (ev as { labels?: string[] }).labels;
```
This looks local, but it means every consumer owns a private version of the
event contract. The next field change will update one command and miss another.
**Good**: Decode once at the event boundary, then export typed projections:
```typescript
if (!isThreadEvent(ev)) return false;
return ev.thread === filter.thread;
```
**Rule**: For append-only logs, JSON streams, RPC payloads, or config files,
create one owner for:
- event / payload type definitions
- type guards and normalization from `unknown`
- metadata projections used by UI commands
- reducers that replay state from the source of truth
Rendering code may format fields, but it must not redefine the payload contract.
---
## Checklist for Cross-Layer Features
Before implementation:
- [ ] Mapped the complete data flow
- [ ] Identified all layer boundaries
- [ ] Defined format at each boundary
- [ ] Decided where validation happens
After implementation:
- [ ] Tested with edge cases (null, empty, invalid)
- [ ] Verified error handling at each boundary
- [ ] Checked data survives round-trip
- [ ] Checked that consumers import shared decoders / projections instead of
casting payload fields locally
- [ ] Checked that derived state points back to the source event identifier
(`seq`, `id`, `version`) instead of inventing a second cursor
---
## Cross-Platform Template Consistency
In Trellis, command templates (e.g., `record-session.md`) exist in **multiple platforms** with identical or near-identical content. This is a cross-layer boundary.
### Checklist: After Modifying Any Command Template
- [ ] Find all platforms with the same command: `find src/templates/*/commands/trellis/ -name "<command>.*"`
- [ ] Update all platform copies (Markdown `.md` and TOML `.toml`)
- [ ] For Gemini TOML: adapt line continuations (`\\` vs `\`) and triple-quoted strings
- [ ] Run `/trellis:check-cross-layer` to verify nothing was missed
**Real-world example**: Updated `record-session.md` in Claude to use `--mode record`, but forgot iFlow, Kilo, OpenCode, and Gemini — caught by cross-layer check.
---
## Generated Runtime Template Upgrade Consistency
Some generated files are both documentation and runtime input. In Trellis,
`.trellis/workflow.md` is parsed by `get_context.py`, `workflow_phase.py`,
SessionStart filters, and per-turn hooks. Template changes must be validated
against both fresh init and upgrade paths.
### Checklist: After Modifying A Runtime-Parsed Template
- [ ] Identify every runtime parser that reads the template, not just the file
writer that installs it
- [ ] Check whether relevant syntax lives outside obvious managed regions
such as tag blocks
- [ ] Verify fresh `init` output and a versioned `update` scenario that writes
the older `.trellis/.version`
- [ ] Add an upgrade regression using an older pristine template fixture, then
assert the installed file reaches the current packaged shape
- [ ] Update the backend spec that owns the runtime contract
---
## Versioned Documentation Boundary
Versioned documentation is a cross-layer boundary: source paths, `docs.json`
version routing, and the rendered version selector must all describe the same
release line.
### Checklist: Before Editing Versioned Docs
- [ ] Identify the target release line: stable, beta, or RC
- [ ] Verify the edited MDX path matches that line:
- stable: `docs-site/{start,advanced,...}` and `docs-site/zh/{start,advanced,...}`
- beta: `docs-site/beta/**` and `docs-site/zh/beta/**`
- RC: `docs-site/rc/**` and `docs-site/zh/rc/**`
- [ ] Verify `docs.json` navigation points the version label to the same paths
- [ ] Grep the opposite tree for release-line-specific terms before committing
- [ ] Treat beta content appearing under root release paths as a source-path bug,
not a rendering bug
**Real-world example**: A beta-only task workflow change documented
`prd.md` + `design.md` + `implement.md`, task-creation consent, and Codex
mode banners under root `start/` and `advanced/` paths. The docs site then
served 0.6 beta behavior under the Release selector. The fix was to restore root
release docs, move the 0.6 content to `beta/` and `zh/beta/`, and add a grep
audit for beta markers against the root release tree.
**Real-world example**: Codex inline mode changed workflow platform markers from
`[Codex]` / `[Kilo, Antigravity, Windsurf]` to `[codex-sub-agent]` /
`[codex-inline, Kilo, Antigravity, Windsurf]`. Fresh init was correct, but
`trellis update` only merged `[workflow-state:*]` blocks and preserved stale
markers outside those blocks. Result: upgraded projects got new hook scripts
but old workflow routing, so `get_context.py --mode phase --platform codex`
could return empty Phase 2.1 detail.
---
## Mode-Detection Probe Checklist
When a CLI auto-detects a mode by probing a remote resource (e.g., checking if `index.json` exists to decide marketplace vs direct download):
### Before implementing:
- [ ] Probe runs in **ALL** code paths that use the result (interactive, `-y`, `--flag` combos)
- [ ] 404 vs transient error are distinguished — don't treat both as "not found"
- [ ] Transient errors **abort or retry**, never silently switch modes
- [ ] Shared state (caches, prefetched data) is **reset** when context changes (e.g., user switches source)
- [ ] **Shortcut paths** (e.g., `--template` skipping picker) must have the same error-handling quality as the probed path — check that downstream functions don't call catch-all wrappers
### After implementing:
- [ ] Trace every path from probe result to the mode-decision branch — no fallthrough
- [ ] External format contracts (giget URI, raw URLs) are tested or at least documented as comments
- [ ] Metadata reads consume a complete response or use a streaming parser — never parse a fixed-size prefix as full JSON
- [ ] When reconstructing a composite identifier from parsed parts, verify **all** fields are included and in the **correct position** (e.g., `provider:repo/path#ref` not `provider:repo#ref/path`)
- [ ] Verify that **action functions** called after a shortcut don't internally use the old catch-all fetch — they must use the probe-quality variant when error distinction matters
**Real-world example**: Custom registry flow had 8 bugs across 3 review rounds: (1) probe only ran in interactive mode, (2) transient errors fell through to wrong mode, (3) giget URI had `#ref` in wrong position, (4) prefetched templates leaked across source switches, (5) `--template` shortcut bypassed probe but `downloadTemplateById` internally used catch-all `fetchTemplateIndex`, turning timeouts into "Template not found".
**Real-world example**: Agent-session update hints fetched npm `latest` metadata with `response.read(4096)` and then parsed it as complete JSON. The `@mindfoldhq/trellis` package metadata exceeded 4 KB, so the JSON was truncated, parse failed silently, and the first session injection showed no update hint. Fix: read the complete response before parsing, and add a regression where `version` is followed by an 8 KB metadata tail.
---
## Cross-Platform Template Consistency
In Trellis, command templates (e.g., `record-session.md`) exist in **multiple platforms** with identical or near-identical content. This is a cross-layer boundary.
### Checklist: After Modifying Any Command Template
- [ ] Find all platforms with the same command: `find src/templates/*/commands/trellis/ -name "<command>.*"`
- [ ] Update all platform copies (Markdown `.md` and TOML `.toml`)
- [ ] For Gemini TOML: adapt line continuations (`\\` vs `\`) and triple-quoted strings
- [ ] Run `/trellis:check-cross-layer` to verify nothing was missed
**Real-world example**: Updated `record-session.md` in Claude to use `--mode record`, but forgot iFlow, Kilo, OpenCode, and Gemini — caught by cross-layer check.
---
## Generated Runtime Template Upgrade Consistency
Some generated files are both documentation and runtime input. In Trellis,
`.trellis/workflow.md` is parsed by `get_context.py`, `workflow_phase.py`,
SessionStart filters, and per-turn hooks. Template changes must be validated
against both fresh init and upgrade paths.
### Checklist: After Modifying A Runtime-Parsed Template
- [ ] Identify every runtime parser that reads the template, not just the file
writer that installs it
- [ ] Check whether relevant syntax lives outside obvious managed regions
such as tag blocks
- [ ] Verify fresh `init` output and a versioned `update` scenario that writes
the older `.trellis/.version`
- [ ] Add an upgrade regression using an older pristine template fixture, then
assert the installed file reaches the current packaged shape
- [ ] Update the backend spec that owns the runtime contract
**Real-world example**: Codex inline mode changed workflow platform markers from
`[Codex]` / `[Kilo, Antigravity, Windsurf]` to `[codex-sub-agent]` /
`[codex-inline, Kilo, Antigravity, Windsurf]`. Fresh init was correct, but
`trellis update` only merged `[workflow-state:*]` blocks and preserved stale
markers outside those blocks. Result: upgraded projects got new hook scripts
but old workflow routing, so `get_context.py --mode phase --platform codex`
could return empty Phase 2.1 detail.
---
## Mode-Detection Probe Checklist
When a CLI auto-detects a mode by probing a remote resource (e.g., checking if `index.json` exists to decide marketplace vs direct download):
### Before implementing:
- [ ] Probe runs in **ALL** code paths that use the result (interactive, `-y`, `--flag` combos)
- [ ] 404 vs transient error are distinguished — don't treat both as "not found"
- [ ] Transient errors **abort or retry**, never silently switch modes
- [ ] Shared state (caches, prefetched data) is **reset** when context changes (e.g., user switches source)
- [ ] **Shortcut paths** (e.g., `--template` skipping picker) must have the same error-handling quality as the probed path — check that downstream functions don't call catch-all wrappers
### After implementing:
- [ ] Trace every path from probe result to the mode-decision branch — no fallthrough
- [ ] External format contracts (giget URI, raw URLs) are tested or at least documented as comments
- [ ] Metadata reads consume a complete response or use a streaming parser — never parse a fixed-size prefix as full JSON
- [ ] When reconstructing a composite identifier from parsed parts, verify **all** fields are included and in the **correct position** (e.g., `provider:repo/path#ref` not `provider:repo#ref/path`)
- [ ] Verify that **action functions** called after a shortcut don't internally use the old catch-all fetch — they must use the probe-quality variant when error distinction matters
**Real-world example**: Custom registry flow had 8 bugs across 3 review rounds: (1) probe only ran in interactive mode, (2) transient errors fell through to wrong mode, (3) giget URI had `#ref` in wrong position, (4) prefetched templates leaked across source switches, (5) `--template` shortcut bypassed probe but `downloadTemplateById` internally used catch-all `fetchTemplateIndex`, turning timeouts into "Template not found".
**Real-world example**: Agent-session update hints fetched npm `latest` metadata with `response.read(4096)` and then parsed it as complete JSON. The `@mindfoldhq/trellis` package metadata exceeded 4 KB, so the JSON was truncated, parse failed silently, and the first session injection showed no update hint. Fix: read the complete response before parsing, and add a regression where `version` is followed by an 8 KB metadata tail.
---
## When to Create Flow Documentation
Create detailed flow docs when:
- Feature spans 3+ layers
- Multiple teams are involved
- Data format is complex
- Feature has caused bugs before
---
## Event Log / Projection Boundary
Append-only logs are cross-layer contracts. A single event travels through:
```
CLI input → event writer → events.jsonl → reader → filter → reducer → display
```
### Checklist: After Adding A New Event Kind Or Field
- [ ] Add the event kind to the central event taxonomy
- [ ] Add a typed event variant or type guard at the event layer
- [ ] Add normalization helpers for array/object fields that come from
user input or JSON
- [ ] Keep `seq` / `id` assignment in the event writer only
- [ ] Make filters and reducers consume the typed event guard, not local casts
- [ ] Make display code consume reducer output or typed events, not raw JSON
- [ ] Add at least one regression that proves history replay and live filtering
use the same filter model
**Real-world example**: Thread channels added `kind: "thread"`, `description`,
`context`, labels, and `lastSeq`. The first implementation replayed thread
state correctly, but several commands still re-parsed event payload fields with
local casts. The fix was to make the core event layer own `ThreadChannelEvent`
and `isThreadEvent`, make `reduceChannelMetadata` the only channel metadata
projection, and make `reduceThreads` the only thread replay reducer.
提交前至少回答：输入在哪里解析？错误在哪里转换？状态由谁拥有？字段是否在后端、客户端类型和 UI 测试中一致？如果答案不清楚，先补充契约或拆分边界，再实现功能。
.trellis/spec/guides/index.md
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# Thinking Guides
# 跨层思考指南
> **Purpose**: Expand your thinking to catch things you might not have considered.
这些指南用于本仓库中跨模块的设计检查，不替代 backend/frontend 的具体规格。
---
| 指南 | 触发场景 |
| --- | --- |
| [代码复用](./code-reuse-thinking-guide.md) | 要新增 helper、常量、UI primitive、query key 或重复转换时 |
| [跨层契约](./cross-layer-thinking-guide.md) | 变更后端 HTTP、前端 API 类型、query 或页面状态时 |
## Why Thinking Guides?
**Most bugs and tech debt come from "didn't think of that"**, not from lack of skill:
- Didn't think about what happens at layer boundaries → cross-layer bugs
- Didn't think about code patterns repeating → duplicated code everywhere
- Didn't think about edge cases → runtime errors
- Didn't think about future maintainers → unreadable code
These guides help you **ask the right questions before coding**.
---
## Available Guides
| Guide | Purpose | When to Use |
|-------|---------|-------------|
| [Code Reuse Thinking Guide](./code-reuse-thinking-guide.md) | Identify patterns and reduce duplication | When you notice repeated patterns |
| [Cross-Layer Thinking Guide](./cross-layer-thinking-guide.md) | Think through data flow across layers | Features spanning multiple layers |
---
## Quick Reference: Thinking Triggers
### When to Think About Cross-Layer Issues
- [ ] Feature touches 3+ layers (API, Service, Component, Database)
- [ ] Data format changes between layers
- [ ] Multiple consumers need the same data
- [ ] You're not sure where to put some logic
- [ ] You are adding an event kind, JSONL record, RPC payload, or config field
- [ ] UI / command code starts casting raw payload fields directly
→ Read [Cross-Layer Thinking Guide](./cross-layer-thinking-guide.md)
### When to Think About Code Reuse
- [ ] You're writing similar code to something that exists
- [ ] You see the same pattern repeated 3+ times
- [ ] You're adding a new field to multiple places
- [ ] **You're modifying any constant or config**
- [ ] **You're creating a new utility/helper function** ← Search first!
- [ ] Two files read the same untyped payload field with local casts
- [ ] Multiple branches update the same derived state from `kind` / `action`
→ Read [Code Reuse Thinking Guide](./code-reuse-thinking-guide.md)
### When Verifying AI Cross-Review Results
- [ ] Reviewer claims "user input can be malicious" → Check the actual data source (internal manifest? user config? external API?)
- [ ] Reviewer flags "missing validation" → Is the data from a trusted internal source?
- [ ] Reviewer says "behavior change" → Read the code comments — is it intentional design?
- [ ] Reviewer identifies a "bug" in test → Mentally delete the feature being tested — does the test still pass? If yes → tautological test
**Common AI reviewer false-positive patterns**:
1. **Trust boundary confusion**: Treating internal data (bundled JSON manifests) as untrusted external input
2. **Ignoring design comments**: Flagging intentional behavior documented in code comments as bugs
3. **Variable misreading**: Not tracing a variable to its actual definition (e.g., Map keyed by path vs name)
**Verification rule**: Every CRITICAL/WARNING finding must be verified against the actual code before prioritizing. Budget ~35% false-positive rate for AI reviews.
---
## Pre-Modification Rule (CRITICAL)
> **Before changing ANY value, ALWAYS search first!**
```bash
# Search for the value you're about to change
grep -r "value_to_change" .
```
This single habit prevents most "forgot to update X" bugs.
---
## How to Use This Directory
1. **Before coding**: Skim the relevant thinking guide
2. **During coding**: If something feels repetitive or complex, check the guides
3. **After bugs**: Add new insights to the relevant guide (learn from mistakes)
---
## Contributing
Found a new "didn't think of that" moment? Add it to the relevant guide.
---
**Core Principle**: 30 minutes of thinking saves 3 hours of debugging.
每次修改先用源码和测试验证假设，再更新对应规格；不要把通用框架偏好写成项目规则。