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feat: refresh course through workflow and goal loops

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@@ -1,45 +1,47 @@
# s08: Context Compact — 上下文总会满，要有办法腾地方
# s08: Context Compact — Context Will Fill Up, Have a Way to Make Room
[中文](README.md) · [English](README.en.md) · [日本語](README.ja.md)
[English](README.md) · [中文](README.zh.md) · [日本語](README.ja.md)
s01 → s02 → s03 → s04 → s05 → s06 → s07 → `s08` → [s09](../s09_memory/) → s10 → ... → s20
> *"上下文总会满, 要有办法腾地方"* — 四层压缩策略, 便宜的先跑贵的后跑。
s01 → s02 → s03 → s04 → s05 → s06 → s07 → `s08` → [s09](../s09_memory/) → s10 → ... → s20 → s21 → s22
> *"Context will fill up — have a way to make room"* — Four-layer compression pipeline: cheap first, expensive last.
>
> **Harness 层**: 压缩 — 干净的记忆, 无限的会话。
> **Harness Layer**: Compression — clean memory, unlimited sessions.
---
## 问题
## The Problem
Agent 跑着跑着，不动了。
The agent is running along, then freezes.
手里有 bash、有 read、有 write，能力是够的。但它读了一个 1000 行的文件（~4000 token），又读了 30 个文件，跑了 20 条命令。每条命令的输出、每个文件的内容，全都堆在 `messages` 列表里。
It has bash, read, write — all the capabilities it needs. But it read a 1000-line file (~4000 tokens), then read 30 more files, ran 20 commands. Every command's output, every file's contents, all pile up in the `messages` list.
上下文窗口是有限的。满了之后，API 直接拒绝：`prompt_too_long`。
The context window is finite. Once full, the API outright rejects the call: `prompt_too_long`.
不压缩，Agent 根本没法在大项目里干活。
Without compression, an agent simply cannot work on large projects.
---
## 解决方案
## The Solution
![Compact Overview](images/compact-overview.svg)
![Compact Overview](images/compact-overview.en.svg)
保留 s07 的 hook 结构、技能加载、子 Agent 等骨架，省略部分工具细节以聚焦压缩。核心变动：每轮 LLM 调用前插入三层预处理器（0 API），token 仍超阈值时触发 LLM 摘要（1 API），API 报错时应急裁剪。
The hook structure, skill loading, and sub-Agent from s07 are preserved, with some tools omitted to focus on compaction. The core change: insert three pre-processors (0 API calls) before each LLM call, trigger an LLM summary (1 API call) when tokens still exceed the threshold, and emergency-trim if the API throws an error.
核心设计：便宜的先跑，贵的后跑。
Core design: cheap first, expensive last.
> **Boundary with s09:** s08 manages the current session's finite context and may lose detail while compressing it. s09 adds a separate durable store for selected information that must survive compaction and future sessions. They solve different failure modes, so they remain separate lessons.
---
## 工作原理
## How It Works
![四层压缩管线](images/compaction-layers.svg)
![Four-layer compression pipeline](images/compaction-layers.en.svg)
### L1: snip_compact — 裁掉无关的旧对话
### L1: snip_compact — Trim Irrelevant Old Conversation
Agent 跑了 80 轮对话，`messages` 攒了 160 条。最前面的"帮我创建 hello.py"和当前工作几乎无关了，但全占着位置。
The agent ran 80 turns of conversation, accumulating 160 `messages`. The very first "help me create hello.py" is barely relevant to current work, yet it still occupies space.
消息数超过 50 条 → 保留头部 3 条（初始上下文）和尾部 47 条（当前工作），中间裁掉；唯一额外边界条件是，不能把 `assistant(tool_use)` 和后面的 `user(tool_result)` 拆开：
Message count exceeds 50 → keep the first 3 (initial context) and the last 47 (current work), trim the middle; the only extra boundary rule is that `assistant(tool_use)` must not be separated from the following `user(tool_result)`:
```python
def snip_compact(messages, max_messages=50):
@@ -58,15 +60,15 @@ def snip_compact(messages, max_messages=50):
return messages[:head_end] + [placeholder] + messages[tail_start:]
```
裁掉的是消息本身，只是在切口处多做一步保护；剩下的消息里 `tool_result` 内容仍在累积——第 34 条消息里可能躺着 30KB 的旧文件内容。→ L2。
Messages are still trimmed directly; this just adds one boundary guard. `tool_result` content within remaining messages still keeps accumulating — message #34 may still hold 30KB of old file contents. → L2.
### L2: micro_compact — 旧工具结果占位
### L2: micro_compact — Placeholder for Old Tool Results
![旧结果占位](images/micro-compact.svg)
![Old results placeholder](images/micro-compact.en.svg)
Agent 连续读了 10 个文件。第 1-7 次的完整内容还躺在上下文里，早就不需要了，但占着大量空间。
The agent read 10 files consecutively. The full contents of reads 1–7 are still sitting in context, no longer needed, but hogging large amounts of space.
只保留最近 3 条 `tool_result` 的完整内容，更旧的替换为一行占位符：
Keep only the 3 most recent `tool_result` entries intact; replace older ones with a one-line placeholder:
```python
KEEP_RECENT_TOOL_RESULTS = 3
@@ -81,15 +83,15 @@ def micro_compact(messages):
return messages
```
旧结果清掉了，但单条新结果可能就有 500KB——一个 `cat` 大文件的输出就能打满上下文。→ L3。
Old results are cleared, but a single new result can be 500KB — one `cat` of a large file can max out the context. → L3.
### L3: tool_result_budget — 大结果落盘
### L3: tool_result_budget — Persist Large Results to Disk
![大结果落盘](images/layer1-budget.svg)
![Large results to disk](images/layer1-budget.en.svg)
模型一次读了 5 个大文件，单条 user 消息里所有 `tool_result` 加起来 500KB。
The model read 5 large files in one go; all `tool_result` blocks in the last user message total 500KB.
统计最后一条 user 消息里所有 `tool_result` 的总大小。超过 200KB → 按大小排序，从最大的开始落盘到 `.task_outputs/tool-results/`，上下文里只留 `<persisted-output>` 标记 + 前 2000 字符预览。模型看到标记后知道完整内容在磁盘上，需要时可以重新读。
Sum the size of all `tool_result` blocks in the last user message. If over 200KB → sort by size, starting from the largest, persist to `.task_outputs/tool-results/`, keeping only a `<persisted-output>` marker + a 2000-character preview in context. The model sees the marker and knows the full content is on disk, re-reading it when needed.
```python
def tool_result_budget(messages, max_bytes=200_000):
@@ -108,35 +110,35 @@ def tool_result_budget(messages, max_bytes=200_000):
return messages
```
前三层都是纯文本/结构操作，0 API 调用，但也无法"理解"对话内容。上下文可能仍然太大。→ L4。
The first three layers are all plain-text / structural operations — 0 API calls — but they cannot "understand" conversation content. Context may still be too large. → L4.
### L4: compact_history — LLM 全量摘要
### L4: compact_history — Full LLM Summary
![LLM 全量摘要](images/auto-compact.svg)
![Full LLM summary](images/auto-compact.en.svg)
前三层全跑完了，但在超大项目中连续工作 30 分钟后，token 仍然超过阈值。
All three previous layers have run, but after 30 minutes of continuous work on a huge project, tokens still exceed the threshold.
三步流程：
Three-step process:
1. **保存 transcript**：完整对话写入 `.transcripts/`，JSONL 格式。transcript 保留了可恢复记录，但模型的活跃上下文里只剩摘要。对模型当下推理来说，细节已经不在上下文中了。教学代码没有提供 transcript 检索工具。
2. **LLM 生成摘要**：把对话历史发给 LLM，要求保留当前目标、重要发现、已改文件、剩余工作、用户约束等关键信息。
3. **替换消息列表**：所有旧消息被替换为一条摘要。教学版只保留摘要；真实 Claude Code 会在 compact 后重新附加部分最近文件、计划、agent/skill/tool 等上下文。
1. **Save transcript**: Write the full conversation to `.transcripts/` in JSONL format. The transcript preserves a recoverable record, but the model's active context only contains the summary. For the model's current reasoning, the details are no longer in context. The teaching code does not provide a transcript retrieval tool.
2. **LLM generates summary**: Send conversation history to the LLM, asking it to preserve key information: current goals, important findings, modified files, remaining work, user constraints, etc.
3. **Replace message list**: All old messages are replaced with a single summary. The teaching version only keeps the summary; the real Claude Code re-attaches some recent files, plans, agent/skill/tool context after compaction.
```python
def compact_history(messages):
transcript_path = write_transcript(messages) # 先保存完整对话
summary = summarize_history(messages) # LLM 生成摘要
transcript_path = write_transcript(messages) # Save full conversation first
summary = summarize_history(messages) # LLM generates summary
return [{"role": "user",
"content": f"[Compacted]\n\n{summary}"}]
```
**熔断器**：连续失败 3 次后停止重试，防止死循环浪费 API 调用。
**Circuit breaker**: After 3 consecutive failures, stop retrying to prevent an infinite loop wasting API calls.
### 应急: reactive_compact
### Reactive: reactive_compact
有时候 API 还是返回 `prompt_too_long`（413），上下文增长速度快于压缩触发速度时。
Sometimes the API still returns `prompt_too_long` (413) — when context grows faster than compression triggers.
这时触发 **reactive_compact**：触发方式比 compact_history 更激进（API 报错后的应急手段），但压缩策略更温和，保留最近约 5 条原始消息，只总结较早历史。同样避免留下孤立 `tool_result`。
This triggers **reactive_compact**: more aggressive than compact_history in trigger (emergency response to a 413 error), but more conservative in what it removes, keeping ~5 recent messages and only summarizing earlier history. Still avoids an orphaned `tool_result`.
```python
def reactive_compact(messages):
@@ -151,21 +153,21 @@ def reactive_compact(messages):
"content": f"[Reactive compact]\n\n{summary}"}, *messages[tail_start:]]
```
reactive compact 有重试上限（默认 1 次）。再失败就抛出异常，不无限循环。完整的错误恢复逻辑留给 s11。
Reactive compact has a retry limit (default 1). If it still fails, an exception is raised instead of looping forever. Full error recovery is deferred to s11.
### 合起来跑
### Putting It All Together
```python
def agent_loop(messages):
reactive_retries = 0
while True:
# 三个预处理器（0 API 调用）
# 顺序：budget 先跑，确保大内容落盘后再做占位和裁剪
messages[:] = tool_result_budget(messages) # L3: 大结果落盘
messages[:] = snip_compact(messages) # L1: 裁中间
messages[:] = micro_compact(messages) # L2: 旧结果占位
# Three pre-processors (0 API calls)
# Order: budget first, so large content is persisted before placeholders
messages[:] = tool_result_budget(messages) # L3: persist large results
messages[:] = snip_compact(messages) # L1: trim middle
messages[:] = micro_compact(messages) # L2: old result placeholders
# 还不够？LLM 摘要（1 API 调用）
# Still too much? LLM summary (1 API call)
if estimate_token_count(messages) > THRESHOLD:
messages[:] = compact_history(messages)
@@ -173,137 +175,137 @@ def agent_loop(messages):
response = client.messages.create(...)
except PromptTooLongError:
if reactive_retries < MAX_REACTIVE_RETRIES:
messages[:] = reactive_compact(messages) # 应急
messages[:] = reactive_compact(messages) # Emergency
reactive_retries += 1
continue
raise # 超过重试上限，抛出异常
# ... 工具执行 ...
raise # retry limit exceeded, raise exception
# ... tool execution ...
# compact 工具：模型主动调用时触发 compact_history
# compact tool: when the model actively calls it, triggers compact_history
if block.name == "compact":
messages[:] = compact_history(messages)
results.append({..., "content": "[Compacted. History summarized.]"})
messages.append({"role": "user", "content": results})
break # 结束当前 turn，用压缩后的上下文开始新一轮
break # end current turn, start fresh with compacted context
```
**顺序不能换。** L3（budget）在 L2（micro）前面，因为 micro 会把旧的大 tool_result 替换成一行占位符，budget 必须在那之前把完整内容落盘。这也是为什么 CC 源码把 `applyToolResultBudget` 放在最前面。
**The order must not be swapped.** L3 (budget) runs before L2 (micro) because micro replaces old large tool_results with one-line placeholders — budget must persist the full content before that happens. This is why CC source puts `applyToolResultBudget` first.
---
## 相对 s07 的变更
## Changes From s07
| 组件 | 之前 (s07) | 之后 (s08) |
|------|-----------|-----------|
| 上下文管理 | 无（上下文无限膨胀） | 四层压缩管线 + 应急 |
| 新函数 | — | snip_compact, micro_compact, tool_result_budget, compact_history, reactive_compact |
| 工具 | bash, read, write, edit, glob, todo_write, task, load_skill (8) | 8 + compact (9) |
| 循环 | LLM 调用 → 工具执行 | 每轮前跑三层预处理器 + 阈值触发 compact_history |
| 设计原则 | — | 便宜的先跑，贵的后跑 |
| Component | Before (s07) | After (s08) |
|-----------|-------------|-------------|
| Context management | None (context grows unbounded) | Four-layer compression pipeline + emergency |
| New functions | — | snip_compact, micro_compact, tool_result_budget, compact_history, reactive_compact |
| Tools | bash, read_file, write_file, edit_file, glob, todo_write, task, load_skill (8) | 8 + compact (9) |
| Loop | LLM call → tool execution | Three pre-processors before each turn + threshold-triggered compact_history |
| Design principle | — | Cheap first, expensive last |
---
## 试一下
## Try It
```sh
cd learn-claude-code
python s08_context_compact/code.py
```
试试这些 prompt：
Try these prompts:
1. `Read the file README.md, then read code.py, then read s01_agent_loop/README.md`（连续读多个文件，观察 L2 压缩旧结果）
2. `Read every file in s08_context_compact/`（一次性读大量内容，观察 L3 落盘）
3. 反复对话 20+ 轮，观察是否出现 `[auto compact]` 或 `[reactive compact]`
1. `Read the file README.md, then read code.py, then read s01_agent_loop/README.md` (read multiple files consecutively, observe L2 compressing old results)
2. `Read every file in s08_context_compact/` (read a large amount of content at once, observe L3 persisting to disk)
3. Chat for 20+ turns, observe whether `[auto compact]` or `[reactive compact]` appears
观察重点：每次工具执行后，旧 tool_result 是否被压缩？连续对话后 token 超阈值时，是否自动触发了摘要？
What to watch for: After each tool execution, are old `tool_result` entries compressed? When tokens exceed the threshold after extended conversation, is summarization triggered automatically?
---
## 接下来
## What's Next
上下文压缩让 Agent 能跑很久不会崩。但每次压缩后，用户之前告诉它的偏好、约束也跟着丢了。能不能让 Agent 有选择地记住重要的事？
Context compression lets an agent run for a long time without crashing. But after each compression, the preferences and constraints the user told it are also lost. Can we let the agent selectively remember important things?
s09 Memory → 三个子系统：选择记什么、提取关键信息、整理巩固。跨压缩、跨会话。
s09 Memory → three subsystems: choosing what to remember, extracting key information, consolidating and organizing. Across compressions, across sessions.
<details>
<summary>深入 CC 源码</summary>
<summary>Deep Dive Into CC Source Code</summary>
> 以下基于 CC 源码 `compact.ts`、`autoCompact.ts`、`microCompact.ts`、`query.ts` 的分析。
> The following is based on analysis of CC source code `compact.ts`, `autoCompact.ts`, `microCompact.ts`, and `query.ts`.
### 执行顺序对照
### Execution Order Comparison
教学版为了讲解方便按 L1/L2/L3/L4 编号，但实际执行顺序和编号不完全对应：
The teaching version labels layers L1/L2/L3/L4 for pedagogical clarity, but actual execution order does not match the numbering:
| 维度 | 教学版 | Claude Code |
|------|--------|-------------|
| 执行顺序 | budget → snip → micro → auto | budget → snip → micro → collapse → auto（`query.ts:379-468`） |
| snip_compact | 保留头 3 + 尾 47 | CC 仅主线程启用；实现不在开源仓库中（`HISTORY_SNIP` feature gate），但接口可见：`snipCompactIfNeeded(messages)` → `{ messages, tokensFreed, boundaryMessage? }`，还暴露了 `SnipTool` 工具让模型主动调用。教学版的 3/47 是简化参数 |
| micro_compact | 文本占位符替换 | 两条路径：time-based 直接清内容，cached 走 API `cache_edits`（legacy path 已移除） |
| micro_compact 白名单 | 按位置（最近 3 条） | time-based 按时间阈值触发；cached 按计数触发（`microCompact.ts`） |
| tool_result_budget | 200KB 字符 | 200,000 字符（`toolLimits.ts:49`） |
| compact_history 阈值 | 字符数估算 | 精确 token：`contextWindow - maxOutputTokens - 13_000` |
| 摘要要求 | 5 类信息 | 9 个部分 + `<analysis>`/`<summary>` 双标签 |
| 压缩 prompt | 简单 prompt | 首尾双重防呆禁止调工具 |
| PTL retry | 有（简化） | `truncateHeadForPTLRetry()` 按消息组回退（`compact.ts:243-290`） |
| 后压缩恢复 | 无（教学版只保留摘要） | 自动重新读取最近文件、计划、agent/skill/tool 等 |
| 熔断器 | 3 次 | 3 次（`autoCompact.ts:70`） |
| reactive 重试 | 1 次 | CC 有更精细的分级重试 |
| Dimension | Teaching Version | Claude Code |
|-----------|-----------------|-------------|
| Execution order | budget → snip → micro → auto | budget → snip → micro → collapse → auto (`query.ts:379-468`) |
| snip_compact | Keep head 3 + tail 47 | CC only enables on main thread; implementation not in open-source repo (`HISTORY_SNIP` feature gate), but interface is visible: `snipCompactIfNeeded(messages)` → `{ messages, tokensFreed, boundaryMessage? }`, also exposes `SnipTool` for model-initiated snipping. Teaching version's 3/47 are simplified parameters |
| micro_compact | Text placeholder replacement | Two paths: time-based clears content directly, cached uses API `cache_edits` (legacy path removed) |
| micro_compact whitelist | By position (most recent 3) | time-based triggers by time threshold; cached triggers by count (`microCompact.ts`) |
| tool_result_budget | 200KB characters | 200,000 characters (`toolLimits.ts:49`) |
| compact_history threshold | Character count estimate | Precise tokens: `contextWindow - maxOutputTokens - 13_000` |
| Summary requirements | 5 categories of info | 9 sections + `<analysis>`/`<summary>` dual tags |
| Compression prompt | Simple prompt | Double-ended hard guardrails forbidding tool calls |
| PTL retry | Yes (simplified) | `truncateHeadForPTLRetry()` retreats by message groups (`compact.ts:243-290`) |
| Post-compaction recovery | None (teaching version only keeps summary) | Auto re-read recent files, plans, agent/skill/tool context |
| Circuit breaker | 3 times | 3 times (`autoCompact.ts:70`) |
| Reactive retry | 1 time | CC has more granular tiered retries |
### 执行顺序详解
### Execution Order Details
CC 源码 `query.ts` 中的真实顺序：
The real order in CC source `query.ts`:
1. `applyToolResultBudget`（L379）：先处理大结果，确保完整内容落盘
2. `snipCompact`（L403）：裁中间消息
3. `microcompact`（L414）：旧结果占位
4. `contextCollapse`（L441）：独立的上下文管理系统（教学版无）
5. `autoCompact`（L454）：LLM 全量摘要
1. `applyToolResultBudget` (L379): persist large results first, ensuring full content is saved
2. `snipCompact` (L403): trim middle messages
3. `microcompact` (L414): old result placeholders
4. `contextCollapse` (L441): independent context management system (not in teaching version)
5. `autoCompact` (L454): LLM full summary
教学版的 budget → snip → micro 顺序与此一致。教学版没有 contextCollapse 机制。
The teaching version's budget → snip → micro order matches this. The teaching version does not have the contextCollapse mechanism.
### read_file 的取舍
### read_file Trade-off
教学版的 `micro_compact` 会把旧 `tool_result` 统一替换成占位符，包括 `read_file`。这通常不影响功能正确性：如果后续还需要文件内容，模型可以重新读一次。代价是可能多一次工具调用，也可能降低 prompt cache 命中率。
The teaching version's `micro_compact` replaces old `tool_result` blocks with placeholders uniformly, including `read_file`. This usually does not affect functional correctness: if the model needs the file contents later, it can read the file again. The cost is an extra tool call and potentially lower prompt cache hit rates.
Claude Code 没有用教学版这种简单规则解决这个问题。它把 `Read` 也放进可 microcompact 的工具集合，但同时维护 `readFileState`：重复读取未变化文件时返回 `FILE_UNCHANGED_STUB`，compact 后再按预算恢复最近读过的文件内容（例如最多 5 个文件、每个 5K token、总预算 50K token）。这是生产级实现里的缓存和恢复机制，教学版不展开，保留“压缩旧结果，必要时重新读取”的简单 trade-off。
Claude Code does not solve this with the teaching version's simple rule. It also puts `Read` in the microcompactable tool set, but maintains a separate `readFileState`: repeated reads of unchanged files return `FILE_UNCHANGED_STUB`, and after compaction it restores recently read file contents within a budget (for example, up to 5 files, 5K tokens per file, 50K tokens total). That is a production-level cache and recovery mechanism. The teaching version does not expand into that machinery; it keeps the simpler trade-off of compacting old results and re-reading when needed.
### 完整常量参考
### Full Constant Reference
| 常量 | 值 | 源文件 |
|------|-----|--------|
| Constant | Value | Source File |
|----------|-------|-------------|
| `AUTOCOMPACT_BUFFER_TOKENS` | 13,000 | `autoCompact.ts:62` |
| `MAX_CONSECUTIVE_AUTOCOMPACT_FAILURES` | 3 | `autoCompact.ts:70` |
| `MAX_OUTPUT_TOKENS_FOR_SUMMARY` | 20,000 | `autoCompact.ts:30` |
| `POST_COMPACT_TOKEN_BUDGET` | 50,000 | `compact.ts:123` |
| `POST_COMPACT_MAX_FILES_TO_RESTORE` | 5 | `compact.ts:122` |
| `POST_COMPACT_MAX_TOKENS_PER_FILE` | 5,000 | `compact.ts:124` |
| 时间 micro_compact 间隔 | 60 分钟 | `timeBasedMCConfig.ts` |
| Time micro_compact interval | 60 minutes | `timeBasedMCConfig.ts` |
| `MAX_COMPACT_STREAMING_RETRIES` | 2 | `compact.ts:131` |
### contextCollapse 和 sessionMemoryCompact
### contextCollapse and sessionMemoryCompact
CC 源码中还有两个机制本教学版没有展开：
CC source code has two additional mechanisms not covered in this teaching version:
- **contextCollapse**：独立的上下文管理系统，启用时抑制 proactive autocompact（`autoCompact.ts:215-222`），由 collapse 的 commit/blocking 流程接管上下文管理。但 manual `/compact` 和 reactive fallback 仍是独立路径，不受 contextCollapse 影响。
- **sessionMemoryCompact**：compact_history 之前，CC 会先尝试用已有的 session memory（s09 会讲到）做轻量摘要，不调 LLM。这个机制等学完 s09 之后回头看会更清楚。
- **contextCollapse**: An independent context management system that, when enabled, suppresses proactive autocompact (`autoCompact.ts:215-222`), with collapse's commit/blocking flow taking over context management. Manual `/compact` and reactive fallback remain independent paths, unaffected by contextCollapse.
- **sessionMemoryCompact**: Before compact_history, CC first attempts a lightweight summary using existing session memory (covered in s09) without calling the LLM. This mechanism becomes clearer after learning s09.
### 压缩 prompt 长什么样？
### What Does the Compression Prompt Look Like?
CC 的压缩 prompt 有两个硬性要求：
CC's compression prompt has two hard requirements:
1. **绝对禁止调用工具**：开头就是 `CRITICAL: Respond with TEXT ONLY. Do NOT call any tools.`，末尾还会再 REMINDER 一次
2. **先分析再总结**：模型需要先在 `<analysis>` 标签里理清思路，然后在 `<summary>` 标签里输出正式摘要。analysis 在格式化时被剥离
1. **Absolutely no tool calls**: It begins with `CRITICAL: Respond with TEXT ONLY. Do NOT call any tools.`, and appends another REMINDER at the end
2. **Analyze first, then summarize**: The model must first reason in an `<analysis>` tag, then output the formal summary in a `<summary>` tag. The analysis is stripped during formatting
### 教学版的简化是刻意的
### Teaching Version Simplifications Are Intentional
- micro_compact 用文本占位 → 我们没有 API 层的 `cache_edits` 权限
- read_file 不特殊处理 → 教学版接受必要时重新读取，避免引入 readFileState 和后压缩恢复机制
- token 用字符数估算 → 精确 tokenizer 不在教学范围内
- 后压缩恢复省略 → 教学版只保留摘要，不自动重新附加文件
- 两个辅助机制不展开 → 属于 10% 的细节
- micro_compact uses text placeholders → we don't have API-level `cache_edits` access
- read_file is not special-cased → the teaching version accepts re-reading when needed instead of introducing readFileState and post-compaction recovery
- Tokens estimated via character count → precise tokenizers are out of scope
- Post-compaction recovery omitted → teaching version only keeps summary, does not auto re-attach files
- Two auxiliary mechanisms not covered → they fall in the 10% detail category
核心设计思想，便宜的先跑贵的后跑，完整保留。
The core design principle, cheap first, expensive last, is fully preserved.
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