feat: 加 prompt-stats 脚本，对比某天前后学生和 AI 对话的情况

轮数分布、层级分布、第 2 轮起的 L1、只粘贴外部代码的比例、“出问题了”“帮我写清楚”
和交作品反思的使用情况。只读。

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

CLAUDE.md

@@ -19,7 +19,7 @@ The backend was rewritten from Django in 2026-10 to cut memory use (~25 MB vs. 1
   - Who can read a submission (`canView` in `routes/submission.ts`): staff, the owner, anyone if it's in the showcase, or any student who has submitted the same task (any class). Students' `GET /submission/` only lists their own. Peer works and the random-rating item are anonymous until the viewer rates them (`PUT /:id/score` returns the author). `flag` now only carries `present` (teacher's in-class presentation queue); the old four colors are no longer shown
   - `src/services/` — `score.ts` (rating weights super×0.5, admin×0.3, normal×0.2, then Bayesian smoothing with C=3 and zones), `gradebook.ts`, `llm.ts` (OpenAI-compatible streaming via fetch; DeepSeek default, ARK for doubao models; chat context is the last 20 messages with code kept only in the newest reply that has code. Chat replies are written as **做了什么 / 我替你定的 / 想一想** — the decisions the AI made for the student and one open question, to prompt the next round; a reply to a plain question has no code and the WebSocket sends `code: null`. For challenges the AI also gets the task brief (`challengeBrief`) as a checklist only (`BRIEF_RULES`): it builds just what the student asked for and asks about one missing requirement in “想一想”; creations get no brief. The “帮我写清楚” coach gets the task brief, recent prompts and the last AI explanation from `guidanceContext` in `services/prompt.ts`, and judges by what the student added beyond the brief, like the classifier), `classifier.ts` (Bloom-level L1–L6 prompt tagging, runs in background; the model sees the task brief and the previous prompt, temperature 0, prompts under 4 chars get no level), `cascade.ts` (Django's CASCADE deletes done by hand, plus the shared delete rules: `hasRatingsOrAwards`, `deleteRound`), `pyround.ts` (`pyRound`/`pySum` reproduce Python's `round()` and `sum()` so scores match the old system bit-for-bit)
   - `src/ws.ts` — `/ws/prompt/<task_id>/?token=<access_token>` (code generation chat; the AI context and the page's chat include `conversation`, `edit` and `manual` messages) and `/ws/guidance/<task_id>/?token=...` (prompt coaching); one message per connection at a time (a message sent while the AI is still replying gets an error, payloads capped at 64 KB); on disconnect the pending user message is deleted and the LLM request aborted
-  - `scripts/` — one-off CLIs: `create-superuser.ts`, `classify-prompts.ts`, `recalculate-scores.ts`
+  - `scripts/` — one-off CLIs: `create-superuser.ts`, `classify-prompts.ts`, `recalculate-scores.ts`, `prompt-stats.ts` (read-only before/after comparison of AI-chat behavior: rounds, levels, external-code share, “出问题了” / reflection usage)
   - Roles: `super`, `admin`, `normal`. Task model is multi-table: `task_task` + one of `task_tutorial` / `task_challenge` / `task_creation` (`task_ptr_id`)
 
 - **`client/`** — Vue 3 + TypeScript SPA built with **Vite**; Bun is the package manager and runtime (no Node/npm)
@@ -62,6 +62,7 @@ bun run typecheck
 bun scripts/create-superuser.ts <username> <password>
 bun scripts/classify-prompts.ts [--task-id N] [--force] [--dry-run]
 bun scripts/recalculate-scores.ts [--dry-run]
+bun scripts/prompt-stats.ts <split-date> [--from date] [--to date]   # dates in Beijing time
 ```
 Config comes from `server/.env` (see `server/.env.example`); dev DB is `server/data/db.sqlite3`, uploads go to the repo-root `media/`.
 

server/scripts/prompt-stats.ts

@@ -0,0 +1,109 @@
+/**
+ * 对比某个日期前后，学生和 AI 对话的情况（比如改了 AI 的回复方式以后有没有变化）。
+ * 日期按北京时间，分界日期那天算“之后”。只读，不改数据。
+ *
+ *   bun scripts/prompt-stats.ts <分界日期> [--from 日期] [--to 日期]
+ *   bun scripts/prompt-stats.ts 2026-10-28 --from 2026-09-01
+ *
+ * 层级是打标签时的标准；分类标准改过以后，先跑 classify-prompts.ts --force 重打，前后才好比。
+ */
+import { all, toDbTime } from "../src/db"
+
+const args = process.argv.slice(2)
+const opt = (name: string) => {
+  const i = args.indexOf(name)
+  return i >= 0 ? args[i + 1] : undefined
+}
+const split = args[0]
+if (!split || !/^\d{4}-\d{2}-\d{2}$/.test(split)) {
+  console.log("用法：bun scripts/prompt-stats.ts <分界日期> [--from 日期] [--to 日期]")
+  process.exit(1)
+}
+const day = (d: string) => toDbTime(new Date(`${d}T00:00:00+08:00`))
+const nextDay = (d: string) => toDbTime(new Date(new Date(`${d}T00:00:00+08:00`).getTime() + 86_400_000))
+const periods = [
+  { name: `${opt("--from") ?? "最早"} ~ ${split} 之前`, from: opt("--from") ? day(opt("--from")!) : "", to: day(split) },
+  { name: `${split} ~ ${opt("--to") ?? "现在"}`, from: day(split), to: opt("--to") ? nextDay(opt("--to")!) : "9999" },
+]
+
+// 和 PromptPanel 里一样：只说“不行”“没反应”的短抱怨；“出问题了”拼出来的句子
+const VAGUE =
+  /不行|不对|没反应|没用|没效果|用不了|不能用|不能玩|动不了|进不去|打不开|没显示|不显示|显示不出|bug|出错|报错|错了|坏了|白屏|空白|有问题/i
+const PROBLEM = /^我.+，(想看到.+，)?但实际.+。$/s
+
+type Msg = {
+  conversation_id: string
+  source: string
+  content: string
+  prompt_level: number | null
+  created: string
+  // 在这个对话的 AI 对话里是第几轮（只数 conversation）
+  round: number | null
+}
+
+// 批处理脚本，整表扫一遍没关系
+const msgs = all<Msg>(
+  `SELECT conversation_id, source, content, prompt_level, created,
+     CASE WHEN source = 'conversation'
+       THEN ROW_NUMBER() OVER (PARTITION BY conversation_id, source = 'conversation' ORDER BY created, id) END AS round
+   FROM prompt_message WHERE role = 'user'`,
+)
+const subs = all<{ created: string; reflected: number }>(
+  `SELECT s.created, r.submission_id IS NOT NULL AS reflected
+   FROM submission_submission s JOIN task_task t ON t.id = s.task_id
+   LEFT JOIN submission_reflection r ON r.submission_id = s.id
+   WHERE t.task_type = 'challenge'`,
+)
+
+const pct = (n: number, d: number) => (d ? `${((n / d) * 100).toFixed(1)}%` : "—")
+const chars = (s: string) => [...s.trim()].length
+
+function stats(from: string, to: string) {
+  const inRange = (t: string) => t >= from && t < to
+  const ms = msgs.filter((m) => inRange(m.created))
+  const byConv = new Map<string, Msg[]>()
+  for (const m of ms) byConv.set(m.conversation_id, [...(byConv.get(m.conversation_id) ?? []), m])
+
+  const convs = [...byConv.values()]
+  const ai = convs.filter((c) => c.some((m) => m.source === "conversation"))
+  const manualOnly = convs.filter((c) => c.some((m) => m.source === "manual") && !c.some((m) => m.source === "conversation"))
+  const rounds = ai.map((c) => c.filter((m) => m.source === "conversation").length)
+  const bucket = (lo: number, hi: number) => rounds.filter((n) => n >= lo && n <= hi).length
+
+  const asked = ms.filter((m) => m.source === "conversation")
+  const levels = [1, 2, 3, 4, 5, 6].map((l) => asked.filter((m) => m.prompt_level === l).length)
+  const leveled = levels.reduce((a, b) => a + b, 0)
+  const later = asked.filter((m) => (m.round ?? 1) >= 2 && m.prompt_level)
+  const vague = asked.filter((m) => chars(m.content) <= 15 && VAGUE.test(m.content))
+  const problem = asked.filter((m) => PROBLEM.test(m.content.trim()))
+  const manual = ms.filter((m) => m.source === "manual")
+  const s = subs.filter((x) => inRange(x.created))
+
+  return [
+    ["有对话或版本的作品", `${convs.length}`],
+    ["只粘贴外部代码", `${manualOnly.length}（${pct(manualOnly.length, convs.length)}）`],
+    ["和平台 AI 对话的作品", `${ai.length}`],
+    ["  平均轮数", ai.length ? (rounds.reduce((a, b) => a + b, 0) / ai.length).toFixed(2) : "—"],
+    ["  1 轮", pct(bucket(1, 1), ai.length)],
+    ["  2–3 轮", pct(bucket(2, 3), ai.length)],
+    ["  4–6 轮", pct(bucket(4, 6), ai.length)],
+    ["  7 轮以上", pct(bucket(7, Infinity), ai.length)],
+    ["提问（AI 对话）", `${asked.length}，已分级 ${leveled}`],
+    ...levels.map((n, i) => [`  L${i + 1}`, pct(n, leveled)]),
+    ["  L4–L6", pct(levels[3]! + levels[4]! + levels[5]!, leveled)],
+    ["  第 2 轮起的 L1", pct(later.filter((m) => m.prompt_level === 1).length, later.length)],
+    ["  只说“不行”的短抱怨", `${vague.length}（${pct(vague.length, asked.length)}）`],
+    ["  用“出问题了”", `${problem.length}（${pct(problem.length, asked.length)}）`],
+    ["用了“帮我写清楚”的作品", `${convs.filter((c) => c.some((m) => m.source === "guidance")).length}（占和 AI 对话的 ${pct(ai.filter((c) => c.some((m) => m.source === "guidance")).length, ai.length)}）`],
+    ["外部 AI 提示词", `${manual.length}，12 字以内 ${pct(manual.filter((m) => chars(m.content) <= 12).length, manual.length)}`],
+    ["挑战提交", `${s.length}，写了反思 ${pct(s.filter((x) => x.reflected).length, s.length)}`],
+  ]
+}
+
+const [a, b] = periods.map((p) => stats(p.from, p.to))
+// 终端里中文占两格
+const cols = (k: string) => [...k].reduce((n, ch) => n + (ch.codePointAt(0)! >= 0x2e80 ? 2 : 1), 0)
+const width = Math.max(...a!.map(([k]) => cols(k!))) + 2
+const pad = (k: string) => k + " ".repeat(width - cols(k))
+console.log(`${pad("")}${periods[0]!.name}  →  ${periods[1]!.name}`)
+for (const [i, [k, v]] of a!.entries()) console.log(`${pad(k!)}${v}  →  ${b![i]![1]}`)