feat(memos-local-plugin): one-round-one-card UI + language-aware knowledge + L2/L3 boundary prompts

[hijzy]

Summary

This PR continues the v2 Reflect2Evolve plugin work merged in #1515 with three orthogonal improvements that landed together because they share the same trace fixtures and tests:

1. UI: one user turn = one memory card — frontend collapses sub-step rows by (episodeId, turnId). Algorithm layer (V/α backprop, L2 induction, Tier-2 retrieval, Decision Repair) keeps step-level granularity per V7 §0.1.
2. Knowledge generation in user's language — every L1/L2/L3/Skill/reflection generation site now detects the dominant language of its evidence and emits a languageSteeringLine so a Chinese user no longer gets half-English memos.
3. L2 / L3 prompts: hard boundary against drift — L2_INDUCTION_PROMPT and L3_ABSTRACTION_PROMPT bumped v1 → v2 with explicit "what NOT to write" guards plus same-fact-two-framings examples to keep procedural ↔ declarative knowledge cleanly separated.

Plus two infrastructure fixes the v2 plugin needed to actually run on better-sqlite3 ≥ v11 (defensive-mode block on sqlite_master) and a documentation alignment doc explaining the 小步/轮/任务 + 经验/环境认知/技能 mental model so future contributors stop conflating UI, storage, and algorithm granularities.

What changed

traces.turn_id + per-turn UI grouping

• New migration 013-trace-turn-id.sql: adds turn_id INTEGER + idx_traces_episode_turn index.
• step-extractor.ts stamps every sub-step from the same user message with the user turn's ts as meta.turnId; capture.ts::pickTurnId threads it into traces.turn_id.
• MemoriesView.tsx introduces MemoryGroup aggregation + <StepList> drawer so a 5-tool turn renders as one card with five collapsible step blocks (each carrying its own V / α / reflection / toolCalls), instead of five sibling cards. Bulk select / delete / share / export operate at card level.
• DB rows from before this migration get NULL turn_id and fall back to per-row rendering.

Language-aware knowledge generation

• core/llm/prompts/index.ts: new detectDominantLanguage(samples, {minSignal}) — counts CJK ideographs vs ASCII letters, returns "zh" | "en" | "auto". Allocation-free, runs on every gen call.
• All five gen sites inject languageSteeringLine:
  • capture/alpha-scorer.ts — reflection-quality reason
  • capture/batch-scorer.ts — per-step batch reflections
  • memory/l2/induce.ts — L2 policy fields
  • memory/l3/abstract.ts — L3 (ℰ, ℐ, C) bullets
  • skill/crystallize.ts — skill body + scope

L2 / L3 boundary prompts (v1 → v2)

• L2_INDUCTION_PROMPT: new "Boundaries — what NOT to write" section explicitly rejects environment topology / declarative behavioural rules / generic taboos. Includes same-fact-two-framings example (procedural vs declarative for the same underlying truth).
• L3_ABSTRACTION_PROMPT: bans imperative verbs (do / should / use / install / run) under any of ℰ/ℐ/C. All three example sets rewritten as pure declarative ("loading a glibc-linked binary wheel inside Alpine raises a dynamic-link error" instead of "if pip fails, install dev libs and retry").
• Test mock keys updated v1 → v2; historical inducedBy audit strings intentionally left at v1 (they record the prompt version a row was generated under, not a call-time match key).

Retrieval injector heading hierarchy

• # User's conversation history (from memory system) is H1; ## Memories / ## Skills / ## Environment Knowledge are H2 — restores the visual outline the LLM consumes.

Migration runner: better-sqlite3 ≥ v11 compatibility

• runMigrations now flips db.raw.unsafeMode(true) at the outer boundary if any pending migration uses PRAGMA writable_schema (resets in finally). Migration 012 (status unification) needs this to swap CHECK constraints in-place; defensive mode otherwise blocked it at runtime.
• Migration 012 SQL uses single-quote literals with doubled inner quotes (was double-quoted, which strict mode treats as identifiers).

Documentation

• New docs/GRANULARITY-AND-MEMORY-LAYERS.md (~365 lines, zh-CN) — the foundational mental-model doc that should be read before any other algorithm doc:
  • 小步 / 轮 / 任务 三个交互粒度 + 与代码层的对应
  • 打分粒度（每步 α/V，每任务 R_human，"轮"无独立分）
  • 检索粒度（技能/单步/子任务序列/环境认知，没有"按轮"召回 + 三层判别）
  • "结构性不确定" vs "操作性疑问" 判别表
  • 经验 / 环境认知 / 技能 五者关系
  • §6 "经验 vs 环境认知 边界裁剪"：7 条不该合并的理由 + 三种折中方案对比 + 7 维度判别速查 + 同事实多框架对照表 + 反例
• docs/Reflect2Skill_算法设计核心.md 头部加阅读顺序提示。
• docs/README.md 索引同步更新。

Algorithm alignment

Per V7 §0.1, the L1 trace is the minimum learning unit and stays step-level — one tool call → one trace, one final reply → one trace. The "one round = one memory" view is purely a frontend display concern using turn_id as a stable group key. Reflection-weighted backprop, cross-task L2 association, error-signature retrieval, and Decision Repair all continue to operate per-step. Documented end-to-end in the new GRANULARITY doc §6.

Test plan

• npx vitest run tests/unit/capture/step-extractor.test.ts — turnId stamped on every sub-step, multi-tool turn shares one turnId (11/11 pass)
• npx vitest run tests/unit/memory/l2/ tests/unit/memory/l3/ tests/unit/llm/prompts.test.ts — prompt v2 mock keys + L2/L3 induction (74/74 pass)
• npx vitest run tests/unit/storage/ — migration 013 applies cleanly (106/106 pass)
• npx vitest run tests/unit/ — full unit sweep: 802/806 pass; 4 failures are pre-existing on main (mock LLM behavior in reward integration + an outdated capture.lite.done event-list assertion), unchanged by this PR
• Local install via bash install.sh --version ./memtensor-memos-local-plugin-2.0.0-beta.1.tgz: gateway + viewer come up clean, traces.turn_id column present, migration 013 logged as applied
• Manual end-to-end: ran a 3-tool query in OpenClaw, verified the memory page shows ONE card with 工具 · 4 步 chip, drawer expands into 4 collapsible step sections with per-step V/α/thinking/tool I-O

Notes

• No backward compat for the schema change is required — fresh installs run all 13 migrations on first open. Existing local DBs auto-pick up 013 the next time the gateway opens them.
• Only apps/memos-local-plugin/ is touched. No changes to other packages.