Fix #2318: ### Pre-submission checklist

apps/memos-local-plugin/core/llm/prompts/l2-induction.ts

@@ -11,15 +11,22 @@ import type { PromptDef } from "./index.js";
  * Boundary contract (see `docs/GRANULARITY-AND-MEMORY-LAYERS.md` §6):
  * an L2 policy is **procedural** ("how to do it") — it MUST contain an
  * action template. Anything declarative ("the environment looks like X")
- * belongs to the L3 world model, not here. The system prompt explicitly
- * rejects environment-fact drift to keep the two layers semantically
- * orthogonal. Bumping the version to v2 captures that change.
+ * belongs to the L3 world model, not here.
+ *
+ * v3 (issue #2318): adds a second boundary against **conversational acts**
+ * (asking, confirming, notifying, reporting status) leaking into the
+ * `action` field. Such policies passed schema validation but crystallised
+ * into dead skills — always retrieved, never callable. The revised prompt
+ * (a) enumerates the dialogue verbs to reject and (b) tells the model to
+ * abstain when the cluster's only shared behaviour is dialogue.
+ *
+ * v2 history: added the L3 world-model drift guard.
  */
 export const L2_INDUCTION_PROMPT: PromptDef = {
   id: "l2.induction",
-  version: 2,
+  version: 3,
   description:
-    "Distill an L2 policy (procedural sub-task strategy) from a cluster of similar L1 traces, with explicit boundaries against L3 world-model drift.",
+    "Distill an L2 policy (procedural sub-task strategy) from a cluster of similar L1 traces, with explicit boundaries against L3 world-model drift and against conversational-act 'actions'.",
   system: `You induce reusable **procedural policies** from agent experience.
 
 A policy is a "how-to": "when you see condition X in the agent's state,
@@ -83,6 +90,59 @@ libs by default":
     "Alpine container images ship only the pure-Python tier of the
      Python dependency stack."
 
+──────────────────── Boundaries — conversational acts are NOT policies ────────────────────
+
+A **conversational act** is an I/O behaviour aimed at the user (or
+another agent) — not a procedure applied to the environment. When a
+trace cluster's dominant shared behaviour is dialogue, do NOT wrap that
+dialogue as an ACTION. Such policies pass schema validation but produce
+"dead skills" — the crystalliser promotes them, retrieval surfaces
+them, and no agent can meaningfully invoke them because there is
+nothing to invoke.
+
+Dialogue verbs / patterns to REJECT as ACTION templates (non-exhaustive):
+  - **asking the user** to clarify, choose, or provide input
+    ("ask the user to confirm the skill name before viewing it")
+  - **requesting confirmation** before proceeding
+    ("confirm with the user before deleting the file")
+  - **notifying / informing** the user about state
+    ("notify the user that the build finished")
+  - **reporting status** back to the user
+    ("report the current pipeline status to the user")
+  - **explaining / clarifying** to the user
+    ("explain to the user why the request failed")
+
+Contrast:
+
+  Wrong (dialogue as action — dead skill):
+    trigger:  "user hasn't specified which skill they want to view"
+    action:   "ask the user to confirm the skill name before viewing it"
+
+  Right (dialogue folded into trigger; real procedure as action):
+    trigger:  "user's view-skill request omits an unambiguous skill id
+               AND at least two skills fuzzy-match the phrase they used"
+    action:   "1. look up all skills whose name/tags fuzzy-match the
+               user's phrase; 2. if exactly one hits, resolve to that
+               skill's id; 3. if multiple hit, present the top 3 with
+               ids and let the user pick by id in the next turn"
+
+The distinction: the "wrong" version's action IS the dialogue; the
+"right" version's action is a resolution procedure the agent can carry
+out, and asking the user is at most a fallback branch, not the main
+step. If your only shared behaviour across traces is "the agent talks
+to the user", you have NO policy to induce — see the abstain rule
+below.
+
+Abstain when the cluster IS dialogue-only: if every trace in the input
+reduces to the same dialogue act with no follow-up procedure the agent
+executes on the environment, **do not emit a policy**. Instead return:
+
+  { "abstain": true, "reason": "cluster's shared behaviour is a
+    conversational act, not a reusable procedure — no policy to induce" }
+
+An abstained induction is a successful outcome, not a failure — it
+prevents dead skills from entering the pool.
+
 ──────────────────── Output ─────────────────────
 
 Return JSON:

apps/memos-local-plugin/tests/e2e/v7-full-chain.e2e.test.ts

@@ -189,7 +189,7 @@ function buildFullChainLlm(): LlmClient {
       }),
 
       // L2 induction — distills a policy from ≥2 similar traces.
-      "l2.l2.induction.v2": (input: unknown) => {
+      "l2.l2.induction.v3": (input: unknown) => {
         const text = lastUserMessage(input);
         const isPython = /python|pip|\.py\b/i.test(text);
         return {

apps/memos-local-plugin/tests/integration/adapters/openclaw-full-chain.test.ts

@@ -26,7 +26,7 @@
  *   - `capture.alpha.reflection.score.v1`— α scoring
  *   - `capture.summarize`                — trace-level summaries
  *   - `reward.reward.r_human.v3`         — R_human axis scoring
- *   - `l2.l2.induction.v2`               — L2 policy induction
+ *   - `l2.l2.induction.v3`               — L2 policy induction
  *   - `l3.abstraction.v2`                — L3 world-model abstraction
  *   - `skill.crystallize`                — skill draft
  *
@@ -234,7 +234,7 @@ function buildLlm(): LlmClient {
         reason: "concrete root-cause reflection",
       }),
 
-      "l2.l2.induction.v2": (input: unknown) => {
+      "l2.l2.induction.v3": (input: unknown) => {
         const evidence = (input as { evidenceTraces?: Array<{ id: string }> })
           ?.evidenceTraces ?? [];
         return {

apps/memos-local-plugin/tests/unit/llm/prompts.test.ts

@@ -48,6 +48,35 @@ describe("llm/prompts", () => {
     expect(detectDominantLanguage(["GRPO / TRL / reward_fn.py"])).toBe("en");
   });
 
+  it("l2 induction prompt guards against conversational-act actions (issue #2318)", () => {
+    // Version must bump when we tighten guidance so downstream
+    // `promptId@version` records attribute the new behaviour correctly.
+    expect(L2_INDUCTION_PROMPT.version).toBe(3);
+
+    const sys = L2_INDUCTION_PROMPT.system;
+
+    // The boundary block must name conversational acts as a rejected
+    // ACTION shape (symmetric to the existing L3 world-model drift guard).
+    expect(sys).toMatch(/conversational act/i);
+
+    // Enumerate the dialogue verbs we want the model to recognise as
+    // dead-skill precursors — must appear in the prompt so the LLM has a
+    // concrete list to check itself against.
+    expect(sys).toMatch(/\bask(ing)?\b.*\bthe user\b|\bask the user\b/i);
+    expect(sys).toMatch(/\bconfirm(ation)?\b/i);
+    expect(sys).toMatch(/\bnotify(ing)?\b|\bnotification\b/i);
+    expect(sys).toMatch(/\breport(ing)? status\b|\bstatus report\b/i);
+
+    // When the trace cluster's ONLY shared behaviour is dialogue, the
+    // prompt must offer the model an "abstain" escape hatch instead of
+    // forcing it to fabricate a policy.
+    expect(sys).toMatch(/abstain|no policy|do not emit|omit the policy/i);
+
+    // The existing L3-drift guard must still be present — this change is
+    // additive, not a rewrite.
+    expect(sys).toMatch(/L3|world model|world-model/i);
+  });
+
   it("retrieval filter prompt asks for ranked output without selected-field leftovers", () => {
     expect(RETRIEVAL_FILTER_PROMPT.system).toContain('"ranked"');
     expect(RETRIEVAL_FILTER_PROMPT.system).not.toContain('"selected"');

apps/memos-local-plugin/tests/unit/memory/l2/induce.test.ts

@@ -44,7 +44,7 @@ describe("memory/l2/induce", () => {
   it("returns {ok:true, draft} and fills support_trace_ids when the LLM omits them", async () => {
     const llm = fakeLlm({
       completeJson: {
-        "l2.l2.induction.v2": {
+        "l2.l2.induction.v3": {
           title: "install system libs first",
           trigger: "pip install fails in container with missing system library",
           procedure: "1. detect missing lib 2. apk/apt-get install 3. retry pip",
@@ -75,7 +75,7 @@ describe("memory/l2/induce", () => {
   it("cleans unsafe markup from LLM-derived policy fields", async () => {
     const llm = fakeLlm({
       completeJson: {
-        "l2.l2.induction.v2": {
+        "l2.l2.induction.v3": {
           title: "<img src=x onerror=alert(1)> install system libs",
           trigger: "<script>alert(1)</script>pip fails [bad](javascript:alert(1))",
           procedure: "Use [safe](https://example.com), ignore [bad](javascript:alert(1))",
@@ -149,7 +149,7 @@ describe("memory/l2/induce", () => {
   it("reason=llm_failed when the LLM draft is malformed (missing title)", async () => {
     const llm = fakeLlm({
       completeJson: {
-        "l2.l2.induction.v2": { trigger: "no title", procedure: "..." },
+        "l2.l2.induction.v3": { trigger: "no title", procedure: "..." },
       },
     });
     const res = await induceDraft(

apps/memos-local-plugin/tests/unit/memory/l2/l2.integration.test.ts

@@ -112,7 +112,7 @@ describe("memory/l2/integration", () => {
 
     const llm = fakeLlm({
       completeJson: {
-        "l2.l2.induction.v2": {
+        "l2.l2.induction.v3": {
           title: "install missing system libs in container",
           trigger: "pip install fails in container with MODULE_NOT_FOUND due to missing system lib",
           procedure: "1. detect lib 2. use distro pkg manager 3. retry pip",
@@ -367,7 +367,7 @@ describe("memory/l2/integration", () => {
         repos: handle.repos,
         llm: fakeLlm({
           completeJson: {
-            "l2.l2.induction.v2": {
+            "l2.l2.induction.v3": {
               title: " 闲聊场景下避免使用emoji ",
               trigger: "用户发起非任务性闲聊，且未明确要求使用emoji",
               procedure: "以简洁、自然的文字回应，但不添加任何emoji或表情符号",
@@ -457,7 +457,7 @@ describe("memory/l2/integration", () => {
         repos: handle.repos,
         llm: fakeLlm({
           completeJson: {
-            "l2.l2.induction.v2": {
+            "l2.l2.induction.v3": {
               title: "结构化呈现外部数据查询结果",
               trigger: "agent通过工具调用获取到外部数据源的原始响应（搜索结果、API返回、数据库查询等），需要向用户呈现信息",
               procedure: "1) 从原始数据中提取关键信息要素；2) 按逻辑分类组织信息（时间、地点、数值、状态等）；3) 使用结构化格式呈现（分类标题、列表、表格等）；4) 可选：基于数据添加简短的实用性总结或建议",
@@ -548,7 +548,7 @@ describe("memory/l2/integration", () => {
         repos: handle.repos,
         llm: fakeLlm({
           completeJson: {
-            "l2.l2.induction.v2": {
+            "l2.l2.induction.v3": {
               ...shared,
               boundary: "仅适用于数据库事务和写入操作，不适用于天气API或公开搜索结果",
               rationale: "边界不同，不能复用天气呈现经验",
@@ -630,7 +630,7 @@ describe("memory/l2/integration", () => {
         repos: handle.repos,
         llm: fakeLlm({
           completeJson: {
-            "l2.l2.induction.v2": {
+            "l2.l2.induction.v3": {
               title: "工具全部失效时坦诚说明并回退到已有知识",
               trigger: "连续尝试多个同类工具（如多个搜索引擎、多个新闻源）后全部因反爬、封禁或网络错误失效，且用户问题需要实时信息",
               procedure: "1) 明确告知用户所有尝试过的工具都已失效（列出具体工具名）；2) 基于训练数据中的已有知识提供最接近的答案；3) 明确标注该信息的时间戳或来源时间，并提醒用户可能存在时效性差异；4) 建议用户通过其他渠道（如直接搜索、官方网站）验证",
@@ -663,7 +663,7 @@ describe("memory/l2/integration", () => {
 
     const llm = fakeLlm({
       completeJson: {
-        "l2.l2.induction.v2": {
+        "l2.l2.induction.v3": {
           title: "t",
           trigger: "tr",
           procedure: "pr",