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Run the harness-mem review daily action when the user invokes hm-review.
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Basierend auf der SOC-Berufsklassifikation
Run the harness-mem distill daily action when the user invokes hm-distill.
Run the harness-mem distill daily action when the user invokes hm-distill.
Evidence answerer for harness-mem memory admission and review. Use when grill-before-distill or review needs concrete support from packet evidence, observations, memory search, files, docs, tests, config, or command output. Does not write candidates or confirmed truth.
Architecture, product-boundary, roadmap, and long-lived-rule consultation for harness-mem memory admission and review. Use when a candidate's truth depends on design judgment rather than only evidence lookup. Does not write memory.
Standard memory admission mode for harness-mem (grill-me role). Runs automatically on distill — depth scales by risk, not a forced heavy loop every time. Three modes: deep interrogation, light checklist, lookback for stale truth. No writes. More than a review-stage helper: admission plus lookback.
Conversation autopilot for harness-mem. Use proactively when starting or resuming a task, when the user asks about previous decisions, when the user asks to remember a rule, when recent work becomes stable enough to preserve, or when a memory conflict appears.
| name | hm-review |
| description | Run the harness-mem review daily action when the user invokes hm-review. |
This is a user-invocable harness-mem Daily command. Follow the action below through the configured harness-mem MCP server; do not replace it with terminal maintenance commands.
通过 MCP 列出当前项目的待审候选记忆。AI 自动做质量判断并处理低风险候选，最后给用户一份复核摘要；用户只在结果不对时纠错。
Input: 可指定项目名（/hm-review bazi-apps）。省略则用 active project。
Core UX
不要把 raw candidates 直接做成逐条单选题或多选表单。用户不是分类器。
正确体验是：
1. 拉取 pending 候选
2. AI 自动判定每条候选的处理动作：confirm / reject / keep_pending / migrate
3. AI 直接执行低风险 reject / keep_pending / migrate 判断
4. 对高置信、低风险 confirm 可直接确认；对会改变长期行为的高风险 confirm 先列入待确认摘要
5. 最后给用户处理结果和待确认项，用户只在不满意时纠错
Steps
确认项目
get_project_status(project_root=<当前工作区>, host_client="grok") 读取 active project，并幂等检查项目 Hook拉所有 status=pending 的候选
list_candidates：
project_name=<project>status="pending"limit=100search_memory 找 pending；search_memory 默认只返回 truth 层（auto_confirmed / user_confirmed），不能作为审核列表来源AI 预审分组
每条候选先判断它属于哪类，而不是直接问用户是否纳入：
默认拒绝这些类型：
TeamCreate / SendMessage / TeamDelete / agent idle 之类工具运行问题，除非项目本身就在开发这些工具。/plan-eng-review、/plan-ceo-review、/plan-design-review、/plan-devex-review 等跨项目 review workflow，除非用户明确要记录为全局工作流记忆。自动处理策略
默认由 AI 直接处理，不要打开逐条单选/多选 UI，不要让用户逐条决定，也不要提供 "All Confirm / Only rule / All Reject" 这种选项菜单。
AI 应按以下规则执行：
执行 MCP 操作
govern_memory(action="decide", arguments={kind:"rule", ...})govern_memory(action="decide", arguments={kind:"memory", ...})govern_memory(action="decide", arguments={kind:"relation", ...})keep_pending 不调用工具。最终复核摘要
最后只给用户看处理结果，不再让用户逐条选择：
已自动处理：confirmed A，rejected B，kept pending C。
需要你最终确认：D 条高风险确认建议。
如果有处理不对，告诉我编号，我会改。
总结呈现
confirmed Nrejected Mstill pending K/hm-distill 应避免同类候选。Notes