Revert "docs(skills): translate dpgen-run docs to Chinese"

This reverts commit f0c7b33.

Author: SchrodingersCattt

skills/dpgen-run/SKILL.md

@@ -1,40 +1,40 @@
 ---
 name: dpgen-run
-description: 使用 param.json 和 machine.json 准备、解释、验证并运行 DP-GEN 并行学习流程。
+description: Prepare, explain, validate, and run DP-GEN concurrent-learning workflows using param.json and machine.json.
 license: LGPL-3.0-or-later
 metadata:
   author: MatMaster
-  version: 0.4.0
+  version: 0.3.0
   repository: https://github.com/deepmodeling/dpgen
 ---
 
 # DP-GEN Run
 
-用于迭代训练、探索、标注和再训练流程。
+Use this skill for the iterative train, explore, label, and retrain workflow.
 
-## 使用契约
+## Contract
 
-- 科学参数放在 `param.json`，执行参数放在 `machine.json`。
-- 精确运行命令为 `dpgen run param.json machine.json`。
-- 默认只准备和验证；只有用户明确确认后才执行。
+- Keep scientific settings in `param.json` and execution settings in `machine.json`.
+- Run exactly `dpgen run param.json machine.json`.
+- Prepare and validate by default; execute only after explicit confirmation.
 
-## 工作流程
+## Workflow
 
-1. 先检查已有数据和配置，再询问缺失输入。
-1. 修改现有可用文件，并保留科学及站点选择。
-1. 只加载当前任务需要的 reference。
-1. 验证 schema、生成的任务输入、阶段状态和监控信号。
-1. 汇报路径、命令、分体系证据、风险和后续检查。
+1. Inspect existing data and configurations before asking for inputs.
+1. Patch working files and preserve scientific/site-specific choices.
+1. Load only the references needed for the task.
+1. Validate schemas, generated task inputs, stage state, and monitoring signals.
+1. Report paths, command, per-system evidence, risks, and next checks.
 
-## Reference
+## References
 
-- [工作流说明](references/workflow.md)：范围、布局、策略和汇报。
-- [配置、验证与执行](references/validation-and-run.md)：两个 JSON、生成输入检查及安全启动/恢复。
-- [监控与排障](references/monitoring.md)：准确率趋势、阶段进度和证据化诊断。
+- [Workflow guidance](references/workflow.md): scope, layout, policy, and reporting.
+- [Configuration, validation, and execution](references/validation-and-run.md): both JSON files, generated-input checks, and safe launch/restart checks.
+- [Monitoring and troubleshooting](references/monitoring.md): accuracy trends, stage progress, and evidence-based diagnosis.
 
-## 防护规则
+## Guardrails
 
-- 不得合并两个 JSON，也不得臆造路径、队列、模块或命令。
-- 保留 descriptor、backend、阈值、计划、ensemble 和 `type_map` 选择。
-- 必须准确写成 `se_atten_v2`；当前 `dpgen run` 不接受 `fp_style: "none"`。
-- 无法安全发现必需的科学或执行输入时，停止并询问。
+- Never merge the JSON files or invent paths, queues, modules, or commands.
+- Preserve descriptor, backend, thresholds, schedules, ensemble, and `type_map` choices.
+- Spell `se_atten_v2` exactly; `fp_style: "none"` is invalid for current `dpgen run`.
+- Stop and ask when required scientific or execution inputs are undiscoverable.

skills/dpgen-run/references/monitoring.md

@@ -1,44 +1,54 @@
-# DP-GEN 监控与排障
-
-在每轮迭代后，或准确率/阶段进度停滞时加载本 reference。“准确率”指能量/力的训练与
-验证 RMSE；model deviation 和 FP failure ratio 是独立信号。
-
-## 证据循环
-
-每轮记录参数/配置身份、系统及温度/压力条件、训练/验证 RMSE、model-deviation 的
-accurate/candidate/failed 数量、FP 成功/失败数量、当前阶段，以及新标注是否进入下一轮
-训练输入。先汇报分 system、分 condition 结果，再给全局汇总。
-
-## 按顺序诊断
-
-| 现象                                 | 首先检查                                             | 针对性调整                                        |
-| ------------------------------------ | ---------------------------------------------------- | ------------------------------------------------- |
-| 训练和验证 RMSE 都长期偏高           | 单位、标签、type map、覆盖范围、descriptor 容量      | 先修数据/schema，再检查 fitting net、学习率和步数 |
-| 训练 RMSE 下降但验证 RMSE 停滞或上升 | 数据泄漏、重复样本、异常值、验证覆盖                 | 先重新划分/去重并补代表性标注，再改模型容量       |
-| RMSE 良好但 model deviation 偏高     | 未见过的体系/构型或条件、初始结构                    | 定向扩展采样并标注候选区间                        |
-| candidate 数为零                     | 信任阈值、MD 稳定性、轨迹频率、模型多样性            | 先检查阈值和采样设置，不要直接改训练              |
-| candidate 过多或 FP 失败增加         | 阈值、MD 不稳定、结构损坏、FP 收敛                   | 修复输入/采样，不要把失败标签加入训练             |
-| 阶段不推进或任务数不对               | record 状态、计划长度、sys_idx、生成输入、提交元数据 | 对齐状态与实际任务，禁止盲目重启                  |
-| 生效步数与请求步数不同               | 生成输入中的 reuse/迭代 override                     | 对比根配置和任务配置，有意修正 override           |
-
-## 数据与标签检查
-
-检查单位和符号、原子顺序、type map、重复样本、异常幅值以及训练/验证覆盖。每个训练帧
-必须有有限且可解析的 energy 和 force；只有在配置并验证后才纳入 virial/stress。合并前拒绝
-缺失、重复或未收敛的标签。
-
-## 训练检查
-
-比较训练/验证曲线、loss 分量、学习率衰减、步数预算、batch size、模型容量、ensemble seed
-和 backend/version 兼容性。全局指标良好也不能掩盖某个 system 或 condition 的失败。一次只
-改变一个主要因素，并保留修改前的证据。
-
-## 采样与决策检查
-
-检查 MD 稳定性、温度、压力、ensemble、`nsteps`、`trj_freq`、信任区间位置及分 condition
-分布。区分模型问题和覆盖不足：RMSE 良好但 deviation 偏高通常需要定向采样；已覆盖区域
-RMSE 仍高则优先检查数据或训练。
-
-监控是基于证据的决策阶段，不只是状态播报。扩展采样、修改阈值、增加步数或重启前，先记录
-证据、精确参数差异、预期效果和 controller 状态；之后记录新任务身份与结果。禁止静默调参
-或重启。
+# DP-GEN Monitoring and Troubleshooting
+
+Load this reference after each iteration or whenever accuracy or stage progress
+stalls. Here "accuracy" means energy/force train and validation RMSE; model
+deviation and FP failure ratio are separate signals.
+
+## Evidence loop
+
+For each iteration, record the parameter/configuration identity, systems and
+temperature/pressure conditions, train/validation RMSE, model-deviation
+accurate/candidate/failed counts, FP success/failure counts, current stage, and
+whether new labels reached the next training input. Report per-system and
+per-condition results before any global aggregate.
+
+## Diagnose in this order
+
+| Symptom                                              | First checks                                                                 | Focused adjustment                                                          |
+| ---------------------------------------------------- | ---------------------------------------------------------------------------- | --------------------------------------------------------------------------- |
+| Train and validation RMSE both stay high             | Units, labels, type map, coverage, descriptor capacity                       | Fix data/schema first; then review fitting net, learning rate, and steps    |
+| Train RMSE falls but validation RMSE stalls or rises | Split leakage, duplicates, outliers, validation coverage                     | Re-split or deduplicate; add representative labels before changing capacity |
+| RMSE is good but model deviation is high             | Unseen systems/geometries or conditions, initial structures                  | Broaden targeted sampling and labeling the candidate window                 |
+| Candidate count is zero                              | Trust thresholds, MD stability, trajectory frequency, model diversity        | Check thresholds and sampling settings before changing training             |
+| Candidate count is excessive or FP failures rise     | Thresholds, unstable MD, malformed structures, FP convergence                | Repair inputs/sampling; do not add failed labels                            |
+| Stage does not advance or task count is wrong        | record state, schedule length, sys_idx, generated input, submission metadata | Reconcile state and actual tasks; do not restart blindly                    |
+| Effective steps differ from requested steps          | Reuse/iteration override in generated input                                  | Compare root and task-level values; fix the override deliberately           |
+
+## Data and label checks
+
+Check units and signs, atom ordering, type maps, duplicates, abnormal
+magnitudes, and train/validation coverage. Every training frame must have
+finite, parseable energy and force; include virial/stress only when configured
+and verified. Reject incomplete, duplicated, or unconverged labels before
+merging them into training data.
+
+## Training checks
+
+Compare train and validation curves, loss components, learning-rate decay, step
+budget, batch size, model capacity, ensemble seeds, and backend/version
+compatibility. A good global metric must not hide a failing system or condition.
+Change one major factor at a time and retain the previous evidence.
+
+## Sampling and decision checks
+
+Inspect MD stability, temperatures, pressures, ensembles, `nsteps`, `trj_freq`,
+trust-window placement, and per-condition distributions. Distinguish a model
+problem from missing coverage: good RMSE with high deviation usually calls for
+targeted sampling, while high RMSE on covered data calls for data or training
+review.
+
+Monitoring is an evidence-based decision stage, not only status reporting.
+Before extending sampling, changing thresholds, increasing steps, or restarting,
+record the evidence, exact parameter delta, expected effect, and controller
+state. Afterward record the new task identity and outcome; never retune or
+restart silently.

skills/dpgen-run/references/validation-and-run.md

@@ -1,80 +1,94 @@
-# DP-GEN 配置、验证与执行
+# DP-GEN Configuration, Validation, and Execution
 
-在创建、审阅、验证、启动或恢复 `dpgen run` 配置时加载本 reference。
+Load this reference when creating, reviewing, validating, launching, or
+recovering a `dpgen run` configuration.
 
-## 1. `param.json` 要点
+## 1. `param.json` essentials
 
-应保留或收集：
+Preserve or collect:
 
-- `type_map`（有序元素符号）、可选的 `mass_map`、`init_data_prefix` 和 `init_data_sys`；
-- `sys_configs_prefix` 与 `sys_configs`（系统列表，每个系统又是结构列表）；
-- `numb_models` 以及完整的 `default_training_param.model`、`learning_rate`、`loss` 和 `training`；
-- `model_devi_dt`、`model_devi_skip`、信任阈值、清理标志和 `model_devi_jobs`；
-- `fp_style`、`fp_task_min`、`fp_task_max` 及后端专用 FP 输入。
+- `type_map` (ordered symbols), optional `mass_map`, `init_data_prefix`, and `init_data_sys`;
+- `sys_configs_prefix` and `sys_configs` (a list of systems, each a list of structures);
+- `numb_models` and complete `default_training_param.model`, `learning_rate`, `loss`, and `training`;
+- `model_devi_dt`, `model_devi_skip`, trust thresholds, cleanup flag, and `model_devi_jobs`;
+- `fp_style`, `fp_task_min`, `fp_task_max`, and backend-specific FP inputs.
 
-DeePMD-kit 1.x 使用 `training.systems`，2.x/3.x 使用
-`training.training_data.systems`。将对应版本的 systems 留空，由 DP-GEN 根据
-`init_data_sys` 填充。使用版本相关 descriptor 或功能前，核对 `deepmd_version`
-和已安装的软件栈。
+DeepMD-kit 1.x uses `training.systems`; 2.x/3.x use
+`training.training_data.systems`. Leave the version-appropriate value empty so
+DP-GEN can fill it from `init_data_sys`. Verify `deepmd_version` and the
+installed stack before using version-specific descriptors or features.
 
-`train_backend` 支持 `tensorflow` 和 `pytorch`，但实际兼容性仍取决于已安装的
-DeePMD-kit 与 descriptor。核对组合并准确拼写 `se_atten_v2`。普通 LAMMPS 探索使用
-默认 `model_devi_engine`；替代引擎只有在验证后才能设置。当前 `dpgen run` 不接受
-`fp_style: "none"`。
+`train_backend` accepts `tensorflow` and `pytorch`, but compatibility still
+depends on the installed DeePMD-kit and descriptor. Verify the combination;
+spell `se_atten_v2` exactly. Ordinary LAMMPS exploration uses the default
+`model_devi_engine`; set an alternative only when verified. Current `dpgen run`
+does not accept `fp_style: "none"`.
 
-每个 `init_data_sys` 目录必须包含 `type_map.raw`、`type.raw` 和 `set.000/`；
-每个 `type_map.raw` 必须与 `param.json.type_map` 完全一致。
+Each `init_data_sys` directory must contain `type_map.raw`, `type.raw`, and
+`set.000/`; every `type_map.raw` must match `param.json.type_map` exactly.
 
-## 2. `machine.json` 要点
+## 2. `machine.json` essentials
 
-保持独立的 `train`、`model_devi` 和 `fp` block。每个 block 提供 `command`、
-`machine`（`batch_type`、`context_type`、`local_root` 以及需要时的
-`remote_root`/profile）和 `resources`（节点数、CPU/GPU 数、`group_size`、
-队列/分区参数及调度器 `source_list`）。通用 local、scheduler 和 SSH 结构见
-[machine schema 说明](../../../doc/run/example-of-machine.md)。
+Keep separate `train`, `model_devi`, and `fp` blocks. Each block supplies a
+`command`, `machine` (`batch_type`, `context_type`, `local_root`, and any
+required `remote_root`/profile), and `resources` (`number_node`, CPU/GPU counts,
+`group_size`, queue/partition flags, and scheduler `source_list`). See the
+[repository machine schema walkthrough](../../../doc/run/example-of-machine.md)
+for generic local, scheduler, and SSH shapes.
 
-启动 shell 与派发任务处于不同环境。调度器任务必须通过 `source_list` 激活自己的
-运行环境，外层激活不会可靠继承。训练命令必须选择与 `train_backend` 相同的后端
-（DeePMD-kit 3.x 的 PyTorch 可能需要 `dp --pt`）。应根据已安装的 DPDispatcher
-验证命令、context、batch 别名、路径和资源，不要凭经验统一改写。
+The launcher shell and dispatched tasks are different environments. Scheduler
+jobs must activate their own runtime through `source_list`; outer activation is
+not inherited reliably. The training command must select the same backend as
+`train_backend` (for DeePMD-kit 3.x PyTorch this may be `dp --pt`). Validate
+commands, context types, batch aliases, roots, and resources against installed
+DPDispatcher rather than normalizing by assumption.
 
-## 3. 启动前验证生成输入
+## 3. Validate generated inputs before launch
 
 ```bash
 dpgen -h
 python -m json.tool param.json
 python -m json.tool machine.json
 ```
 
-用 `run_jdata_arginfo()` 规范化 `param.json`，用 `convert_mdata()` 转换
-`machine.json`。解析所有数据/结构路径，比较 type map，检查可执行文件和调度器权限，
-并核对 FP 输入及成本上限。
+Normalize `param.json` with `run_jdata_arginfo()` and convert `machine.json` with
+`convert_mdata()`. Resolve all data/structure paths, compare type maps, check
+executables and scheduler access, and verify FP inputs and cost limits.
 
-提交前逐个检查每个阶段/任务实际生成的 `input.json`。不能从根目录 JSON 推断最终配置：
-核对模型数、seed、checkpoint、数据系统、`sys_idx`、温度/压力和最终训练步数。存在
-reuse 或迭代 override 时，同时断言默认值和生效值。
+Before submission, inspect the generated `input.json` for every stage/task. Do
+not infer the effective configuration from the root files: check model count,
+seed, checkpoint, data systems, `sys_idx`, temperatures/pressures, and final
+training steps. When reuse or iteration overrides exist, assert both the default
+and effective values.
 
-将目标迭代与 `record.dpgen`、阶段目录、DPDispatcher work base/提交元数据及日志交叉核对。
-仅有目录或 job 并不代表阶段已完成。
+Cross-check the intended iteration against `record.dpgen`, stage directories,
+DPDispatcher work base/submission metadata, and logs. A stage is not complete
+because a directory or job exists.
 
-## 4. 恢复与重启
+## 4. Restart and recovery
 
-从 `record.dpgen` 恢复属于可能产生费用的操作。同一工作目录只能保留一个 controller。
-编辑状态前先停止 controller、备份状态文件、记录阶段/job ID 及新旧设置，并查询调度器。
+Treat a restart from `record.dpgen` as cost-bearing. Keep one controller for a
+work directory. Before editing state, stop it, back up the state file, record
+stage/job IDs and old/new settings, and query the scheduler.
 
-活动阶段的 command、context、remote root、队列、资源、`group_size`、flags、激活方式、
-任务列表或 common files 变化，都可能产生新的 DPDispatcher submission identity。新 identity
-不会导入旧 identity 的完成状态。要区分旧提交恢复和使用新 remote root 的有意重跑；连接超时
-本身不是重新提交理由。
+A changed active-stage command, context, remote root, queue, resources,
+`group_size`, flags, activation, task list, or common files can create a new
+DPDispatcher submission identity. It will not import completion state from the
+old identity. Distinguish recovery of the old submission from an intentional
+rerun with a new remote root; never resubmit solely because a connection timed
+out.
 
-恢复的正证据包括旧 submission hash、未变化的 job ID 和恢复日志；同时不能出现新的提交日志。
-`record.dpgen` 是工作流指针，不是调度器完成记录。当前计费阶段推进后，才能应用后续迭代的
-machine 修改。
+Positive recovery evidence is the old submission hash, unchanged job IDs, and a
+recovery message; absence of a new-submission message is required.
+`record.dpgen` is a workflow pointer, not a scheduler completion record.
+Apply later-iteration machine changes only after the current cost-bearing stage
+advances.
 
-## 5. 确认与检查
+## 5. Confirm and inspect
 
-展示文件、验证摘要、未解决风险和 `dpgen run param.json machine.json`。只有明确确认后才执行。
-启动后检查 `iter.*`、阶段日志、失败/等待状态、选中和标注数量，以及
-[监控输出](monitoring.md)。
+Show the exact files, validation summary, unresolved risks, and
+`dpgen run param.json machine.json`. Execute only after explicit confirmation.
+After launch, inspect `iter.*`, stage logs, failure/pending states, selected and
+labeled counts, and [monitoring outputs](monitoring.md).
 
-外部参考：https://docs.deepmodeling.com/projects/dpgen/en/latest/run/index.html
+External references: https://docs.deepmodeling.com/projects/dpgen/en/latest/run/index.html