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reflow-machine-maintenance-guidance回流焊机维护指导

Agent Skill

reflow-machine-maintenance-guidance 用于辅助前端页面、组件、样式和交互逻辑开发,适合在 OpenClaw 中需要维护前端项目、生成组件或检查界面实现时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,187

周安装

93

GitHub Stars

公开资料未说明

下载量

766
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:reflow-machine-maintenance-guidance(回流焊机维护指导)
来源仓库:https://github.com/wu-uk/reflow-machine-maintenance-guidance
安装命令:
openclaw skills install reflow-machine-maintenance-guidance
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install reflow-machine-maintenance-guidance

简介

提供回流焊机维护指导,基于热电偶和 MES 数据。

  • 适合回答设备维护问题或生成详细操作指南。
  • 可结合具体数据输入提供合规建议。
  • 安装命令:openclaw skills install reflow-machine-maintenance-guidance。
  • 使用前建议确认权限范围和维护状态,避免触发未授权操作。

SKILL.md

name
reflow_machine_maintenance_guidance
description
This skill should be considered when you need to answer reflow machine maintenance questions or provide detailed guidance based on thermocouple data, MES data or defect data and reflow technical handbooks. This skill covers how to obtain important concepts, calculations, definitions, thresholds, and others from the handbook and how to do cross validations between handbook and datasets.

This skill should be considered when you need to answer reflow equipment maintenance questions based on thermocouple data, MES data, defect data, and reflow technical handbooks. Based on the questions, first retrieve related info from the handbook and corresponding datasets. Most frequently asked concepts include preheat, soak, reflow, cooling, ramp, slope, C/s, liquidus and wetting time, ramp rate guidance, time above liquidus, TAL, peak temperature guidance, minimum peak, margin above liquidus, conveyor speed, dwell time, heated length, zone length, time-in-oven, thermocouple placement, cold spot, worst case, representative sensor, numeric limits, temperature regions, etc. If the handbook provides multiple values or constraints, implement all and use the stricter constraint or the proper value.

Common equations used in manufacturing reflow machines include the max ramp is max(s_i) over the region, where s_i = (T_i - T_{i-1}) / (t_i - t_{i-1}) for dt > 0. For the temperature band region, only consider segments where both endpoints satisfy tmin <= T <= tmax. For the zone band region, only consider zone_id in zones. For time band region, only consider t_start_s <= time_s <= t_end_s. For wetting/TAL-type metrics, compute time above a threshold thr using segment interpolation. For each TC, peak_tc = max(temp_c). min_peak_run = min(peak_tc), and required_peak = liquidus + peak_margin. Given heated length L_eff_cm, minimum dwell t_min_s, speed_max_cm_min = (L_eff_cm / t_min_s) * 60. Given L_eff_cm, maximum time t_max_s, speed_min_cm_min = (L_eff_cm / t_max_s) * 60. When reducing multiple thermocouples to one run-level result, if selecting maximum metric, choose (max_value, smallest_tc_id). If selecting minimum metric, choose (min_value, smallest_tc_id).

Here are reference codes.

#Suggest to get a config object from the handbook and use it for all computations.
cfg = {
#   temperature region for the ramp calculation:
#   either {"type":"temp_band", "tmin":..., "tmax":...}
#   or {"type":"zone_band", "zones":[...]}
#   or {"type":"time_band", "t_start_s":..., "t_end_s":...}
#   "preheat_region": {...},
#   "ramp_limit_c_per_s": ...,
#   "tal_threshold_c_source": "solder_liquidus_c",   # if MES provides it
#   "tal_min_s": ...,
#   "tal_max_s": ...,
#   "peak_margin_c": ...,
#   conveyor feasibility can be many forms; represent as a rule object
}
runs = pd.read_csv(os.path.join(DATA_DIR, "mes_log.csv"))
tc   = pd.read_csv(os.path.join(DATA_DIR, "thermocouples.csv"))

runs["run_id"] = runs["run_id"].astype(str)
tc["run_id"]   = tc["run_id"].astype(str)
tc["tc_id"]    = tc["tc_id"].astype(str)

runs = runs.sort_values(["run_id"], kind="mergesort")
tc   = tc.sort_values(["run_id","tc_id","time_s"], kind="mergesort")
#Always sort samples by time before any computation in thermocouple computation. Ignore segments where `dt <= 0`
df_tc = df_tc.sort_values(["run_id","tc_id","time_s"], kind="mergesort")
def max_slope_in_temp_band(df_tc, tmin, tmax):
    g = df_tc.sort_values("time_s")
    t = g["time_s"].to_numpy(dtype=float)
    y = g["temp_c"].to_numpy(dtype=float)
    best = None
    for i in range(1, len(g)):
        dt = t[i] - t[i-1]
        if dt <= 0:
            continue
        if (tmin <= y[i-1] <= tmax) and (tmin <= y[i] <= tmax):
            s = (y[i] - y[i-1]) / dt
            best = s if best is None else max(best, s)
    return best  # None if no valid segments
def time_above_threshold_s(df_tc, thr):
    g = df_tc.sort_values("time_s")
    t = g["time_s"].to_numpy(dtype=float)
    y = g["temp_c"].to_numpy(dtype=float)
    total = 0.0
    for i in range(1, len(g)):
        t0, t1 = t[i-1], t[i]
        y0, y1 = y[i-1], y[i]
        if t1 <= t0:
            continue
        if y0 > thr and y1 > thr:
            total += (t1 - t0)
            continue
        crosses = (y0 <= thr < y1) or (y1 <= thr < y0)
        if crosses and (y1 != y0):
            frac = (thr - y0) / (y1 - y0)
            tcross = t0 + frac * (t1 - t0)
            if y0 <= thr and y1 > thr:
                total += (t1 - tcross)
            else:
                total += (tcross - t0)
    return total

适合场景

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需要根据任务场景推荐可安装能力包时

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能力 2

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能力 3

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能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

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通过

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可疑

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

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