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bears-machines熊机

Agent Skill

bears-machines 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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

659

周安装

28

GitHub Stars

公开资料未说明

下载量

231
CodexClaudeCursorGemini CLI

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

unknown

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:bears-machines(熊机)
来源仓库:https://github.com/pudap/bears-skills
仓库路径:skills/bears-machines
安装命令:
npx skills add https://github.com/pudap/bears-skills --skill bears-machines
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 npx skills 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。

skills.shnpx skills
npx skills add https://github.com/pudap/bears-skills --skill bears-machines

简介

bears-machines 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

  • 它为 PUDA 工作流提供机器选择和能力指导,支持液体处理和机器人操作。
  • 可通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 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操作。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

BEARS machines

Goal

Provide machine-selection and capability guidance for PUDA workflows, then load the correct machine reference before generating commands.

Critical Rule

If you are unsure which machine should be used for a command, ask the user before proceeding. Do not assume.

Machine Capabilities and When to Use

First Machine (machine_id: "first")

Use for liquid handling and deck operations.

Capabilities:

  • Pipetting workflows: aspirate, dispense, attach tip, drop tip
  • Deck and labware workflows: load deck, position-dependent operations
  • Sequenced robotic handling steps in wet-lab protocols

Use this machine when:

  • The task is about moving liquids between wells/labware
  • The user mentions tip usage, aspiration/dispensing, or deck slots/labware setup

Before command generation:

  • Refer to: first-machine
  • Run puda machine commands first to understand available commands
  • Follow constraints and sequencing in references/first-machine.md

Biologic Machine (machine_id: "biologic")

Use for electrochemical testing and characterization.

Capabilities:

  • OCV (Open Circuit Voltage)
  • CA (Chronoamperometry)
  • PEIS / GEIS (Impedance spectroscopy)
  • CV (Cyclic Voltammetry)
  • MPP variants (MPP, MPP_Cycles, MPP_Tracking)

Use this machine when:

  • The task is an electrochemical measurement or battery/cell characterization
  • The user asks for OCV, CA, EIS, CV, or MPP tests

Before command generation:

  • Refer to: biologic-machine
  • Run puda machine commands biologic to understand available commands
  • Follow constraints in references/biologic-machine.md

Balance Machine

Use for gravimetric mass measurement via an Arduino-based USB load-cell balance on Linux.

Capabilities:

  • Continuous calibrated mass readings from a load-cell over USB serial (/dev/ttyUSB* or /dev/ttyACM*)
  • Background reader thread streaming readings at ~4 Hz; no polling required
  • Tare command to zero the balance before a dispense step
  • Freshness check (fresh flag) to detect stale/disconnected readings
  • NATS telemetry publishing via the edge service
  • Custom calibration CSV support

Use this machine when:

  • The workflow requires weighing a container before or after a liquid transfer
  • The user asks for gravimetric calibration, transfer error calculation, or balance feedback
  • The task involves viscosity or transfer accuracy experiments needing mass data

Before use:

  • Refer to: balance-machine
  • Ask the user for the Linux serial port (/dev/ttyUSB1, etc.) — do not assume
  • Ensure the edge service is running (uv run --package balance-edge python edge/balance.py)

Opentrons Machine (machine_id: "opentrons")

Use for automated liquid handling and full protocol generation on the Opentrons OT-2 robot.

Capabilities:

  • Full protocol code generation via Protocol.to_python_code() — produces valid runnable OT-2 Python
  • Pipetting workflows: aspirate, dispense, transfer (with auto-chunking for large volumes)
  • Tip management: pick_up_tip, drop_tip
  • Deck and labware setup: load_labware, load_instrument
  • Flow control: flow_rate, air_gap, blow_out, touch_tip, move_to
  • Protocol utilities: delay, comment, home
  • CSV-driven loops: read_csv_file + loop for data-driven protocols
  • Custom labware support: AMDM mass balance vials (30 mL, 50 mL) loaded inline
  • All gen2 pipette types: p10, p20, p300, p1000 (single and multi-channel)
  • External camera image capture: camera_capture — triggers the external camera mounted above the deck to capture and save a still image of the wellplate

Use this machine when:

  • The user references an Opentrons OT-2 robot
  • The task involves generating a complete OT-2 protocol or individual liquid handling commands
  • The user mentions Opentrons labware (tip racks, well plates, reservoirs, NEST, Corning, mass balance vials)
  • The workflow requires data-driven dispensing from a CSV file
  • The workflow requires capturing a camera image of the wellplate after dispensing steps

Before command generation:

  • Refer to: opentrons-machine
  • Run puda machine commands opentrons to understand available commands
  • Follow all command types, params, sequencing rules, and labware constraints in references/opentrons-machine.md

Selection Workflow

  1. Parse user intent and identify the tasks.
  2. Match intent to the machine capabilities above.
  3. If machine selection is unclear or ambiguous, ask the user and wait for confirmation.
  4. Load the corresponding reference file and CLI help.
  5. Generate commands only after machine choice is confirmed.

Output Guidance

When answering machine-selection questions:

  • State the recommended machine and a one-line reason tied to capability.
  • If uncertain, ask a direct clarification question instead of guessing.

Critical sequencing rules

  • opentrons protocols must always end with no tip attached to any pipette.
  • opentrons deck slot (location) for every load_labware command must be explicitly confirmed by the user — never assume a slot.
  • opentrons capture_image must be its own standalone protocol — never combined with pipetting commands in the same protocol.
  • balance — always call startup() before reading and shutdown() after. Always tare before a dispense step. Always verify fresh == True before using a reading.

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

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

Codex

33.16%
按下载量换算77

Claude

32.04%
按下载量换算74

Cursor

20.49%
按下载量换算47

Gemini CLI

8.61%
按下载量换算20

安全审计

Gen Agent Trust Hub

未通过

Socket

可疑

Snyk

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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