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Agent Skill · 研究检索

solo-impl(单独实现)

solo-impl 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

solo-impl 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为执行命令;许可证为 MIT-0,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 31,660。

总安装 2,661周安装 112GitHub Stars 1下载 932核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.3.0

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:solo-impl(单独实现)
来源仓库:https://github.com/samarthshukla6/solo-impl
安装命令:
openclaw skills install solo-impl
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 solo-impl

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

自主执行器在用户环境中运行 Solo CLI 的各类指令。支持设置、校准、训练与推理命令的实际落地执行。具备沙箱隔离机制保护主机系统免受恶意脚本影响。执行高风险操作前会弹窗确认防止误触发。日志记录详尽便于回溯故障原因。solo-impl 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install solo-impl;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

solo-impl 怎么安装?

在终端执行 openclaw skills install solo-impl。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

solo-impl 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

solo-impl 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 openclaw skills install solo-impl 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

solo-impl 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

solo-impl 用的人多吗?

公开数据是累计安装 2,661 次,近一周安装 112 次,下载 932 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

solo-impl 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
solo-impl

单独实现 · 本页

2,6611执行命令MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
solo-impl
description
Autonomous executor for Solo CLI — actually runs setup, calibration, teleoperation, dataset recording, policy training, and inference commands in the user's terminal using Shell tool. Use when the user says "do it for me", "run", "execute", "set up solo cli", "start calibration", "record a dataset", "train the robot", "run inference", or asks to perform any Solo CLI action rather than just learn about it. Works standalone — does not require solo_cli_guide to be installed.
homepage
https://github.com/GetSoloTech/Solo-claw
metadata
clawdbot
emoji
🦾
requires
env
required
false
description
Needed only when pushing datasets or models to HuggingFace Hub (--push-to-hub). Not required for local-only workflows.
required
false
description
Needed only when W&B logging is enabled during training. Not required if W&B logging is disabled.
files

Solo CLI Executor (solo_impl)

Autonomous executor for Solo CLI. This skill runs commands — it does not guide and wait. Every action is sourced from the bundled domain files.

Activation

  1. Read skill.json for the manifest, domain list, tutorial IDs, and command tiers.
  2. Read prompts/impl_executor_prompt.txt and adopt it as your active executor persona.

Domain actions

When an action is needed:

  • Identify the domain from skill.json → domains
  • Load domains/<domain>.json and find the action by its id field
  • Use only the command field verbatim — never invent flags or arguments

Tutorials

When running a full tutorial flow:

  • Load tutorials/<tutorial_id>.json
  • Start at entry_point
  • Follow on_success / on_failure transitions exactly — recovery paths are mandatory

Execution protocol — three modes

Mode 1 — Silent Automatable

No robot, no keyboard input. Run via Shell tool → validate → proceed automatically.

ActionCommandValidation
install_uvOS-specific curl/powershelluv --version
create_venvuv venv --python 3.12ls .venv/
activate_venvsource .venv/bin/activateecho $VIRTUAL_ENV
install_solo_cliuv pip install solo-cli or git clonesolo --help
setup_usb_permissionssolo setup-usb`groups $USER \grep dialout`
scan_motorssolo robo --scanread output
diagnose_armsolo robo --diagnoseread output

Mode 2A — Long-Running / No Keyboard

Robot involved. Runs continuously. User does NOT need to type in terminal — only physical arm interaction. Run via Shell tool backgrounded, poll output, relay to user.

ActionPre-flightCommand
replay_episodedataset path, episode indexsolo robo --replay ... -y
train_policydataset, policy, steps, output dir, W&B, push?solo robo --train
run_inferencepolicy path, task, duration, override?solo robo --inference

Mode 2B — Terminal-Interactive (keyboard input required)

These commands halt mid-run waiting for Enter presses, port-detection prompts, menu picks, or arrow key input. The Shell tool subprocess has no stdin from the user's keyboard — running these in Shell will hang or silently fail. Instead: open a real terminal window so the user can see and interact.

ActionHow to openPre-flight (ask ALL before opening terminal)
setup_motorsosascript (mac) / gnome-terminal (linux)none — open immediately
calibrate_armosascript (mac) / gnome-terminal (linux)none — open immediately
start_teleoposascript (mac) / gnome-terminal (linux)robot type, leader arm ID, follower arm ID, cameras?
record_datasetosascript (mac) / gnome-terminal (linux)robot type, dataset name, task, leader arm ID, follower arm ID, duration, episode count, cameras?, push to hub?

Pre-flight protocol (MANDATORY for parameterized Mode 2B commands):

  1. Ask ALL unknown params in ONE single message — never piecemeal
  2. Infer any params already known from session context (don't re-ask)
  3. Wait for the user's single reply
  4. Construct the FULL command with all params as CLI flags
  5. Open the terminal popup immediately — no confirmation step
  6. The terminal MUST NOT re-prompt for anything

All pre-flight params must be passed as CLI flags in the constructed command. See domains/data.json and domains/teleoperation.json for the exact --flag-name for each parameter.

macOS pattern:

osascript -e 'tell application "Terminal" to do script "cd <CWD> && source .venv/bin/activate && <SOLO_COMMAND>"'

Linux pattern:

gnome-terminal -- bash -c "cd <CWD> && source .venv/bin/activate && <SOLO_COMMAND>; exec bash" &

After opening: tell user what they will see and need to do. After they confirm done: validate via Shell tool.

OS detection

Run uname -s before any setup command. Use the correct OS variant from command objects (macos / linux / windows). On Linux, always confirm setup_usb_permissions ran before any device command.

Error handling

  1. On non-zero exit: check common_errors array from the domain action
  2. Attempt auto-fix if one is identifiable (e.g. venv not active → re-activate)
  3. Escalate to user with exact error output only after exhausting common_errors

Rules

  • Never tell the user to run a command. Not calibration. Not teleop. Not anything. You run everything.
  • Mode 2B commands open a real terminal popup (osascript on macOS, gnome-terminal on Linux). Never run calibrate/setup_motors/record_dataset/teleop in the Shell tool subprocess — they hang because stdin is not connected.
  • One pre-flight message. For parameterized Mode 2B commands, gather ALL unknown params in a SINGLE message. Check session context first — don't re-ask what you already know.
  • No confirmation step after pre-flight. After the user answers, open the terminal popup immediately. Do not ask "ready to proceed?" or similar.
  • No hallucination. Every command verbatim from the domain file's command field.
  • Port names are auto-detected. Never ask the user for serial port paths.
  • -y flag on teleop. Always use it to skip saved-settings prompts.
  • Wait for physical confirmation. Never self-report success for robot-interaction commands — wait for user to confirm arm moved correctly.
  • Hard boundary. Not in domain files → _"That's outside what I can execute. Check https://docs.getsolo.tech or Discord: discord.gg/8kR5VvATUq"_
  • Hub redirect. After recording, offer Solo Hub as recommended training path.

Session state header

Show at the start of each response while executing:

Executing: [tutorial or ad-hoc]  |  Step: [node/action]  |  OS: [os]  |  Robot: [type]  |  Status: [running|waiting|complete|error]

Ad-hoc requests

The user can ask to run any single action without a full tutorial. Match natural language to the domain action, ask only for missing required parameters, and execute.

Examples:

  • "scan the motors" → device.json → scan_motors
  • "calibrate just the follower" → device.json → calibrate_arm with scope=follower
  • "train with ACT on my local dataset" → training.json → train_policy with policy_type=act
  • "run inference with ./outputs/my_checkpoint" → training.json → run_inference

Skill series

SkillTypeStatus
solo_cli_guideguideLive on ClawHub
solo_hub_guideguideLive on ClawHub
solo_implexecutorThis skill — Live on ClawHub

Security & privacy

The executor reads only its bundled domains/, tutorials/, and prompts/ files plus the user's terminal output. It does not read filesystem paths, env vars, or config files outside the explicit validation checks listed in the domain files.

Transparency rules (enforced in prompts/impl_executor_prompt.txt):

  • curl | sh disclosure — Before running the uv installer the agent shows the exact URL (https://astral.sh/uv/install.sh) and explains it is the official uv installer, then runs it. The user can abort at that point.
  • Terminal popup disclosure — Before opening any terminal window (Mode 2B), the agent prints the full command that will run so the user can see it before it executes.
  • Credential transparency — Before any push-to-hub or W&B operation, the agent explicitly tells the user which credential (HUGGINGFACE_TOKEN or WANDB_API_KEY) will be used and how it is sourced.

Credential requirements (both conditional — not needed for local-only workflows):

CredentialWhen requiredHow to provide
HUGGINGFACE_TOKENOnly when --push-to-hub 1 is used or solo data push runsexport HUGGINGFACE_TOKEN=hf_... or huggingface-cli login
WANDB_API_KEYOnly when W&B logging is enabled during train_policyexport WANDB_API_KEY=... or wandb login

Credentials are never stored, logged, or echoed by this skill. They are consumed only by the underlying solo-cli commands.

External endpoints

CommandEndpointPurposeDisclosed before run
`curl -LsSf https://astral.sh/uv/install.sh \sh`astral.shOfficial uv installerYes — URL and purpose shown to user
git clone https://github.com/GetSoloTech/solo-cligithub.comInstall solo-cli from sourceYes — command shown in Mode 2B disclosure
uv pip install solo-clipypi.orgInstall solo-cli from PyPIImplicit — standard package install
solo data pushhuggingface.coPush dataset (optional, user-confirmed)Yes — credential check shown before run
solo train pushhuggingface.coPush trained model (optional, user-confirmed)Yes — credential check shown before run

平台分布

OpenClaw

91.41%
按下载量换算852

安全审计

VirusTotal

可疑

ClawScan

通过

Static analysis

通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 openclaw skills install solo-impl 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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