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Agent Skill · 研究检索

openmath-claim-reward(openmath 领取奖励)

openmath-claim-reward 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。

openmath-claim-reward 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,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 19,623。

总安装 5,532周安装 226GitHub Stars 公开资料未说明下载 1,772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
1.0.4

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:openmath-claim-reward(openmath 领取奖励)
来源仓库:https://github.com/bennyzhe/openmath-claim-reward
安装命令:
openclaw skills install openmath-claim-reward
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 openmath-claim-reward

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。

openmath-claim-reward 用于索赔从 OpenMath 平台获得的奖励,适合查询可领取的导入/证明奖励。它支持提取已验证的申图奖励,适用于参与形式验证项目的用户。

通过 clawhub 安装,命令为 openclaw skills install openmath-claim-reward,需结合来源仓库和 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。适用于需要自动化奖励申领或项目结算的场景。

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 openmath-claim-reward;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

openmath-claim-reward 怎么安装?

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

openmath-claim-reward 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。

openmath-claim-reward 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 openmath-claim-reward 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。

openmath-claim-reward 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。

openmath-claim-reward 用的人多吗?

公开数据是累计安装 5,532 次,近一周安装 226 次,下载 1,772 次。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。

openmath-claim-reward 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。

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。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

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
openmath-claim-reward

openmath 领取奖励 · 本页

5,532—执行命令MIT-0通过

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
openmath-claim-reward
description
Claims earned rewards from the OpenMath platform. Use when the user wants to query claimable imported/proof rewards or withdraw verified Shentu rewards after a proof has passed verification.
version
v1.0.4
requirements
commands
environment_variables
side_effects

OpenMath Claim Reward

Instructions

Query and withdraw rewards for verified OpenMath activity on Shentu. Flow: query bounty rewards → withdraw via bounty withdraw-rewards → wait 5–10 s → re-query. Uses SHENTU_CHAIN_ID and SHENTU_NODE_URL for runtime chain/RPC settings, with built-in mainnet defaults if unset. Shared config resolution order: --config <path> → OPENMATH_ENV_CONFIG → ./.openmath-skills/openmath-env.json → ~/.openmath-skills/openmath-env.json. If OPENMATH_ENV_CONFIG is set, treat it as the selected config path. If that file is missing or invalid, stop and fix it instead of silently falling back.

Requires trusted local python3 and shentud binaries on PATH. Read-only reward queries shell out to local shentud and query a Shentu RPC endpoint. Withdrawals additionally rely on the local OS keyring via shentud --keyring-backend os. Before any withdrawal, confirm the key name, resolved address, and node URL with the user.

First-run gate

If the user already provided an address explicitly, reward query can run immediately.

If no address was provided, auto-discover prover_address from OPENMATH_ENV_CONFIG when it is set; otherwise check only ./.openmath-skills/openmath-env.json or ~/.openmath-skills/openmath-env.json. If no usable config exists, or if the config exists but prover_address is missing, do not guess the address. Follow references/init-setup.md.

For withdrawals, do not proceed until a local os keyring key is known for the same address. Do not broadcast a withdrawal until the user confirms the matching key name/address and the RPC node they want to use.

Workflow checklist

  • [ ] Env: If needed, export SHENTU_CHAIN_ID / SHENTU_NODE_URL, or set OPENMATH_ENV_CONFIG to a specific openmath-env.json; otherwise use the built-in mainnet defaults and standard config auto-discovery.
  • [ ] Address: Use an explicit address, or let query_reward_status.py rewards auto-discover prover_address from OPENMATH_ENV_CONFIG or the standard openmath-env.json locations.
  • [ ] Query: Run query_reward_status.py rewards [address] (or shentud q bounty rewards <address> --node <shentu_node_url>) to see imported_rewards and/or proof_rewards.
  • [ ] Withdraw: If any bucket is non-empty, first make sure a local os keyring key controls the same address, confirm shentud keys show <your-key> -a --keyring-backend os matches the reward address, then run shentud tx bounty withdraw-rewards --from <your-key> --keyring-backend os --chain-id <shentu_chain_id> --node <shentu_node_url> --gas-prices 0.025uctk --gas-adjustment 2.0 --gas auto (use SHENTU_CHAIN_ID / SHENTU_NODE_URL or the built-in defaults).
  • [ ] Wait: 5–10 s for block inclusion.
  • [ ] Re-query: Run query_reward_status.py tx <txhash> --wait-seconds 6, then query_reward_status.py rewards <address> --wait-seconds 6 to confirm withdrawal; empty buckets are reported as zero, not error.

Scripts

ScriptCommandUse when
Query rewardspython3 scripts/query_reward_status.py rewards [address] [--config <path>] [--wait-seconds 0]Checking claimable imported_rewards and proof_rewards for an address, or auto-discovering prover_address from --config, OPENMATH_ENV_CONFIG, or the default config locations when omitted.
Query txpython3 scripts/query_reward_status.py tx <txhash> [--wait-seconds 6]After withdraw broadcast to confirm inclusion.

Withdraw is done with raw shentud tx bounty withdraw-rewards --keyring-backend os (see workflow above).

Notes

  • Buckets: imported_rewards (theorem imported/referenced), proof_rewards (proofs verified). One withdraw pulls both if present.
  • Mainnet: Default --chain-id shentu-2.2 --node https://rpc.shentu.org:443.
  • Config override: Set OPENMATH_ENV_CONFIG=/path/to/openmath-env.json or use --config if you want a non-default config path.
  • Requirements: Requires trusted local python3 and shentud on PATH.
  • Env vars: OPENMATH_ENV_CONFIG, SHENTU_CHAIN_ID, and SHENTU_NODE_URL are optional overrides, not required for the default mainnet flow.
  • Keyring: Always use --keyring-backend os for reward withdrawal commands generated from this skill.
  • Trust boundary: Reward queries shell out to local shentud; withdrawals also sign through the local OS keyring. Verify the key name, resolved address, and RPC/node URL before broadcasting.

References

Load when needed (one level from this file):

Identity setup for theorem submission still lives in openmath-submit-theorem, but reward querying itself does not require openmath-env.json.

平台分布

OpenClaw

91.14%
按下载量换算1,615

安全审计

VirusTotal

通过

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 openmath-claim-reward 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。目前只有一个公开来源,建议同时核对源仓库。

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