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openmath-claim-reward（openmath 领取奖励）

Agent Skill

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。

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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

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：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。

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ClawHubOpenClaw

openclaw skills install openmath-claim-reward

简介

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。

SKILL.md

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

Script	Command	Use when
Query rewards	`python3 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 tx	`python3 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):

references/init-setup.md — Reward address discovery and withdraw-key setup.
references/reward_claim_flow.md — Address-based buckets, withdraw behavior, and on-chain claim flow.

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

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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。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

GitHub 仓库

github.com

/bennyzhe/openmath-claim-reward

SKILL.md 来源

clawhub.ai

/bennyzhe/openmath-claim-reward

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