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Agent Skill · 研究检索

openmath-submit-theorem(openmath 提交定理)

openmath-submit-theorem 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-submit-theorem 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;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 16,043。

总安装 7,432周安装 316GitHub Stars 公开资料未说明下载 2,604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
1.0.7

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-submit-theorem(openmath 提交定理)
来源仓库:https://github.com/bennyzhe/openmath-submit-theorem
安装命令:
openclaw skills install openmath-submit-theorem
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-submit-theorem

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-submit-theorem 用于使用两阶段提交-显示流程向 OpenMath 平台提交证明。它支持提交证明哈希或揭示 Lean/Rocq 证明,适用于形式验证项目。

通过 clawhub 安装,命令为 openclaw skills install openmath-submit-theorem,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。适用于需要自动化证明提交或项目协作的场景。

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-submit-theorem;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

    它需要接触敏感数据,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-submit-theorem 怎么安装?

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

openmath-submit-theorem 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-submit-theorem 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

openmath-submit-theorem 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-submit-theorem 用的人多吗?

公开数据是累计安装 7,432 次,近一周安装 316 次,下载 2,604 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-submit-theorem 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

  • 需要接触敏感数据,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

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 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-submit-theorem

openmath 提交定理 · 本页

7,432—接触敏感数据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-submit-theorem
description
Submits proofs to the OpenMath platform using a two-stage commit-reveal flow. Use when the user wants to commit a proof hash or reveal a Lean/Rocq proof on the Shentu network.
version
v1.0.7
requirements
commands
side_effects

OpenMath Submit Theorem

Instructions

Use this skill for the two-stage Shentu proof submission flow.

  • Stage 1 submits a proof hash as a commitment so others can see you already know the answer without learning the proof details.
  • Stage 2 submits the proof detail to reveal and verify the same proof on-chain.
  • This is an operational skill, not an instruction-only note: it shells out to trusted local python3 and shentud, reads shared openmath-env.json, queries/broadcasts to a Shentu RPC endpoint, and uses the local OS keyring for signing flows.
  • Default: authz + feegrant from prover_address (the user's OpenMath Wallet Address).
  • 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.
  • Shentu chain/RPC settings come from SHENTU_CHAIN_ID and SHENTU_NODE_URL or built-in defaults, not from openmath-env.json.
  • The skill always uses --keyring-backend os for local key lookups and generated submission commands.
  • Direct signer fallback: generate_submission.py --mode direct.
  • Required local dependencies are python3 and shentud. Environment variables are optional overrides, not mandatory setup: OPENMATH_ENV_CONFIG, OPENMATH_SHENTUD_BIN, OPENMATH_SUBMISSION_MODE, OPENMATH_INNER_TX_FEES, OPENMATH_INNER_TX_GAS, SHENTU_CHAIN_ID, and SHENTU_NODE_URL.

For least-privilege operation, treat openmath-env.json creation or editing, shentud installation, and local key creation or recovery as manual prerequisites documented in references/. The default skill flow may run read-only checks such as command -v shentud, shentud version, or shentud keys show, but it should not auto-install binaries, auto-edit config files, or run shentud keys add as part of normal execution.

Before any action that writes openmath-env.json or creates or recovers a local key, get explicit user approval. shentud installation should stay a manual user step, guided by the reference docs rather than performed by the skill. Prefer the official Shentu releases page plus a user-local install at $HOME/bin/shentud, and use OPENMATH_SHENTUD_BIN only as an explicit fallback. Do not generate or manage mnemonics on the user's machine without that approval.

First-run gate

If the selected openmath-env.json is missing, or if it exists but is missing prover_address, agent_address, or agent_key_name, do not proceed. Follow references/init-setup.md, and treat any config write or key creation/recovery as an explicit-user-approval step, then validate:

python3 scripts/check_authz_setup.py [--config <path>]

Require Status: ready before any submission. Repeat on each new machine or workspace.

This gate is mandatory for authz-mode scripts that advance the submission flow. generate_submission.py must not produce authz proof-hash or proof-detail commands until check_authz_setup.py returns Status: ready. Read-only status polling via query_submission_status.py is exempt.

Workflow checklist

  • [ ] Manual prerequisites: If shentud is missing, use references/submission_guidelines.md for manual binary install. If openmath-env.json is missing or incomplete, or the local os key does not exist yet, use references/init-setup.md. If authz or feegrant is still missing after setup, use references/authz_setup.md. The default skill flow should not auto-edit config files, auto-install shentud, or auto-create or recover keys.
  • [ ] Env: openmath-env.json exists in ./.openmath-skills/ or ~/.openmath-skills/, and check_authz_setup.py reports Status: ready.
  • [ ] Stage 1 (Commit): Run generate_submission.py hash <theoremId> <proofPath> <proverKeyOrAddress> <proverAddr> only after the first-run gate passes; this generates the commitment hash and the corresponding shentud tx authz exec proofhash.json ... --fee-granter <prover-address> flow.
  • [ ] Wait: 5–10 s, then query_submission_status.py tx <txhash> --wait-seconds 6. Confirm proof in PROOF_STATUS_HASH_LOCK_PERIOD and record proof_id.
  • [ ] Stage 2 (Reveal): Run generate_submission.py detail <proofId> <proofPath> <proverKeyOrAddress> only after the first-run gate passes; this reveals the proof detail and emits the corresponding shentud tx authz exec proofdetail.json ... --fee-granter <prover-address> flow. Do not wait for hash lock expiry.
  • [ ] Verify: Wait 5–10 s, then query_submission_status.py theorem <theoremId> --wait-seconds 6. Confirm theorem reaches THEOREM_STATUS_PASSED.

Scripts

ScriptCommandUse when
Authz readinesspython3 scripts/check_authz_setup.py [--config <path>]Before first submission and when changing env; validates CLI, keys, RPC, authz, feegrant.
Stage 1 commandspython3 scripts/generate_submission.py hash <theoremId> <proofPath> <proverKeyOrAddress> <proverAddr>Generating proofhash.json and the broadcast command for the commitment stage. In authz mode, refuses to continue until the first-run gate passes.
Stage 2 commandspython3 scripts/generate_submission.py detail <proofId> <proofPath> <proverKeyOrAddress>Generating proofdetail.json and the broadcast command for the reveal stage (use proof_id from Stage 1). In authz mode, refuses to continue until the first-run gate passes.
Query txpython3 scripts/query_submission_status.py tx <txhash> [--wait-seconds 6]After broadcast to confirm inclusion.
Query theorempython3 scripts/query_submission_status.py theorem <theoremId> [--wait-seconds 6]Final status check.
Proof hash (debug)python3 scripts/calculate_proof_hash.py <theoremId> <proverAddress> <proofContentOrFile>Standalone hash check; normally used by generate_submission.

submission_config.py loads and validates only the identity/authz fields in openmath-env.json using the shared config resolution order above. Chain/RPC settings come from SHENTU_CHAIN_ID and SHENTU_NODE_URL.

Reference split:

  • references/submission_guidelines.md: manual binary install, pre-submission checks, and the two-stage submit flow
  • references/init-setup.md: openmath-env.json, local key setup, and the normal website authorization flow
  • references/authz_setup.md: manual authz + feegrant CLI fallback after setup

Notes

  • Authz: Default flow uses shentud tx authz exec with --fee-granter <prover-address>. For direct signer use --mode direct on generate_submission.py.
  • Commit-reveal: Stage 1 publishes only the proof hash as a commitment, which reduces proof leakage and front-running risk while reserving your claim. Stage 2 reveals the full proof detail for verification.
  • Key material: Treat local key creation or recovery as a manual step. If shentud keys add is needed, show the user the documented commands in the references instead of running them from the skill, and warn that mnemonics or recovery material may be shown.
  • Binary setup: Manual binary setup is required. Prefer a trusted release binary installed at $HOME/bin/shentud. Verify with command -v shentud and shentud version before submission.
  • Config editing: Manual config setup is preferred. Point the user to references/init-setup.md and references/openmath-env.example.json instead of having the skill rewrite openmath-env.json.
  • Binary resolution: Check the plain shentud command first. If shentud already works from PATH, do not force a separate binary path. Set OPENMATH_SHENTUD_BIN only as a fallback when the default shentud lookup is missing or broken and you need a specific trusted binary.
  • Advanced env vars: OPENMATH_SUBMISSION_MODE changes the default generate_submission.py --mode (authz by default). OPENMATH_INNER_TX_FEES and OPENMATH_INNER_TX_GAS override the generated inner --generate-only tx fees/gas in authz mode.
  • Local shell env: PATH affects local python3 / shentud discovery, and references may use $HOME/bin when showing non-persistent local install examples.
  • Block wait: After each broadcast wait ~5–10 s before querying.

References

Load when needed (one level from this file):

平台分布

OpenClaw

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按下载量换算2,159

安全审计

ClawScan

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Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

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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