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openmath-submit-theoremopenmath 提交定理

Agent Skill

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。

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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

2

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-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。

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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

简介

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。

SKILL.md

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

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