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Get Started Free →用于调取微信实名认证信息的执行技能。通过法院发协助函或律师持调查令向财付通支付科技有限公司申请调取目标微信号的实名认证信息，获取真实姓名、身份证号等权威身份证明。此技能仅适用于已确认实名认证的微信号。
.claude/skills/thomasmoreai-wechat-query-realname-info/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 27% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 21% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 6% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 7% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 25% | 0% |
本技能指导律师或法院工作人员通过合法程序向财付通（微信支付运营主体）调取微信号实名认证信息，获取包含真实姓名、身份证号码、微信号等信息的权威复函，是证明微信使用者身份的最有力证据。
根据调研文章，对于已实名认证的微信号，可以通过法院发协助函或律师持调查令的方式，向财付通支付科技有限公司申请调取实名认证信息。财付通的复函中会清晰显示认证者的真实姓名、身份证号码、微信号等信息，其法律效力极高。
通过法院或律师渠道向财付通申请调取实名认证信息。
执行路径：
| 申请人类型 | 所需文件 | 申请对象 | |-----------|---------|---------| | 法院 | 协助执行函/调查函 | 财付通支付科技有限公司 | | 律师 | 律师调查令（法院签发） | 财付通支付科技有限公司 |
调取信息内容：
财付通联系信息：
json{ "$schema": "http://json-schema.org/draft-07/schema#", "title": "WeChat Realname Info Query", "type": "object", "properties": { "applicant_type": { "type": "string", "enum": ["court", "lawyer"], "description": "申请人类型" }, "case_info": { "type": "object", "properties": { "case_number": {"type": "string", "description": "案号"}, "court_name": {"type": "string", "description": "受理法院"}, "case_type": {"type": "string", "description": "案件类型"} }, "required": ["case_number", "court_name"] }, "target_wechat_id": { "type": "string", "description": "目标微信号" }, "target_user_alias": { "type": "string", "description": "目标用户的推测/已知姓名（如有）" }, "application_purpose": { "type": "string", "description": "申请目的/证明事项" } }, "required": ["applicant_type", "case_info", "target_wechat_id"] }
json{ "query_result": { "status": "success|pending|rejected", "tenpay_response": { "response_date": "ISO 8601 date", "response_reference": "复函编号", "realname": "真实姓名", "id_number": "身份证号", "wechat_id": "微信号", "verification_status": "verified|unverified" }, "evidence_usability": { "has_official_seal": true, "legal_validity": "high|medium|low", "recommended_usage": "direct_evidence|supporting_evidence" } } }
风险提示：
合规要求：
证据效力：
| 属性 | 值 | |-----|-----| | logic_origin | 《如何在法庭上证明微信使用者的真实身份》第02节 | | evolution_value | 获取最权威的身份证明材料，直接证明微信使用者的真实身份，显著提升证据效力 | | execution_context | 诉讼中、诉前调查阶段 |
输入：
json{ "applicant_type": "lawyer", "case_info": { "case_number": "（2025）粤0305民初1234号", "court_name": "深圳市南山区人民法院", "case_type": "民间借贷纠纷" }, "target_wechat_id": "wxid_debtor2025", "target_user_alias": "张三", "application_purpose": "证明微信号 wxid_debtor2025 的使用者真实身份，用于确认借贷关系当事人" }
期望输出：
json{ "query_result": { "status": "success", "tenpay_response": { "response_date": "2025-03-15", "response_reference": "财付通复函〔2025〕第XXX号", "realname": "张某三", "id_number": "440***********1234", "wechat_id": "wxid_debtor2025", "verification_status": "verified" }, "evidence_usability": { "has_official_seal": true, "legal_validity": "high", "recommended_usage": "direct_evidence" } } }
输入：
json{ "applicant_type": "court", "case_info": { "case_number": "（2025）京0108民初5678号", "court_name": "北京市海淀区人民法院", "case_type": "买卖合同纠纷" }, "target_wechat_id": "seller_wxid888", "application_purpose": "核实交易对方身份，确认合同相对方" }
期望输出：
json{ "query_result": { "status": "success", "tenpay_response": { "response_date": "2025-02-20", "response_reference": "协查函复〔2025〕第XXX号", "realname": "李某四", "id_number": "110***********5678", "wechat_id": "seller_wxid888", "verification_status": "verified" }, "evidence_usability": { "has_official_seal": true, "legal_validity": "high", "recommended_usage": "direct_evidence" } } }
wechat-verify-realname: 调取前先确认微信号是否已实名认证wechat-apply-transfer-proof: 如存在转账记录，可同时申请电子回单作为补充证据wechat-present-evidence: 获取复函后在法庭上展示证据的规范流程| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | pass→pass | 21,797 | 18,777 | -14% | 1 | 1 | 0% | 3,290 | 4,895 | +49% | 0 | 0 | — |
case-07 | fail→pass | 16,635 | 6,594 | -60% | 1 | 1 | 0% | 2,543 | 3,234 | +27% | 0 | 0 | — |
case-02 | pass→pass | 13,117 | 8,780 | -33% | 1 | 1 | 0% | 2,027 | 3,406 | +68% | 0 | 0 | — |
case-03 | pass→fail | 24,931 | 20,099 | -19% | 1 | 1 | 0% | 3,535 | 4,912 | +39% | 0 | 0 | — |
case-04 | fail→pass | 16,035 | 5,501 | -66% | 1 | 1 | 0% | 2,515 | 3,042 | +21% | 0 | 0 | — |
case-05 | fail→pass | 13,397 | 5,317 | -60% | 1 | 1 | 0% | 2,691 | 2,852 | +6% | 0 | 0 | — |
case-06 | fail→pass | 14,468 | 4,702 | -68% | 1 | 1 | 0% | 2,593 | 2,771 | +7% | 0 | 0 | — |
case-08 | fail→pass | 11,169 | 3,636 | -67% | 1 | 1 | 0% | 2,025 | 2,524 | +25% | 0 | 0 | — |
case-09 | fail→pass | 9,225 | 2,940 | -68% | 1 | 1 | 0% | 1,724 | 2,479 | +44% | 0 | 0 | — |
case-10 | fail→pass | 12,224 | 5,336 | -56% | 1 | 1 | 0% | 2,419 | 2,979 | +23% | 0 | 0 | — |
case-11 | fail→pass | 16,699 | 5,977 | -64% | 1 | 1 | 0% | 3,262 | 2,866 | -12% | 0 | 0 | — |
case-12 | fail→pass | 8,583 | 4,909 | -43% | 1 | 1 | 0% | 1,819 | 2,918 | +60% | 0 | 0 | — |
case-13 | fail→pass | 10,794 | 5,367 | -50% | 1 | 1 | 0% | 1,667 | 2,976 | +79% | 0 | 0 | — |
case-14 | pass→pass | 13,872 | 7,097 | -49% | 1 | 1 | 0% | 2,224 | 3,341 | +50% | 0 | 0 | — |
case-15 | pass→pass | 12,379 | 8,489 | -31% | 1 | 1 | 0% | 2,123 | 3,356 | +58% | 0 | 0 | — |
case-16 | pass→pass | 11,085 | 6,861 | -38% | 1 | 1 | 0% | 1,883 | 2,941 | +56% | 0 | 0 | — |
case-17 | pass→pass | 13,086 | 3,942 | -70% | 1 | 1 | 0% | 1,746 | 2,568 | +47% | 0 | 0 | — |
case-18 | pass→pass | 14,664 | 8,069 | -45% | 1 | 1 | 0% | 2,344 | 3,025 | +29% | 0 | 0 | — |
case-19 | pass→pass | 18,060 | 10,142 | -44% | 1 | 1 | 0% | 2,721 | 3,456 | +27% | 0 | 0 | — |
case-20 | pass→pass | 19,815 | 10,958 | -45% | 1 | 1 | 0% | 2,716 | 3,596 | +32% | 0 | 0 | — |
case-21 | fail→pass | 13,429 | 6,197 | -54% | 1 | 1 | 0% | 2,149 | 2,641 | +23% | 0 | 0 | — |
case-22 | pass→pass | 16,285 | 11,339 | -30% | 1 | 1 | 0% | 2,400 | 3,469 | +45% | 0 | 0 | — |
case-23 | pass→pass | 16,561 | 10,583 | -36% | 1 | 1 | 0% | 2,516 | 3,580 | +42% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted. The headline lift of +43 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.