add guided skill and web consultation surfaces
This commit is contained in:
732642856
@@ -78,6 +78,22 @@ python3 scripts/user_invocation_acceptance_check.py
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该命令必须返回 `"status": "pass"`，并显式列出 VedAstro / PyJHora-JHora / jyotishganit 的可用、partial 或 blocked 状态；否则不得声称云端 Git 仓库调用已可高质量使用。
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### 首次调用与降级合同
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普通用户不必先理解 API、MCP、分盘或校时方法。Skill/MCP 首次调用必须先使用
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`skill_onboarding`：缺出生字段时只收集日期、时间、经纬度；出生时间有误差时返回
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`rectification` 的选择题问卷；时间明确时进入 `direct_chart`。不得要求用户先提交长篇
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人生事件表。
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安装或运行异常时调用 `skill_doctor`。它只报告本地资产与外部适配器 readiness，不得把
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adapter available 解释为已完成 VedAstro、PyJHora/JHora 或 jyotishganit raw-oracle 校验。
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每个工作流结果必须包含 `execution_status`：
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- `official_verified`：仅此状态可说 VedAstro 官方 raw evidence 已被使用；
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- `official_blocked`：官方请求失败、额度/网络/超时受阻；
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- `local_fallback`：本地计算继续可用，但不能称为官方云端闭环。
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**强制工作流**（完整规范 → `references/ai-reading-workflow-prompt.md` v5.1.0）：
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0. **阶段负一**：问题类型路由（事业/婚恋/财务/应期/历史验证/综合解盘）→ 必须先读 `references/strict-workflow-router.md`，按对应 strict checklist 执行；用户不需要主动点名高级技法。
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+20
-1
@@ -45,6 +45,7 @@ from mcp.server.fastmcp import FastMCP
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from functional_benefics import derive_functional_benefic_malefic
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from vedastro_priority import official_snapshot_evidence
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from unified_consultation_orchestrator import UnifiedConsultationOrchestrator
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from skill_experience import build_skill_doctor, build_skill_onboarding, summarize_execution_status
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load_local_env(SCRIPT_DIR)
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@@ -718,7 +719,7 @@ def _execute_mcp_consultation_workflow(
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from jyotish_api_server import JyotishAPIHandler, execute_consultation_workflow
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handler = JyotishAPIHandler.__new__(JyotishAPIHandler)
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return execute_consultation_workflow(
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result = execute_consultation_workflow(
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handler,
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body={
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"question": question,
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@@ -740,6 +741,9 @@ def _execute_mcp_consultation_workflow(
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},
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surface="skill_mcp",
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)
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if isinstance(result, dict):
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result["execution_status"] = summarize_execution_status(result)
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return result
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def _safe_get(data: Dict[str, Any], *path: str) -> Any:
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@@ -4353,6 +4357,21 @@ def life_event_graph(
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}
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# ============================================================================
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# Skill experience tools
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@mcp.tool()
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def skill_onboarding(payload: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
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"""Return the minimal next input or active rectification question set."""
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return build_skill_onboarding(payload)
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@mcp.tool()
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def skill_doctor() -> Dict[str, Any]:
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"""Check local Skill assets and external adapter readiness."""
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return build_skill_doctor()
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# ============================================================================
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# Resources
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# ============================================================================
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@@ -32,6 +32,18 @@ try:
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from scripts.unified_consultation_orchestrator import UnifiedConsultationOrchestrator
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except ModuleNotFoundError: # pragma: no cover - script execution path
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from unified_consultation_orchestrator import UnifiedConsultationOrchestrator
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try:
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from scripts.skill_experience import (
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build_rectification_questionnaire,
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score_rectification_answers,
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summarize_execution_status,
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)
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except ModuleNotFoundError: # pragma: no cover - script execution path
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from skill_experience import (
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build_rectification_questionnaire,
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score_rectification_answers,
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summarize_execution_status,
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)
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try:
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from scripts.western_oracle_adapter import build_packet_from_oracle_payload
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except ModuleNotFoundError: # pragma: no cover - script execution path
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@@ -54,6 +66,22 @@ _ASYNC_JOB_EXECUTOR = ThreadPoolExecutor(
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_ASYNC_JOB_CAPACITY = threading.BoundedSemaphore(_ASYNC_JOB_WORKERS + _ASYNC_JOB_QUEUE_SIZE)
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def build_evidence_packet_view(job_record: dict | None) -> dict:
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"""Public, token-protected job view. Excludes prompt internals and raw input."""
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job_record = job_record or {}
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result = job_record.get('result')
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result = result if isinstance(result, dict) else {}
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return {
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'scope': 'evidence_packet_view',
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'job_id': job_record.get('job_id'),
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'status': job_record.get('status', 'unknown'),
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'execution_status': summarize_execution_status(result),
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'machine_evidence_packet': result.get('machine_evidence_packet') or {},
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'technique_audit': result.get('technique_audit') or result.get('technique_audit_table') or [],
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'warnings': result.get('warnings') or [],
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}
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def _submit_background_job(callback):
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if not _ASYNC_JOB_CAPACITY.acquire(blocking=False):
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raise JobQueueFull('Async job queue is full')
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@@ -900,6 +928,17 @@ class JyotishAPIHandler(BaseHTTPRequestHandler):
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def _error_json(self, message, status=500, error_code='ERR_INTERNAL'):
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self._json({'success': False, 'error': message, 'error_code': error_code}, status)
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def _html(self, content, status=200):
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encoded = content.encode('utf-8')
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self.send_response(status)
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self.send_header('Content-Type', 'text/html; charset=utf-8')
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self._send_cors_headers()
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self.send_header('X-Content-Type-Options', 'nosniff')
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self.send_header('Cache-Control', 'no-store')
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self.send_header('Content-Length', str(len(encoded)))
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self.end_headers()
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self.wfile.write(encoded)
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def _send_cors_headers(self):
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origin = self.headers.get('Origin')
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allowed = getattr(self.server, 'allowed_origins', DEFAULT_ALLOWED_ORIGINS)
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@@ -979,7 +1018,19 @@ class JyotishAPIHandler(BaseHTTPRequestHandler):
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path = urlparse(self.path).path
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try:
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self._enforce_request_security()
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if path == '/api/health':
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if path == '/evidence':
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page = Path(REPO_ROOT) / 'web' / 'evidence_packet.html'
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if not page.is_file():
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self._error_json('Evidence Packet page unavailable', 404, 'ERR_NOT_FOUND')
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else:
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self._html(page.read_text(encoding='utf-8'))
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elif path == '/rectification':
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page = Path(REPO_ROOT) / 'web' / 'rectification.html'
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if not page.is_file():
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self._error_json('Rectification page unavailable', 404, 'ERR_NOT_FOUND')
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else:
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self._html(page.read_text(encoding='utf-8'))
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elif path == '/api/health':
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swisseph_available = False
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swisseph_version = None
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try:
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@@ -1030,6 +1081,20 @@ class JyotishAPIHandler(BaseHTTPRequestHandler):
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self._error_json('Not found', 404, 'ERR_NOT_FOUND')
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else:
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self._json(result)
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elif path.startswith('/api/evidence_packet/chart/'):
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job_id = path.rsplit('/', 1)[-1]
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result = self._get_chart_job(job_id)
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if result is None:
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self._error_json('Not found', 404, 'ERR_NOT_FOUND')
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else:
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self._json(build_evidence_packet_view(result))
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elif path.startswith('/api/evidence_packet/high_rigor_workflow/'):
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job_id = path.rsplit('/', 1)[-1]
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result = self._get_high_rigor_job(job_id)
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if result is None:
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self._error_json('Not found', 404, 'ERR_NOT_FOUND')
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else:
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self._json(build_evidence_packet_view(result))
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elif path == '/api/real_case_revalidation':
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self._json(self._real_case_revalidation())
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else:
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@@ -1146,6 +1211,14 @@ class JyotishAPIHandler(BaseHTTPRequestHandler):
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elif path == '/api/aspects':
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result = self._compute_aspects(body)
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self._json(result)
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elif path == '/api/rectification/questionnaire':
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self._json(build_rectification_questionnaire(body))
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elif path == '/api/rectification/answers':
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questionnaire = body.get('questionnaire')
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answers = body.get('answers')
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if not isinstance(questionnaire, dict) or not isinstance(answers, dict):
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raise BadRequest('questionnaire and answers must be JSON objects')
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self._json(score_rectification_answers(questionnaire, answers))
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elif path == '/api/rectification_gate':
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result = self._compute_rectification_gate(body)
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self._json(result)
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@@ -0,0 +1,135 @@
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"""Stable user-facing contracts shared by Skill and MCP entry points."""
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from __future__ import annotations
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from pathlib import Path
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from typing import Any
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from scripts.active_rectification_questions import build_questionnaire, score_answers
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from scripts.diagnose_external_engine_adapters import build_report as adapter_report
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ROOT = Path(__file__).resolve().parents[1]
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_REQUIRED_BIRTH_FIELDS = ("year", "month", "day", "hour", "minute", "lat", "lon")
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def _missing_birth_fields(payload: dict[str, Any]) -> list[str]:
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return [field for field in _REQUIRED_BIRTH_FIELDS if payload.get(field) is None]
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def build_skill_onboarding(payload: dict[str, Any] | None = None) -> dict[str, Any]:
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"""Return the next minimal user action; never infer missing birth inputs."""
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payload = payload or {}
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missing = _missing_birth_fields(payload)
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if missing:
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return {
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"scope": "skill_onboarding",
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"status": "needs_birth_data",
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"entry_mode": "pending",
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"missing_fields": missing,
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"next_action": "collect_birth_data",
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"input_template": {
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"year": "YYYY", "month": "MM", "day": "DD",
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"hour": "0-23", "minute": "0-59", "lat": "decimal", "lon": "decimal",
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"time_uncertainty_minutes": "optional; use when birth time is approximate",
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"question": "optional; career, relationship, wealth, health, general",
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},
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}
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uncertainty = int(payload.get("time_uncertainty_minutes") or 0)
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if uncertainty > 0:
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birth_time = (
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f"{int(payload['year']):04d}-{int(payload['month']):02d}-{int(payload['day']):02d} "
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f"{int(payload['hour']):02d}:{int(payload['minute']):02d}"
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)
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questionnaire = build_questionnaire(birth_time, uncertainty_minutes=uncertainty)
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first_question = questionnaire.get("questions", [{}])[0]
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return {
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"scope": "skill_onboarding",
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"status": "ready",
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"entry_mode": "rectification",
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"next_action": "run_rectification_questionnaire",
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"first_question": first_question,
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"questionnaire": questionnaire,
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}
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return {
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"scope": "skill_onboarding",
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"status": "ready",
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"entry_mode": "direct_chart",
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"next_action": "run_consultation_workflow",
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"question": str(payload.get("question") or ""),
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}
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def build_rectification_questionnaire(payload: dict[str, Any]) -> dict[str, Any]:
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"""Build the active-choice questionnaire from a minimal approximate time."""
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required = ("year", "month", "day", "hour", "minute")
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missing = [field for field in required if payload.get(field) is None]
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if missing:
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raise ValueError(f"missing rectification fields: {', '.join(missing)}")
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birth_time = (
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f"{int(payload['year']):04d}-{int(payload['month']):02d}-{int(payload['day']):02d} "
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f"{int(payload['hour']):02d}:{int(payload['minute']):02d}"
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)
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uncertainty = max(int(payload.get("time_uncertainty_minutes") or 30), 1)
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step = max(int(payload.get("step_minutes") or 1), 1)
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return build_questionnaire(birth_time, uncertainty_minutes=uncertainty, step_minutes=step)
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def score_rectification_answers(questionnaire: dict[str, Any], answers: dict[str, str]) -> dict[str, Any]:
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"""Score user choices; preserves the boundary against false minute precision."""
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return score_answers(questionnaire, answers or {})
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def build_skill_doctor() -> dict[str, Any]:
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"""Expose readiness, not an unsupported promise that all engines are usable."""
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assets = {
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"skill_instructions": (ROOT / "SKILL.md").is_file(),
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"mcp_server": (ROOT / "mcp_server.py").is_file(),
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"native_engine": (ROOT / "scripts" / "jyotish_engine.py").is_file(),
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"unified_orchestrator": (ROOT / "scripts" / "unified_consultation_orchestrator.py").is_file(),
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}
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adapters = adapter_report()
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adapter_status = adapters.get("status", "blocked")
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return {
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"scope": "skill_doctor",
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"status": "ready" if all(assets.values()) and adapter_status == "ready" else "degraded",
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"core_assets": assets,
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"external_engine_adapters": adapters,
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"boundary": "Readiness only. An available adapter is not external raw-oracle verification.",
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}
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def _vedastro_status(result: dict[str, Any]) -> str:
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engines = result.get("external_engine_cross_validation")
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if isinstance(engines, dict):
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engines = engines.get("engines")
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vedastro = engines.get("VedAstro") if isinstance(engines, dict) else None
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if isinstance(vedastro, dict):
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return str(vedastro.get("status") or "")
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return ""
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def summarize_execution_status(result: dict[str, Any] | None) -> dict[str, Any]:
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"""Normalize official/local evidence state for every conversational surface."""
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result = result or {}
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fallback_reason = str(result.get("fallback_reason") or "")
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vedastro = _vedastro_status(result)
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raw_status = str(result.get("official_evidence_status") or "")
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if raw_status == "official_verified" or vedastro == "official_verified":
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official, source = "official_verified", "official_raw"
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elif fallback_reason or vedastro in {"local_fallback", "official_blocked", "blocked"}:
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official, source = "official_blocked", "local_fallback"
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else:
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official, source = "official_not_requested", "local_or_unverified"
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return {
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"scope": "execution_status",
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"official_evidence_status": official,
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"calculation_source": source,
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"fallback_reason": fallback_reason or None,
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"allowed_claims": ["official_verified", "official_blocked", "local_fallback"],
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"claim_boundary": (
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"Only official_verified permits claims that VedAstro official raw evidence was used."
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),
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}
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@@ -0,0 +1,59 @@
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import json
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from pathlib import Path
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from scripts import jyotish_api_server as api
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def test_evidence_packet_view_exposes_only_auditable_result_sections():
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packet = api.build_evidence_packet_view({
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"job_id": "job_1",
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"status": "completed",
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"result": {
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"fallback_reason": "VedAstro official snapshot blocked: timeout",
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"machine_evidence_packet": {"status": "draft", "metadata": {"capture_id": "x"}},
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"technique_audit": [{"technique": "D9", "status": "used"}],
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"ai_prompt_pack": {"prompt_zh": "internal prompt"},
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},
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})
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assert packet["job_id"] == "job_1"
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assert packet["execution_status"]["official_evidence_status"] == "official_blocked"
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assert packet["machine_evidence_packet"]["metadata"]["capture_id"] == "x"
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assert "ai_prompt_pack" not in packet
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def test_async_job_route_extracts_id_before_loading(monkeypatch, tmp_path):
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record = {"job_id": "chart_abc", "status": "completed", "result": {}}
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monkeypatch.setattr(api, "_load_async_job_record", lambda *args, **kwargs: record)
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handler = api.JyotishAPIHandler.__new__(api.JyotishAPIHandler)
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handler.path = "/api/chart/jobs/chart_abc"
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handler.headers = {"Origin": ""}
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handler._enforce_request_security = lambda: None
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captured = {}
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handler._json = lambda data, status=200: captured.update(data=data, status=status)
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handler._error_json = lambda message, status=500, error_code="ERR_INTERNAL": captured.update(error=error_code, status=status)
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handler._job_access_token = lambda: "token"
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handler.do_GET()
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assert captured["status"] == 200
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assert captured["data"]["job_id"] == "chart_abc"
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def test_evidence_packet_page_is_present_and_does_not_embed_birth_data():
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page = Path(api.REPO_ROOT) / "web" / "evidence_packet.html"
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source = page.read_text(encoding="utf-8")
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assert "Evidence Packet" in source
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assert "access token" in source
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assert "birth" not in source.lower()
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def test_rectification_page_uses_choice_questionnaire_contract():
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page = Path(api.REPO_ROOT) / "web" / "rectification.html"
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source = page.read_text(encoding="utf-8")
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assert "/api/rectification/questionnaire" in source
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assert "/api/rectification/answers" in source
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assert "候选簇排序" in source
|
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@@ -0,0 +1,82 @@
|
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from scripts.skill_experience import (
|
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build_rectification_questionnaire,
|
||||
build_skill_doctor,
|
||||
build_skill_onboarding,
|
||||
score_rectification_answers,
|
||||
summarize_execution_status,
|
||||
)
|
||||
|
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|
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def test_onboarding_requests_only_missing_birth_fields():
|
||||
packet = build_skill_onboarding({"year": 1993, "month": 4, "day": 17})
|
||||
|
||||
assert packet["status"] == "needs_birth_data"
|
||||
assert packet["entry_mode"] == "pending"
|
||||
assert packet["missing_fields"] == ["hour", "minute", "lat", "lon"]
|
||||
assert packet["next_action"] == "collect_birth_data"
|
||||
|
||||
|
||||
def test_onboarding_selects_rectification_for_uncertain_time():
|
||||
packet = build_skill_onboarding({
|
||||
"year": 1993,
|
||||
"month": 4,
|
||||
"day": 17,
|
||||
"hour": 14,
|
||||
"minute": 49,
|
||||
"lat": 36.68,
|
||||
"lon": 114.35,
|
||||
"time_uncertainty_minutes": 20,
|
||||
})
|
||||
|
||||
assert packet["status"] == "ready"
|
||||
assert packet["entry_mode"] == "rectification"
|
||||
assert packet["next_action"] == "run_rectification_questionnaire"
|
||||
assert packet["first_question"]
|
||||
|
||||
|
||||
def test_execution_status_makes_official_fallback_machine_readable():
|
||||
status = summarize_execution_status({
|
||||
"fallback_reason": "VedAstro official snapshot blocked: official_snapshot_budget_exhausted",
|
||||
"external_engine_cross_validation": {
|
||||
"engines": {"VedAstro": {"status": "local_fallback"}}
|
||||
},
|
||||
})
|
||||
|
||||
assert status["official_evidence_status"] == "official_blocked"
|
||||
assert status["calculation_source"] == "local_fallback"
|
||||
assert status["fallback_reason"] == "VedAstro official snapshot blocked: official_snapshot_budget_exhausted"
|
||||
assert "official_verified" in status["allowed_claims"]
|
||||
|
||||
|
||||
def test_doctor_has_machine_readable_core_and_adapter_state():
|
||||
packet = build_skill_doctor()
|
||||
|
||||
assert packet["scope"] == "skill_doctor"
|
||||
assert "core_assets" in packet
|
||||
assert "external_engine_adapters" in packet
|
||||
assert packet["status"] in {"ready", "degraded"}
|
||||
|
||||
|
||||
def test_mcp_exposes_skill_experience_tools():
|
||||
import mcp_server
|
||||
|
||||
onboarding = mcp_server.skill_onboarding({})
|
||||
doctor = mcp_server.skill_doctor()
|
||||
|
||||
assert onboarding["scope"] == "skill_onboarding"
|
||||
assert doctor["scope"] == "skill_doctor"
|
||||
|
||||
|
||||
def test_rectification_contract_generates_and_scores_choice_answers():
|
||||
questionnaire = build_rectification_questionnaire({
|
||||
"year": 1993, "month": 4, "day": 17, "hour": 14, "minute": 49,
|
||||
"time_uncertainty_minutes": 20,
|
||||
})
|
||||
scored = score_rectification_answers(questionnaire, {
|
||||
"education_environment_shift": "A",
|
||||
"health_crisis_or_low_period": "C",
|
||||
})
|
||||
|
||||
assert questionnaire["scope"] == "active_birth_time_rectification_questionnaire"
|
||||
assert scored["scope"] == "active_birth_time_rectification_scoring"
|
||||
assert scored["candidate_cluster_rankings"]
|
||||
@@ -0,0 +1,26 @@
|
||||
<!doctype html>
|
||||
<html lang="zh-CN">
|
||||
<meta charset="utf-8">
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1">
|
||||
<title>Jyotish Evidence Packet</title>
|
||||
<style>
|
||||
body{margin:0;background:#f5f7f8;color:#17212b;font:15px system-ui,-apple-system,"PingFang SC",sans-serif}
|
||||
main{max-width:960px;margin:0 auto;padding:28px 18px 56px}.bar{display:grid;grid-template-columns:1fr 1fr auto;gap:8px;margin:18px 0}
|
||||
input,button{font:inherit;padding:10px;border:1px solid #b6c1c6;border-radius:4px}button{background:#006b6b;color:white;border-color:#006b6b;cursor:pointer}
|
||||
section{background:white;border:1px solid #dce3e5;border-radius:6px;padding:18px;margin:14px 0}h1,h2{margin:0 0 10px}h1{font-size:24px}h2{font-size:17px}pre{white-space:pre-wrap;word-break:break-word;margin:0}.badge{display:inline-block;padding:3px 8px;border-radius:99px;background:#e1f2f0;color:#075d5a}
|
||||
@media(max-width:640px){.bar{grid-template-columns:1fr}main{padding:20px 12px}}
|
||||
</style>
|
||||
<main>
|
||||
<h1>Evidence Packet</h1>
|
||||
<p>仅显示已完成任务的可审计计算状态、证据包和技法审计。不会展示内部提示词或原始出生输入。</p>
|
||||
<div class="bar"><input id="url" placeholder="/api/evidence_packet/high_rigor_workflow/{job_id}"><input id="token" placeholder="access token"><button id="load">加载</button></div>
|
||||
<section><h2>运行状态</h2><div id="status">等待加载</div></section>
|
||||
<section><h2>Technique Audit</h2><pre id="audit">-</pre></section>
|
||||
<section><h2>Machine Evidence Packet</h2><pre id="packet">-</pre></section>
|
||||
<section><h2>Warnings</h2><pre id="warnings">-</pre></section>
|
||||
</main>
|
||||
<script>
|
||||
const show=(id,value)=>document.getElementById(id).textContent=JSON.stringify(value,null,2);
|
||||
document.getElementById('load').onclick=async()=>{const url=document.getElementById('url').value.trim(),token=document.getElementById('token').value.trim();if(!url||!token)return alert('需要 job URL 与 access token');const r=await fetch(url,{headers:{Authorization:`Bearer ${token}`}});const d=await r.json();if(!r.ok){show('status',d);return}const s=d.execution_status||{};document.getElementById('status').innerHTML=`<span class="badge">${s.official_evidence_status||'unknown'}</span><pre>${JSON.stringify(s,null,2)}</pre>`;show('audit',d.technique_audit||[]);show('packet',d.machine_evidence_packet||{});show('warnings',d.warnings||[])};
|
||||
</script>
|
||||
</html>
|
||||
@@ -0,0 +1,13 @@
|
||||
<!doctype html>
|
||||
<html lang="zh-CN"><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1">
|
||||
<title>主动问询式生时校正</title>
|
||||
<style>body{margin:0;background:#f5f7f8;color:#17212b;font:15px system-ui,-apple-system,"PingFang SC",sans-serif}main{max-width:860px;margin:auto;padding:28px 16px}form,.question,#result{background:#fff;border:1px solid #dce3e5;border-radius:6px;padding:16px;margin:12px 0}input,button{padding:9px;font:inherit;border:1px solid #b6c1c6;border-radius:4px}button{background:#006b6b;color:#fff;border-color:#006b6b}.grid{display:grid;grid-template-columns:repeat(3,1fr);gap:8px}.question label{display:block;padding:5px 0}pre{white-space:pre-wrap;word-break:break-word}@media(max-width:600px){.grid{grid-template-columns:1fr}}</style>
|
||||
<main><h1>主动问询式生时校正</h1><p>先扫描候选时间，再回答选择题。结果只缩小候选簇，不宣称已经精确到分钟。</p>
|
||||
<form id="birth"><div class="grid"><input name="year" placeholder="出生年" required><input name="month" placeholder="月" required><input name="day" placeholder="日" required><input name="hour" placeholder="时" required><input name="minute" placeholder="分" required><input name="time_uncertainty_minutes" value="30" placeholder="误差分钟"></div><p><button>生成第一轮问题</button></p></form><div id="questions"></div><div id="result"></div></main>
|
||||
<script>
|
||||
let questionnaire;
|
||||
const asObject=f=>Object.fromEntries(new FormData(f).entries());
|
||||
document.querySelector('#birth').onsubmit=async e=>{e.preventDefault();const p=asObject(e.target);for(const k of Object.keys(p))p[k]=Number(p[k]);const r=await fetch('/api/rectification/questionnaire',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify(p)});questionnaire=await r.json();render(questionnaire.questions||[])};
|
||||
function render(qs){const root=document.querySelector('#questions');root.innerHTML=qs.map(q=>`<section class="question"><strong>${q.prompt}</strong>${q.options.map(o=>`<label><input type="radio" name="${q.id}" value="${o.key}"> ${o.key}. ${o.label}</label>`).join('')}</section>`).join('')+'<button id="score">提交本轮答案</button>';document.querySelector('#score').onclick=score}
|
||||
async function score(){const answers={};document.querySelectorAll('#questions input:checked').forEach(e=>answers[e.name]=e.value);const r=await fetch('/api/rectification/answers',{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify({questionnaire,answers})});const d=await r.json();document.querySelector('#result').innerHTML=`<h2>候选簇排序</h2><pre>${JSON.stringify({candidate_cluster_rankings:d.candidate_cluster_rankings,next_round:d.next_round,boundary:d.boundary},null,2)}</pre>`}
|
||||
</script></html>
|
||||
Reference in New Issue
Block a user