228 lines
6.8 KiB
Python
228 lines
6.8 KiB
Python
"""技能 slug 语义校验（verb-noun-platform 规范，见 development/NAMING.md）。"""
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from __future__ import annotations
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import os
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import re
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_KEBAB_SLUG = re.compile(r"^[a-z0-9]+(?:-[a-z0-9]+)*$")
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_MAX_SLUG_LENGTH = 48
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_MIN_SEGMENTS = 3
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_MAX_SEGMENTS = 5
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_MAX_NOUN_SEGMENTS = 3
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_SKILL_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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_NAMING_DIR = os.path.join(_SKILL_ROOT, "assets", "naming")
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_BUILTIN_VERBS = frozenset(
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{
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"scrape",
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"receive",
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"download",
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"export",
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"add",
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"fill",
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"enter",
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"bind",
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"withdraw",
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"reconcile",
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"verify",
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"submit",
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"query",
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"process",
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"reply",
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"generate",
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"draft",
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"review",
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"confirm",
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"track",
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"upload",
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"match",
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"triage",
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"ship",
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"reprice",
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}
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)
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_BUILTIN_PLATFORMS = frozenset(
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{
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"amazon",
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"shopee",
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"xinghang",
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"alibaba",
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"kingdee",
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"icbc",
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"pingpong",
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"worldfirst",
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"paypal",
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"lianlian",
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"single-window",
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"export-rebate",
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"etax",
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}
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)
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_BUILTIN_SCOPES = frozenset({"all", "batch", "multi", "default"})
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LEGACY_EXEMPT_SLUGS: frozenset[str] = frozenset(
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{
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"account-manager",
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"skill-template",
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"your-skill-slug",
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"your_skill_slug",
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}
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)
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def load_wordlist(filename: str) -> frozenset[str]:
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"""从 assets/naming/{filename} 加载词表；文件不存在时 fallback 内置集合。"""
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fallbacks = {
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"verbs.txt": _BUILTIN_VERBS,
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"platforms.txt": _BUILTIN_PLATFORMS,
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"scopes.txt": _BUILTIN_SCOPES,
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}
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fallback = fallbacks.get(filename, frozenset())
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path = os.path.join(_NAMING_DIR, filename)
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if not os.path.isfile(path):
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return fallback
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words: set[str] = set()
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with open(path, encoding="utf-8") as f:
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for line in f:
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word = line.strip()
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if word and not word.startswith("#"):
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words.add(word)
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return frozenset(words) if words else fallback
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def parse_slug_segments(slug: str) -> list[str]:
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"""按 '-' 分割；非法字符由上层 kebab 校验处理。"""
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slug = slug.strip()
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if not slug:
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return []
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return slug.split("-")
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def _match_platform_suffix(
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segments: list[str],
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platforms: frozenset[str],
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) -> tuple[str, int] | None:
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"""返回 (platform_name, platform_part_count) 或 None。"""
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for platform in sorted(platforms, key=lambda item: (-item.count("-"), item)):
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parts = platform.split("-")
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part_count = len(parts)
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if len(segments) < part_count + 2:
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continue
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if segments[-part_count:] == parts:
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return platform, part_count
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return None
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def classify_slug_form(segments: list[str], *, slug: str = "") -> str:
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"""
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返回: 'legacy_exempt' | 'standard' | 'scope' | 'invalid'
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- legacy_exempt: slug in LEGACY_EXEMPT_SLUGS
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- standard: 末段匹配 platforms, 首段 in verbs, 3<=len<=5
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- scope: 末段 in scopes, 首段 in verbs, 3<=len<=5
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- invalid: 其他
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"""
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if slug in LEGACY_EXEMPT_SLUGS:
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return "legacy_exempt"
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if not segments or not (_MIN_SEGMENTS <= len(segments) <= _MAX_SEGMENTS):
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return "invalid"
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verbs = load_wordlist("verbs.txt")
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platforms = load_wordlist("platforms.txt")
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scopes = load_wordlist("scopes.txt")
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if segments[0] not in verbs:
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return "invalid"
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if segments[-1] in scopes:
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return "scope"
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if _match_platform_suffix(segments, platforms) is not None:
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return "standard"
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return "invalid"
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def validate_slug_semantics(
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slug: str,
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*,
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strict: bool = True,
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) -> tuple[list[str], list[str]]:
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"""返回 (errors, warnings)。"""
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slug = slug.strip()
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if slug in LEGACY_EXEMPT_SLUGS:
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return [], []
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errors: list[str] = []
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warnings: list[str] = []
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if not _KEBAB_SLUG.fullmatch(slug):
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msg = f"slug 必须为 kebab-case（小写字母、数字、连字符）：{slug!r}"
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(errors if strict else warnings).append(msg)
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return errors, warnings
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if len(slug) > _MAX_SLUG_LENGTH:
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msg = f"slug 长度不得超过 {_MAX_SLUG_LENGTH} 字符（当前 {len(slug)}）：{slug!r}"
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(errors if strict else warnings).append(msg)
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segments = parse_slug_segments(slug)
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segment_count = len(segments)
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if segment_count < _MIN_SEGMENTS:
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msg = (
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f"slug 段数应为 {_MIN_SEGMENTS}～{_MAX_SEGMENTS}（verb-noun-platform），"
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f"当前为 {segment_count} 段：{slug!r}；新技能禁止 2 段无平台形态（如 reconcile-finance）"
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)
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(errors if strict else warnings).append(msg)
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return errors, warnings
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if segment_count > _MAX_SEGMENTS:
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msg = f"slug 段数不得超过 {_MAX_SEGMENTS}（当前 {segment_count}）：{slug!r}"
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(errors if strict else warnings).append(msg)
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verbs = load_wordlist("verbs.txt")
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platforms = load_wordlist("platforms.txt")
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scopes = load_wordlist("scopes.txt")
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if segments[0] in platforms and segments[0] not in verbs:
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msg = f"平台名不应放在 slug 最前：{slug!r}（应为 verb-noun-platform）"
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(errors if strict else warnings).append(msg)
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if segments[0] not in verbs:
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msg = f"slug 首段应为动词白名单中的词：{segments[0]!r}（slug={slug!r}）"
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(errors if strict else warnings).append(msg)
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form = classify_slug_form(segments, slug=slug)
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if form == "scope":
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if segments[-1] not in scopes:
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msg = f"slug 末段应为 scope 白名单中的词：{segments[-1]!r}（slug={slug!r}）"
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(errors if strict else warnings).append(msg)
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noun_count = segment_count - 2
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elif form == "standard":
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platform_match = _match_platform_suffix(segments, platforms)
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if platform_match is None:
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msg = f"slug 末段应匹配平台白名单：{slug!r}"
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(errors if strict else warnings).append(msg)
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noun_count = 0
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else:
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_platform_name, platform_part_count = platform_match
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noun_count = segment_count - 1 - platform_part_count
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else:
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if segment_count >= _MIN_SEGMENTS:
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if segments[-1] not in scopes:
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platform_match = _match_platform_suffix(segments, platforms)
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if platform_match is None:
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msg = f"slug 末段应匹配平台或 scope 白名单：{slug!r}"
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(errors if strict else warnings).append(msg)
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noun_count = 0
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if noun_count > _MAX_NOUN_SEGMENTS:
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warnings.append(
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f"slug 中间名词段建议不超过 {_MAX_NOUN_SEGMENTS} 个词（当前 {noun_count}）：{slug!r}"
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)
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return errors, warnings
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