“regex”的搜索结果

帮助设计表单、发票和题集的信息提取流程，先用正则表达式提取并清理文本，再评估置信度，仅将不确定的条目交给LLM检查和修正，同时介绍测试与指标记录。

• 提取题干、选项和答案
• 设计表单或发票提取流程
• 选择正则表达式或LLM

根据评分标准反复改进提示词、正则表达式、SQL和短代码。由AI生成候选方案，利用失败示例继续修正，并通过日志和快照查看结果。

• 根据参考答案改进提示词
• 按测试通过率调整正则表达式
• 通过测试改进SQL或代码

使用Fluentd的regexp插件解析MySQL慢查询日志，将其切分为时间、用户、查询时间和SQL内容四个字段，并配置字段长度限制以避免解析错误。

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Detects PII in unstructured data including emails, documents, images, and logs using NER-based detection with spaCy and Microsoft Presidio, regex patterns, OCR integration, and confidence scoring. Keywords: PII detection, unstructured data, NER, spaCy, Presidio, OCR, regex, email scanning, document scanning.

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Fix JavaScript regex failures when matching CJK (Chinese/Japanese/Korean) text using \b word boundaries. Use when: (1) Regex pattern with \b silently fails to match Japanese, Chinese, or Korean text, (2) Pattern works for Latin/ASCII text but not CJK, (3) hasDriverLicenceCue or similar text-detection function returns false for CJK input despite correct characters, (4) inferIssuerFromTitleText or pattern-matching functions fail on non-Latin scripts. Root cause: JavaScript \b only recognizes [a-zA-Z0-9_] as "word characters" — CJK characters are classified as \W (non-word), so \b before/after CJK always sees a non-word/non-word boundary and fails to match.

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Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

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Decision framework for choosing between regex and LLM when parsing structured text — start with regex, add LLM only for low-confidence edge cases.

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使用Fluentd的regexp插件解析MySQL慢查询日志，将其切分为时间、用户、查询时间和SQL内容四个字段，并配置字段长度限制以避免解析错误。

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Helps create, explain, debug, and optimize regular expressions for finding, extracting, or replacing text. Accounts for differences between programming languages and checks matching and nonmatching examples to identify overly broad patterns and potential performance problems.

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• Create patterns for extracting text
• Explain unfamiliar regular expressions
• Fix unwanted matches and missed matches

Hunting skill for sqli vulnerabilities. Built from 12 public bug bounty reports including modern NoSQL injection (Rocket.Chat CVE-2021-22911 MongoDB $regex, Mongoose ORM CVE-2024-53900 $where bypass), modern ORM raw-fragment SQLi (Django CVE-2024-42005, Sequelize GHSA-wrh9-cjv3-2hpw), second-order SOQL injection (HackerOne Salesforce), time-based blind SQLi in GraphQL resolvers, and SQLi on OIDC-proxy backends. Use when hunting SQLi on any target. Dedicated NoSQL operator injection (MongoDB/CouchDB $where/$regex/$ne) is owned by hunt-nosqli — NoSQL appears here only as adjacent ORM/WAF context.

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在解析结构化文本时，用于在正则表达式（Regex）和大型语言模型（LLM）之间进行选择的决策框架——优先使用正则表达式，仅针对低置信度的边界情况引入 LLM。

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Debug regex patterns with visual breakdowns, plain English explanations, test case generation, and flavor conversion. Use when user needs help with regular expressions or pattern matching.

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In-process semantic search over text files or in-memory strings, using Gemini embeddings via the CF AI Gateway. Use when user wants fuzzy/conceptual search where exact-keyword grep would miss — "sessions discussing regulatory constraints", "code about retry logic", "notes mentioning burnout even if the word isn't there". Complements searching-codebases (regex/AST) and extracting-keywords (YAKE). Do NOT use when an exact string/regex match is what's wanted — grep/rg wins on speed and precision there.

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Living catalogue of 7 known hazard entries (SE-01 through SE-07) specific to agent-studio: Windows backslash paths, prototype pollution, hook exit codes, async swallowing, ReDoS in glob-to-regex, DST arithmetic, array mutation during iteration. Each entry: symptom, root cause, fix, test assertion.

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Configurable regex-based alert system for detecting patterns in tool calls and session activity. Supports error triggers, content regex matching, token threshold triggers, and pattern detection with configurable actions.

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copy_tuner（CopyTuner / copy_tuner_client）で集中管理している i18n データを、prefix（正規表現）単位で Rails 標準の config/locales（YAML）管理へ移すスキル。gem の local_first_key_regexp を使うので、 gem を残したまま特定 prefix だけをローカル管理にできる（部分ローカル化）。1 回の実行で 1 prefix。 全 prefix を移して gem ごと撤去したい場合は繰り返し、完了後 copy-tuner-to-locales-cleanup スキルへ進む。 対象 prefix はスキル引数で指定でき、未指定なら export を俯瞰して選定する。

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TJS2 (吉里吉里Z 内蔵スクリプト言語) の言語仕様と組み込みクラスのリファレンス。.tjs ファイル / *.ks (KAG) 内の埋め込みスクリプト / TJS2 コード断片 を扱う、書く、レビューする、デバッグするときに使う。JavaScript / TypeScript に似ているが文法と意味論が違うので、JS の感覚で書くと壊れる場合が多い。組み込みクラス (Array / Dictionary / Date / Math / RegExp / Exception) の API もここに集約。**呼び出されたら必ず「JS との主な違い」セクションを最初に確認し、その上で必要な詳細リファレンスを Read で取りに行くこと。** 吉里吉里Z 本体のクラス API (Window / Layer / System / Storages / Bitmap 等) や engine 内部はこのスキルの対象外。

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Comprehensive regular expressions guide covering character classes, quantifiers, anchors, groups, lookahead/lookbehind, common patterns (email, URL, IP, phone, dates, semver), ReDoS prevention, Unicode support, flags, debugging, and when NOT to use regex.

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Hunting skill for sqli vulnerabilities. Built from 12 public bug bounty reports including modern NoSQL injection (Rocket.Chat CVE-2021-22911 MongoDB $regex, Mongoose ORM CVE-2024-53900 $where bypass), modern ORM raw-fragment SQLi (Django CVE-2024-42005, Sequelize GHSA-wrh9-cjv3-2hpw), second-order SOQL injection (HackerOne Salesforce), time-based blind SQLi in GraphQL resolvers, and SQLi on OIDC-proxy backends. Use when hunting SQLi on any target. Dedicated NoSQL operator injection (MongoDB/CouchDB $where/$regex/$ne) is owned by hunt-nosqli — NoSQL appears here only as adjacent ORM/WAF context.

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为孟加拉语搜索和AI处理整理字符表示统一、分词、数字转换及不破坏连字的显示方法，也可用于准备训练数据和存储文本。

• 实现孟加拉语搜索和输入
• 整理孟加拉语训练数据
• 支持孟加拉语与英语对话

创建 Hookify 规则，按条件检测命令执行、文件编辑、提示词输入和任务结束事件，对危险操作、敏感文件修改及调试代码发出警告或进行拦截。

• 拦截危险命令
• 对敏感文件编辑发出警告
• 检测编辑时新增的调试代码

使用JSON、正则表达式、Pydantic类型定义或固定选项约束LLM输出，将文字中提取的信息和分类结果整理为便于后续程序使用的格式。

• 从文字中提取企业和产品信息
• 按固定标签分类评论
• 按字段整理个人资料

Screen AI application inputs and historical logs for prompt injection, where malicious instructions try to redirect the model. Combine pattern matching, text structure scoring, and a DeBERTa classifier to flag suspicious content and produce JSON results.

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• Screening chatbot and AI agent inputs
• Checking external text used in retrieval-based AI applications
• Auditing historical interaction logs for injection attempts

Analyze Apache and Nginx access logs for attack attempts, scanner activity, and repeated login requests. Add geographic context to source IP addresses and produce a prioritized findings report to support intrusion investigations and monitoring reviews.

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• Investigating suspicious requests in web access logs
• Finding injection attempts and unauthorized file access attempts
• Identifying scanners and repeated login attempts