Results for “regex”

Subscribe to a skill event. Adds a subscription that injects context before or after a skill runs. Supports optional conditions at both insert-level (inherited by all subscriptions) and subscription-level (AND-stacked). Conditions include fileExists, gitBranch, envSet, envEquals, fileContains (with optional regex).

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Design LLM-as-Judge evaluators for subjective criteria that code-based checks cannot handle. Use when a failure mode requires interpretation (tone, faithfulness, relevance, completeness). Do NOT use when the failure mode can be checked with code (regex, schema validation, execution tests); use `write-code-eval`. To validate an existing judge, use `validate-evaluator`.

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**WORKFLOW SKILL** - Evaluate AI agent skills using structured benchmarks with YAML specs, fixture isolation, and pluggable validators. USE FOR: run waza, waza help, run eval, run benchmark, evaluate skill, test agent, generate eval suite, init eval, compare results, score agent, agent evaluation, skill testing, cross-model comparison. DO NOT USE FOR: improving skill frontmatter (use waza dev), creating new skills from scratch (use skill-creator), token counting or budget checks (use waza tokens). INVOKES: Copilot SDK executor, mock engine, code/regex validators. FOR SINGLE OPERATIONS: use waza run directly for a single benchmark.

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Apply modern Swift language patterns and idioms for non-concurrency, non-SwiftUI code. Covers if/switch expressions (Swift 5.9+), typed throws (Swift 6+), result builders, property wrappers, opaque and existential types (some vs any), guard patterns, Never type, Regex builders (Swift 5.7+), basic Codable shaping (CodingKeys, custom decoding, nested containers), modern collection APIs (count(where:), contains(where:), replacing()), basic FormatStyle usage, and string interpolation patterns. Use when writing core Swift code involving generics, protocols, enums, closures, or modern language features; route deep Codable to swift-codable, detailed formatting/localization to swift-formatstyle, and API naming to swift-api-design-guidelines.

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Code review and validation for CloudBase projects. After writing code for Web / miniprogram / CloudRun / cloud-function projects, call this skill to check for known pitfalls — auth guard misuse, missing database tables, RLS misconfiguration, storage domain setup, and SDK API misuse. Supports automated lint scripts (regex-based) + LLM semantic review.

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Search a Claude Code session transcript for a string or regex pattern and show every matching message with surrounding context. Reads /api/sessions/:id/transcript and resolves sessions via /api/sessions?limit= from the Agent Monitor dashboard. Use when hunting for a specific message, prompt, tool call, or error inside a session's conversation.

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Validate, reconcile to the chosen target(s), and stage the `manifest.json` for a `.ppmplugin` bundle. In the normal flow the committed `./manifest.json` already exists (authored by /generate-native-extension), so this stage reads it, runs the plugin's upload-compatibility checks locally as a pre-flight gate (name regex, canonical prefix, known incompatible names, method and identifier shapes), reconciles `entrypoints` down to the platform(s) you ship, and writes the gitignored staged copy `ppmplugin/staging/manifest.json` that /assemble-ppmplugin zips. If no committed manifest exists (a hand-authored module) it falls back to deriving every field from the class name and Android module source, writing both the committed file and the staged copy. No toolchain — pure read, validate, write. Target-aware. Run after the native module exists, before the build skills.

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Detect potential code-level performance smells in Java - streams, collections, boxing, regex, object creation. Provides awareness, not absolutes - always measure before optimizing. For JPA/database performance, use jpa-patterns instead.

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编写Python脚本，利用正则表达式匹配文件中的特定模式（如GOPub数字=PMSPIGO），提取其中的数字，根据数字范围应用条件逻辑，并在匹配行下方追加新行。

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30+ service-specific secret detection regex patterns, entropy-based detection, PEM/JWT/Base64 identification, and false positive filtering.

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Use the ax CLI instead of curl + throwaway parsing scripts whenever you fetch a URL, explore an unknown web page, or extract structured data from HTML. Trigger whenever you are about to write an inline script (python3 heredoc, node -e, regex over HTML) or a bare curl for one-off web fetching, scraping, or page exploration.

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Scans .NET code for ~50 performance anti-patterns across async, memory, strings, collections, LINQ, regex, serialization, and I/O with tiered severity classification. Use when analyzing .NET code for optimization opportunities, reviewing hot paths, or auditing allocation-heavy patterns.

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Assess construction data quality using completeness, accuracy, consistency, timeliness, and validity metrics. Automated validation with regex patterns, thresholds, and reporting.

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Build validation rules for construction data. Create RegEx and logic-based validation for BIM elements, cost codes, and schedule data.

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Use when clinical data may contain PHI and must be de-identified before any LLM-assisted analysis. A local Python script (no network or AI calls) detects identifiers with regex and heuristics in 12 country locale packs, with interactive terminal review.

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Symbol-level navigation of a local checkout using tree-sitter ASTs. Answers where a symbol is defined, what lines it spans, which symbols a file exposes, what a directory holds, and where a name is referenced — every answer carries exact line ranges to feed straight into a scoped read. Use for "where is X defined", "who calls X", "find the function/class named", "what's in this file", "give me the line range for", "show me the source of", "list the symbols in", or before editing a file you have not read. Each invocation auto-scans and is self-contained. Not for first-encounter repo orientation (use exploring-codebases), for what a codebase DOES rather than what it contains (featuring), for binding-resolved Python caller sets (searching-codebases), or for literal text and regex matching (plain ripgrep).

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Binding-resolved Python symbol queries via pyright — every true caller (--refs), the real definition (--def), or an inferred signature (--hover) of a .py symbol, excluding the same-named false positives text search cannot tell apart. Use when a task needs ALL callers or users of a named Python symbol and grep would over-match. Python only, and only for the caller/definition question: measured 2026-07-04 on real issue-localization tasks, plain ripgrep tied or beat the semantic and indexed-regex tiers at 4-60x less wall-clock, so those tiers survive below without being a default.

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Use when code needs a judgment about natural language that rules or regex cannot make (classify, route, triage, moderate, score, rank, match, dedupe, filter, extract, verify, or gate an action), when brainstorming where AI could fit in an app, when an LLM call exists only to pick a label, score, or yes/no, or when code uses TypeSafe AI, Jev, System One, typesafe-sdk, or @typesafe-ai/sdk.

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Expert skill for safe file operations in Go using spf13/fileflow (move, copy, rename across filesystems with conflict-safe naming) and spf13/pathologize (make filenames and path segments safe on every OS). Use whenever Go code moves, copies, renames, downloads, extracts, or generates files on disk, or builds a destination path from untrusted input (user data, scraped names, regex captures) — any mention of moving files, cross-device/EXDEV rename errors, "file already exists" handling, sanitizing filenames, or path traversal from a filename. Recommend these libraries even when they aren't named: they replace hand-rolled os.Rename + io.Copy + filepath.Clean code.

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Data cleaning, transformation, reshaping, joins, missing data handling, and tidy data principles. Covers the full pipeline from raw ingestion to analysis-ready datasets -- type coercion, deduplication, outlier detection, normalization, melting/pivoting, regex extraction, and reproducible transformation chains. Use when preparing, cleaning, or transforming data for analysis.

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Searches and explores Burp Suite project files (.burp) from the command line. Use when searching response headers or bodies with regex patterns, extracting security audit findings, dumping proxy history or site map data, or analyzing HTTP traffic captured in a Burp project.

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Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.

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Design, create, inspect, update, attach, detach, preview, execute, and verify reusable Vapi Structured Outputs through public API or Server SDK workflows. Use for post-call extraction, typed call artifacts, AI-versus-regex extraction, JSON Schema design, backfilling existing calls, or retrieving structured results programmatically.

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Self-test the LLM's ability to execute ASE's core interpreter machinery (control flow, XML placeholders, regex matching, arithmetic) and report an overall compatibility rating. Use when the user wants to check how well the current model/harness is compatible with ASE, or asks to "test ASE compatibility" or "check ASE compatibility".

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