Results for “sanitizers”

Coverage-guided fuzzing for Ruby code and C extensions using libFuzzer and sanitizers.

Use only when writing/updating/fixing C++ tests, configuring GoogleTest/CTest, diagnosing failing or flaky tests, or adding coverage/sanitizers.

Detect memory errors like buffer overflows and use-after-free bugs in C/C++ code during fuzzing and testing using AddressSanitizer.

Comprehensive MDX content sanitizer that escapes angle brackets, generics, and other JSX-conflicting patterns to prevent build failures

Fuzz Python code and C extensions with coverage guidance and AddressSanitizer support using a libFuzzer-based fuzzer.

Fuzz Rust projects with libFuzzer using cargo-fuzz, including harness writing, sanitizer integration, and coverage analysis.

Use only when writing/updating/fixing C++ tests, configuring GoogleTest/CTest, diagnosing failing or flaky tests, or adding coverage/sanitizers.

Batch signal isolation for Phenocycler spleen channels using matched blank pairs and kintsugi.signal

strip-unicode - Unicode-to-ASCII Sanitizer

Write cleaning standard operating procedures. TRIGGERS - Use when user needs help with cleaning-sop related tasks.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Guides setting up and using Atheris for coverage-guided fuzzing of Python code and C extensions, including Docker setup, harness writing, and corpus management.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Fuzz C/C++ projects with multi-core support using AFL++, a fork of AFL with better performance and advanced features.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Use when the user wants to humanize text, make writing sound less like AI, or rewrite content to feel more natural. Triggers on "humanize", "make it sound natural", "too robotic", "sounds like AI", or similar. Works in Hebrew and English. Always output only the rewritten text — no explanations.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Synthesize source-indexed proof-material items into the final proof-usage playbook. Use when Codex needs to cluster proof items by proof shape, de-duplicate across sources, design reusable taxonomy entries, update proof-usage indexes and source maps, or preserve material-ID links from cooked proof entries.

Use when content is verbose, repetitive, or padded and needs tightening while preserving meaning

Reviews parsers, deserialization, uploads, archives, YAML/JSON/XML/pickle, paths, templates, SSRF, and unsafe loaders.

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Apply medicinal chemistry filters for compound triage: drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and a custom query language for library filtering.

Scans a codebase for Martin Fowler's catalog of code smells, reporting each with location, severity, and refactoring recommendations, and ranks files to build a prioritized refactoring queue.

Anti-distillation defense for employee Skills. Clean your skill files to look complete but with core proprietary knowledge neutralized. Use when user wants to protect trade secrets, sanitize forced knowledge transfers, or create safe-to-submit skill documents.

Filtros de química medicinal. Aplique regras de similaridade a fármacos (Lipinski, Veber), filtros PAINS, alertas estruturais, métricas de complexidade, para priorização de compostos e filtragem de bibliotecas.

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Pragmatic coding standards - concise, direct, no over-engineering, no unnecessary comments

Humanizer