writing▌

Techniques for writing effective fuzzing harnesses across languages and frameworks. \n \n Covers harness fundamentals for C/C++, Rust, Go, and Python, including entry point signatures, input validation, and structured data extraction using FuzzedDataProvider and arbitrary crate \n Includes step-by-step guidance from identifying entry points through testing and iteration, with practical patterns for integer casting, complex inputs, and interleaved fuzzing of multiple operations \n Provides tool-s

Define patterns and messages that trigger Claude warnings or blocks on specific agent actions. \n \n Supports four event types (bash commands, file edits, agent stop, user prompts) plus an \"all\" catch-all, each with regex or multi-condition matching \n Rules use markdown files with YAML frontmatter stored in .claude/hookify.{name}.local.md ; changes take effect immediately on next tool use \n Two matching modes: simple pattern field for single-condition rules, or advanced conditions array for

Structured framework for writing blameless postmortems that drive organizational learning from incidents. \n \n Provides templates for standard postmortems, 5 Whys analysis, and quick incident reviews, with sections for timelines, root cause analysis, detection gaps, and action items \n Emphasizes blameless culture by shifting focus from individual blame to systemic failures and conditions that enabled incidents \n Includes facilitation guide for postmortem meetings, anti-patterns to avoid, and

Hookify rules are markdown files with YAML frontmatter that define patterns to watch for and messages to show when those patterns match. Rules are stored in .claude/hookify.{rule-name}.local.md files.

Generate Architectural Decision Records (ADRs) following the MADR template with systematic completeness checking.

Comprehensive guide to writing effective, blameless postmortems that drive organizational learning and prevent incident recurrence.

Write developer-focused technical blog posts via inference.sh CLI.

Write Milvus application-level Jupyter notebook examples as a DevRel workflow. Uses a Markdown-first approach — AI edits .md files, then converts to .ipynb via jupyter-switch.

Transform provided information into well-written academic assignments that match the user's natural writing style, avoiding obvious AI patterns while maintaining professional quality.

Write stories for React Native components using @storybook/react-native v10 and Component Story Format (CSF).