Resume structure, achievement bullet formulas, ATS optimization, and job-targeted tailoring for software engineers. Use when reviewing resumes, crafting achievement bullets, extracting keywords from job descriptions, or tailoring content for specific roles.
Use when content must be translated between audiences with different expertise, context, or goals while preserving accuracy but adapting presentation. Invoke when technical content needs business framing (engineering decisions → executive summary), strategic vision needs tactical translation (board presentation → team OKRs), expert knowledge needs simplification (academic paper → blog post, medical diagnosis → patient explanation), formal content needs casual tone (annual report → social media post), long-form needs summarization (50-page doc → 1-page brief), internal content needs external framing (roadmap → public updates, bug tracking → known issues), cross-cultural adaptation (US idioms → international clarity, Gen Z → Boomer messaging), medium shifts (written report → presentation script, detailed spec → action checklist), or when user mentions "explain to", "reframe for", "translate this for [audience]", "make this more [accessible/formal/technical]", "adapt for [executives/engineers/customers]", "simpl
Create engaging X (Twitter) posts and threads that drive engagement and grow audience. Use this skill when asked to write X/Twitter content, create threads, craft viral tweets, or help with X engagement strategy. Triggers include requests for tweets, X posts, Twitter threads, or social media content for X.
Create author profiles via questionnaire or transcript analysis for consistent article voice
Use when writing or polishing professional scientific emails, journal cover letters, or responses to reviewers. Invoke when user mentions email to collaborator, cover letter to editor, reviewer response, professional correspondence, or needs help with professional tone, clear asks, or diplomatic communication in academic/scientific contexts.
Use when prompts produce inconsistent or unreliable outputs, need explicit structure and constraints, require safety guardrails or quality checks, involve multi-step reasoning that needs decomposition, need domain expertise encoding, or when user mentions improving prompts, prompt templates, structured prompts, prompt optimization, reliable AI outputs, or prompt patterns.
prose style guide. use when your output includes ANYTHING other than a code snippet. use during conversation, and every time you write to a file, commit, PR. enforces academish voice: supported claims, precise language, no hyperbole. user tone: lowercase, terse, anti-sycophancy.
Use when transforming analysis/data into persuasive narratives—presenting to executives, explaining technical concepts to non-technical audiences, creating customer-facing communications, writing investor updates, announcing changes, turning research into insights, or when user mentions "write this for", "explain to", "present findings", "make this compelling", "audience is". Invoke when information needs to become a story tailored to specific stakeholders.
Leverage AI tools effectively for professional writing tasks. Provides prompt patterns, refinement workflows, voice preservation techniques, and quality checkpoints for AI-augmented drafting.
Single source of truth and librarian for ALL OpenAI Codex CLI documentation. Manages local documentation storage, scraping, discovery, and resolution. Use when finding, locating, searching, or resolving Codex CLI documentation; discovering docs by keywords, category, tags, or natural language queries; scraping from llms.txt; managing index metadata (keywords, tags, aliases); or rebuilding index from filesystem. Run scripts to scrape, find, and resolve documentation. Handles doc_id resolution, keyword search, natural language queries, category/tag filtering, alias resolution, llms.txt parsing, markdown subsection extraction for internal use, hash-based drift detection, and comprehensive index maintenance.
Template and guide for restructuring large documentation files into token-efficient directory structures. Reduces context bloat by 40-60% while maintaining accessibility.
Use when the user has a document (PDF, markdown, book notes, research paper, methodology guide) containing theoretical knowledge or frameworks and wants to convert it into an actionable, reusable skill. Invoke when the user mentions "create a skill from this document", "turn this into a skill", "extract a skill from this file", or when analyzing documents with methodologies, frameworks, processes, or systematic approaches that could be made actionable for future use.
Automatically detect performance regressions in CI/CD pipelines by comparing metrics against baselines. Use when validating builds or analyzing performance trends. Trigger with phrases like "detect performance regression", "compare performance metrics", or "analyze performance degradation".
Split datasets into training, validation, and testing sets for ML model development. Use when requesting "split dataset", "train-test split", or "data partitioning".
Execute clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points".
Collect comprehensive infrastructure performance metrics across compute, storage, network, containers, load balancers, and databases. Use when monitoring system performance or troubleshooting infrastructure issues. Trigger with phrases like "collect infrastructure metrics", "monitor server performance", or "track system resources".
Process and validate datasets for training. Use when setting up data pipelines.
Use when designing database schemas, need to model domain entities and relationships clearly, building knowledge graphs or ontologies, creating API data models, defining system boundaries and invariants, migrating between data models, establishing taxonomies or hierarchies, user mentions "schema", "data model", "entities", "relationships", "ontology", "knowledge graph", or when scattered/inconsistent data structures need formalization.
DEPRECATED - Use domain-specific skills instead. Routes to bazinga-db-core, bazinga-db-workflow, bazinga-db-agents, or bazinga-db-context.
Production-grade PostgreSQL query optimization, indexing strategies, performance tuning, and modern features including pgvector for AI/ML workloads. Master EXPLAIN plans, query analysis, and database design for high-performance applications
