productivity▌

Automated secrets scanner that detects hardcoded API keys, tokens, passwords, and sensitive data in codebases. \n \n Scans repositories for leaked credentials and generates findings with severity levels and remediation guidance \n Uses the poltergeist binary to identify secret candidates, then analyzes and confirms each one through parallel subagents \n Stores scan results and findings in a repo-specific cache directory under ~/.ghost/repos/ with git-based versioning \n Orchestrates multi-step w

Read, analyze, and transform Excel spreadsheets with pandas, pivot tables, charts, and data cleaning. \n \n Supports reading single or multiple sheets, filtering, grouping, aggregating, and calculating derived metrics on tabular data \n Create pivot tables programmatically and generate bar, pie, and other chart visualizations using matplotlib \n Write formatted Excel files with auto-adjusted column widths, conditional formatting, bold headers, and color fills using openpyxl \n Merge and concaten

Research and compile information about companies and contacts for effective sales outreach.

Skill by ara.so — Daily 2026 Skills collection.

Deploy PostgreSQL, Redis, MySQL, or MongoDB with pre-configured volumes, networking, and connection variables. \n \n Supports four official database templates: PostgreSQL, Redis, MySQL, and MongoDB, each with persistent volumes and automatic connection variable setup \n Always checks for existing databases before creating to avoid duplicates; queries environment config via GraphQL to detect running instances \n Provides reference variables for connecting other services (e.g., ${{Postgres.DATABAS

Generate a commit message and commit staged changes using git.

Automated analysis of 200+ scientific data formats with format-specific insights and quality assessment. \n \n Supports six major scientific domains: chemistry, bioinformatics, microscopy, spectroscopy, proteomics, and metabolomics, with 60+ formats per category \n Auto-detects file type and generates detailed markdown reports including metadata extraction, statistical summaries, data quality metrics, and visualization recommendations \n Includes reference documentation for each format covering

Multi-layer memory architecture for agents: short-term context, long-term vector storage, and retrieval optimization. \n \n Covers seven memory types: short-term (context window), long-term (vector stores), working, episodic, semantic, and procedural memory, each suited to different information patterns \n Provides three core patterns: memory type selection, vector store choice, and chunking strategy to maximize retrieval accuracy \n Highlights critical retrieval challenges: contextual chunking,

Distributed tracing, service maps, and performance analysis.

Expert guidance for fine-tuning LLMs with parameter-efficient methods and production optimization. \n \n Covers LoRA, QLoRA, and full fine-tuning workflows with Hugging Face PEFT, including dataset validation, hyperparameter configuration, and adapter merging for deployment \n Provides a complete minimal working example with LoRA setup, training loop, and quantization variants for memory-constrained environments \n Includes five-stage workflow: dataset preparation, method selection, training wit