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documents

31

large-scale-text-editing

Guidance for transforming large text files (thousands to millions of rows) using text editors like Vim. This skill should be used when the task involves bulk text transformations, CSV manipulation, column reordering, regex-based find-and-replace operations, or when there are keystroke/efficiency constraints. Applies to tasks requiring macro-based editing, batch substitutions, or complex text processing where understanding the transformation pattern from input/output samples is needed.

letta-ai

content-media

documents

31

extract-moves-from-video

Guidance for extracting text-based game commands, moves, or inputs from video recordings using OCR and frame analysis. This skill applies when extracting user inputs from screen recordings of text-based games (Zork, interactive fiction), terminal sessions, or any video where typed commands need to be recovered. It covers OCR preprocessing, region-of-interest extraction, domain-aware validation, and deduplication strategies.

letta-ai

content-media

productivity-toolsmarketplace

31

slim-executive-summary

Generate comprehensive executive summaries from workspace communication files using AI analysis with automatic context discovery

NASA-AMMOS

tools

documents

31

cobol-modernization

This skill provides guidance for translating COBOL programs to modern languages (Python, Java, etc.) while preserving exact behavior. It should be used when tasks involve COBOL-to-modern-language migration, legacy code translation, fixed-width file format handling, or ensuring byte-level compatibility between source and target implementations.

letta-ai

content-media

academic

31

mteb-retrieve

Guidance for text embedding retrieval tasks using sentence transformers or similar embedding models. This skill should be used when the task involves loading documents, encoding text with embedding models, computing similarity scores (cosine similarity), and retrieving/ranking documents based on semantic similarity to a query. Applies to MTEB benchmark tasks, document retrieval, semantic search, and text similarity ranking.

letta-ai

research

media

31

video-processing

Guide for video analysis and frame-level event detection tasks using OpenCV and similar libraries. This skill should be used when detecting events in videos (jumps, movements, gestures), extracting frames, analyzing motion patterns, or implementing computer vision algorithms on video data. It provides verification strategies and helps avoid common pitfalls in video processing workflows.

letta-ai

content-media

media

31

video-processing

This skill provides guidance for video analysis and processing tasks using computer vision techniques. It should be used when analyzing video frames, detecting motion or events, tracking objects, extracting temporal data (e.g., identifying specific frames like takeoff/landing moments), or performing frame-by-frame processing with OpenCV or similar libraries.

letta-ai

content-media

media

31

reshard-c4-data

Guide for implementing reversible data resharding systems with hierarchical constraints (max files/folders per directory, max file size). Use when building compress/decompress scripts that reorganize datasets while maintaining full reconstruction capability.

letta-ai

content-media

data-analysis

31

count-dataset-tokens

This skill provides guidance for counting tokens in datasets using specific tokenizers. It should be used when tasks involve tokenizing dataset content, filtering data by domain or category, and aggregating token counts. Common triggers include requests to count tokens in HuggingFace datasets, filter datasets by specific fields, or use particular tokenizers (e.g., Qwen, DeepSeek, GPT).

letta-ai

data-ai

data-analysis

31

log-summary-date-ranges

Guidance for analyzing log files and generating summary reports with counts aggregated across multiple date ranges and severity levels. This skill applies when tasks involve parsing log files by date, counting occurrences by severity (ERROR, WARNING, INFO), and outputting structured CSV summaries across time periods like "today", "last 7 days", or "last 30 days".

letta-ai

data-ai

sql-databases

31

query-optimize

Guidance for SQL query optimization tasks. This skill should be used when optimizing slow SQL queries, improving database performance, or rewriting queries to be more efficient. Covers query plan analysis, benchmarking strategies, and database-specific optimization techniques.

letta-ai

databases

sql-databases

31

query-optimize

This skill provides guidance for SQL query optimization tasks, including rewriting slow queries for better performance while preserving semantic equivalence. Use this skill when asked to optimize, improve performance of, or rewrite SQL queries, particularly when dealing with correlated subqueries, complex joins, or queries that need CTEs/window functions.

letta-ai

databases

data-engineering

31

multi-source-data-merger

This skill provides guidance for merging data from multiple heterogeneous sources (CSV, JSON, Parquet, XML, etc.) into unified output formats with conflict detection and resolution. Use when tasks involve combining data from different file formats, field mapping between schemas, priority-based conflict resolution, or generating merged datasets with conflict reports.

letta-ai

data-ai

data-engineering

31

apache-airflow-orchestration

Complete guide for Apache Airflow orchestration including DAGs, operators, sensors, XComs, task dependencies, dynamic workflows, and production deployment

manutej

data-ai

data-engineering

31

sparql-university

Guidance for writing SPARQL queries against RDF/Turtle datasets, particularly for university or academic data. This skill should be used when tasks involve querying RDF data with SPARQL, filtering entities based on multiple criteria, aggregating results, or working with Turtle (.ttl) files.

letta-ai

data-ai

data-engineering

31

sparql-university

Guidance for writing and verifying SPARQL queries against RDF datasets, particularly university/academic ontologies. This skill should be used when tasks involve querying RDF data with SPARQL, working with academic datasets (students, professors, departments, courses), or performing complex graph pattern matching with filters and aggregations.

letta-ai

data-ai

data-engineering

31

multi-source-data-merger

This skill provides guidance for merging data from multiple heterogeneous sources (JSON, CSV, Parquet, XML, etc.) into a unified dataset. Use this skill when tasks involve combining records from different file formats, applying field mappings, resolving conflicts based on priority rules, or generating merged outputs with conflict reports. Applicable to ETL pipelines, data consolidation, and record deduplication scenarios.

letta-ai

data-ai

data-engineering

31

reshard-c4-data

Guidance for data resharding tasks that involve reorganizing files across directory structures with constraints on file sizes and directory contents. This skill applies when redistributing datasets, splitting large files, or reorganizing data into shards while maintaining constraints like maximum files per directory or maximum file sizes. Use when tasks involve resharding, data partitioning, or directory-constrained file reorganization.

letta-ai

data-ai

sql-databases

31

dbt-testing

dbt testing strategies using dbt_constraints for database-level enforcement, generic tests, and singular tests. Use this skill when implementing data quality checks, adding primary/foreign key constraints, creating custom tests, or establishing comprehensive testing frameworks across bronze/silver/gold layers.

sfc-gh-dflippo

databases

framework-internals

31

caffe-cifar-10

Guidance for building Caffe from source and training CIFAR-10 models. This skill applies when tasks involve compiling Caffe deep learning framework, configuring Makefile.config, preparing CIFAR-10 dataset, or training CNN models with Caffe solvers. Use for legacy ML framework installation, LMDB dataset preparation, and CPU-only deep learning training tasks.

letta-ai

development

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