Automatització
Automatització de tasques i creació de scripts
godot-composition-apps
thedivergentai
"Expert architectural standards for building scalable Godot applications (Apps, Tools, UI, or Games) using the Composition pattern. Use when designing node structures, refactoring monolithic scripts, or implementing complex behaviors. Enforces \"Has-A\" relationships over \"Is-A\" inheritance."
anthropic-architect
jamesrochabrun
Determine the best Anthropic architecture for your project by analyzing requirements and recommending the optimal combination of Skills, Agents, Prompts, and SDK primitives.
yao-deepseek-crawler
yaojingang
"Use when a user provides DeepSeek web AI-search keywords, repeat count, target entity, and entity type, then needs repeated fresh-window crawls aggregated into JSON plus a Kami HTML GEO report. Not for generic website crawling, DeepSeek API chat, SEO writing, or one-off answer generation."
yao-chatgpt-crawler
yaojingang
"Use when a user provides ChatGPT web AI-search keywords, repeat count, target entity, entity type, OpenCLI profile, and crawl interval preference, then needs repeated crawls aggregated into JSON plus a Kami HTML GEO report. Not for generic crawling, ChatGPT API chat, SEO writing, or one-off answers."
scrub-docs-code-parity
marin-community
Run the docs/code-parity scrub only from its scheduler or an explicit request for that scrub.
manage-hero-run
marin-community
Launch, resume, monitor, hand off, or seal an explicitly requested production-critical Marin run, normally at least 1e22 FLOPs.
scrub-reflection-self-improvement
marin-community
Run the repository self-improvement scrub only from its scheduler or an explicit request for that scrub.
babysit-zephyr
marin-community
Launch or continuously monitor a specified Zephyr pipeline on Iris only when asked to babysit it; restart only after explicit approval.
run-research
marin-community
Run a coordinated multi-session research program only when explicitly requested; maintain a logbook and durable snapshots.
add-pallas-kernel
marin-community
Add or change a named Pallas/Mosaic kernel, including its reference implementation, correctness tests, wrapper, or requested tuning.
file-issue
marin-community
File a GitHub issue only when explicitly requested or delegated by another selected workflow; do not create issues merely because a problem was found.
debug
marin-community
Diagnose a stated code, JAX, Marin, Iris, Zephyr, or TPU fault or startup/performance regression; do not activate for ordinary implementation or optimization without a symptom.
ra-video-wash-pipeline
Pluviobyte
End-to-end Chinese video washing pipeline. Use when the user provides a Bilibili, Douyin, Xiaohongshu, YouTube, web video URL, or local video file and asks for 视频洗稿, 视频二创, 洗稿并制作视频, 链接视频改写, 提取逐字稿后洗稿, or producing a new AI video from a source video. Orchestrates ra-逐字稿提取skill and ra-洗稿, ending at the 01-内容生产/视频工作台/待制作/ handoff queue; production continues through ra-video-production-director only on explicit request (直接制作/直接出片/一条龙).
use-iris
marin-community
Use Iris to submit, inspect, debug, monitor, or recover jobs and tasks; diagnose scheduling and federation; deploy controllers; or reserve dev GPUs and TPUs. Use for ordinary Iris operations, including babysitting a job, controller rollouts, accelerator sessions, and stuck CoreWeave pods. Use a narrower production-run or Zephyr skill when that workflow is explicitly requested.
fluidsim
K-Dense-AI
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
lamindb
K-Dense-AI
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
cellxgene-census
K-Dense-AI
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
diffdock
K-Dense-AI
Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
deepchem
K-Dense-AI
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
bioservices
K-Dense-AI
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with consistent API. Best for cross-database analysis, ID mapping across services. For quick single-database lookups use gget; for sequence/file manipulation use biopython.
latchbio-integration
K-Dense-AI
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
get-available-resources
K-Dense-AI
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
wilma-triage
aikarjal
Daily triage of Wilma school notifications for Finnish parents. Fetches exams, messages, news, schedules, and homework — filters for actionable items, syncs exams to Google Calendar, and reports via chat. Requires the wilma skill and gog CLI (or gog skill from ClawHub) for calendar access.
nature-literature-pipeline
Yuan1z0825
Complete automated literature discovery pipeline: multi-source search → six-dimension scoring → fine reading → formatted delivery → archival. Combines a configurable engine with daily cron-driven application layer. Works with Feishu, Telegram, or any messaging platform.