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Found 9,731 Skills
Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.
Grounding an assistant in your app with assistant-ui copilots (@assistant-ui/react). Use when steering assistant behavior with useAssistantInstructions, feeding lazy app-state context via useAssistantContext({ getContext }), exposing rendered components with makeAssistantVisible(Component, { clickable, editable }), building two-way interactable state with useAssistantInteractable and Interactables(), or registering instructions and tools imperatively through useAui().modelContext().register({ getModelContext }). Reach for this when the assistant should read the current page, click or edit UI, or read and update component state through auto-generated update_{name} tools. For LLM tools and tool-call UI use the tools skill; for runtime and thread state use the runtime skill.
Workload-aware architecture design for Apache Doris. MUST USE when designing data architectures, choosing between data models, planning ingestion strategies, sizing clusters, or translating business requirements into Apache Doris system designs. Complements doris-best-practices with decision frameworks and sizing-first workflow. Use when user describes a workload involving: IoT, sensor data, telemetry, real-time analytics, dashboard, log analysis, log search, CDC sync, time-series, device monitoring, point query service, ad-hoc analytics, lakehouse federation, ETL/ELT pipeline, report analytics, clickstream, user behavior, observability, metrics, fleet tracking, or any OLAP workload requiring table design from scratch. Also triggers on prompts like: "design a table for...", "how should I store...", "build an architecture for...", "we have X devices sending data every Y seconds", "recommend a cluster size for...", "what data model should I use for...", "we need to ingest X GB/day", "migrate from MySQL/PostgreSQL to Apache Doris". Also use for legacy analytics/search/serving stack consolidation prompts even when Apache Doris is not named explicitly, including replacing or migrating from Impala, Kudu, Elasticsearch/ES, Greenplum, Presto, HBase, Hive, Hadoop, Redis, or Lambda-style multi-engine data platforms.
Search 78 public scientific, biomedical, materials science, and economic databases via REST APIs. Covers physics/astronomy (NASA, NIST, SDSS, SIMBAD), earth/environment (USGS, NOAA, EPA), chemistry/drugs (PubChem, ChEMBL, DrugBank, FDA, KEGG, ZINC, BindingDB), materials (Materials Project, COD), biology/genomics (Reactome, UniProt, STRING, Ensembl, NCBI Gene, GEO, GTEx, PDB, AlphaFold, InterPro, BioGRID, Gene Ontology, dbSNP, gnomAD, ENCODE, Human Protein Atlas, Human Cell Atlas), disease/clinical (COSMIC, Open Targets, ClinicalTrials.gov, OMIM, ClinVar, GDC/TCGA, cBioPortal, DisGeNET, GWAS Catalog), regulatory (FDA, USPTO, SEC EDGAR), economics/finance (FRED, World Bank, US Treasury), demographics (US Census, Eurostat, WHO). Use when looking up compounds, genes, proteins, pathways, variants, clinical trials, patents, economic indicators, or any public database API query.
Generate videos from text prompts (and optional reference or frame images) using OpenRouter's asynchronous video generation API. Use when the user asks to create, generate, or make a video or animation from a description, animate an existing image, or turn a prompt into a short video clip.
Change ANYTHING inside a video — background, scene, lighting, outfit, weather, mood — from a free-form prompt, while keeping the EXACT original facial identity, motion, speech, audio AND closest supported output ratio. Edits the first frame with gpt-image-2, then propagates that look across the clip with Kling reference-video using the original clip as the identity anchor. Triggers: "change anything in my video", "edit my video with a prompt", "change the background of this video", "change my outfit in this clip", "restyle this video without changing the person", "put me on a beach", "make this video at night", "/fix-my-look".
Develop a Base44 app remotely inside Base44's cloud sandbox using your own agent — no local checkout and no deploy/push commands. The implementation is remote: writing a resource file into the sandbox is what ships it (backend functions, entities, and agents all auto-sync from the file you write), and OAuth connectors are set up against the remote app via MCP tools or the projectless `base44 connectors` CLI. This skill is the place for learning what you can author in the sandbox, how backend functions, entities, and agents are structured, and how to connect a connector without a local filesystem. Triggers on 'develop my Base44 app remotely', 'no local files', 'cloud sandbox', 'create an entity/agent remotely', 'connect a connector remotely', 'bring my own agent', or any work editing a Base44 app inside a sandbox.
Write, edit, refactor, or review Python in easy-cheese with concise stdlib-first code, Python 3.12, self-contained .pyz packaging, and repository test and validation conventions. Use for Python changes under src/, shared/scripts/, scripts/, .github/scripts/, or tests/, especially when the user asks for Pythonic, succinct, de-slopped, dataclass-based, CLI, validator, or bundled-helper code.
Triage and remediate vulnerabilities found by a Strix pentest (open-source CLI or app.strix.ai cloud), then re-run Strix to verify each fix. Use after a Strix scan reports findings, or when the user asks to fix security issues from a strix_runs report, vulnerabilities.json, findings.sarif, or a cloud scan's vulnerabilities.
Audit how agent context (CLAUDE.md / AGENTS.md / rules / skills) lines up with the code across a set of repositories and generate a self-contained HTML report — a short list of specific "things to check" (context behind the code, thin coverage for the codebase, oversized files, no per-area context), plus per-repo raw metrics and a folder tree comparing folder LOC to context coverage. Use when the user wants to audit context coverage across repos, "which repos are missing CLAUDE.md", "where is our agent context thin or stale", "context coverage across my org / projects folder", or "/context-coverage". Works on a local folder of clones or a whole GitHub org via the gh CLI.
Deploy compatible server, static-web, worker, scheduled-job, or reviewed remote-desktop workloads from GitHub or local source to Sealos Cloud, then run the default Runtime Truth Pass against the returned App URL, public route, authentication flow, logs, database state, and full resource footprint. Reject unsupported desktop, mobile, CLI, library, extension, hardware-dependent, mixed, and unidentified targets before readiness scoring or build. Use when the user asks to deploy a repository to Sealos or another cloud platform, or invokes "/sealos-deploy".
End-to-end Stellar development playbook. Covers Soroban smart contracts (Rust SDK), Stellar CLI, JavaScript/Python/Go SDKs for client apps, Stellar RPC (preferred) and Horizon API (legacy), Stellar Assets vs Soroban tokens (SAC bridge), wallet integration (Freighter, Stellar Wallets Kit), smart accounts with passkeys, status-sensitive zero-knowledge proof patterns, testing strategies, security patterns, and common pitfalls. Optimized for payments, asset tokenization, DeFi, privacy-aware applications, and financial applications. Use when building on Stellar, Soroban, or working with XLM, Stellar Assets, trustlines, anchors, SEPs, ZK proofs, or the Stellar RPC/Horizon APIs.