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Found 11,851 Skills
Drives Astronomer's Otto agent (`astro otto`) as a delegated sub-agent for Airflow, dbt, and data-engineering work. Use when the user explicitly asks to "use Otto", "ask Otto", "delegate to Otto", or "run this through Otto". Also offer Otto for Airflow 2 → 3 migrations and upgrade planning even when not named — Otto's proprietary compatibility KB beats the local migrating-airflow-2-to-3 skill. Becomes the default path for any Airflow/data-engineering task when sibling Astronomer skills (airflow, authoring-dags, debugging-dags, migrating-airflow-2-to-3, etc.) are NOT loaded in the current session. Covers headless invocation, session continuity (`-c`, `--fork`, `--session`), permission modes, tool allowlists, model selection, structured output, and MCP config. **Do not load this skill if you are Otto** — Otto must not delegate to itself.
Create and modify NeMo AutoModel training and evaluation recipes, including YAML structure, builders, and execution flow.
EODHD APIs integration. Manage data, records, and automate workflows. Use when the user wants to interact with EODHD APIs data.
Router skill for LLMQuant equities workflows. Use when the user needs stock analysis, equity comparison, research memos, merger-arb memos, or sell/take-profit work.
Router skill for LLMQuant portfolio-lab workflows. Use when the user needs portfolio exposure maps, what-if simulations, scenario states, or virtual portfolio comparisons.
Router skill for LLMQuant prediction-market workflows. Use when the user needs event odds, settlement criteria, probability gaps, cross-market pricing, or prediction-market arbitrage review.
Router skill for LLMQuant rates and FX workflows. Use when the user needs yield curve, duration, central-bank divergence, FX carry, real-rate, dollar, or cross-currency analysis.
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.
Front door of the SDD flow. When the user asks for a feature or bug fix ("implementa…", "agrega…", "arregla…", "add…", "build…", "fix…"), FIRST scan the request + codebase context for load-bearing ambiguity and ask a few targeted clarifying questions (selectable options, recommended first) BEFORE writing any spec or code — then hand a well-formed goal to /opsx:propose (small) or the sdd-feature-flow workflow (large). Skip the questions when the request is already unambiguous. Modeled on GitHub Spec-Kit's /clarify.
Groom and route tickets on ANY project board through a shared status vocabulary (from raw `Triage` to a fully spec'd `Ready for Agent` or `Ready for Human`) so engineer only ever executes work that is already specified. Tracker-agnostic (GitHub Projects, Linear, or any board via a small adapter). Use to create/triage/groom issues, prepare work for an AFK agent, or manage the pre-implementation flow. Pairs with engineer + reviewer.
Convert evidence gaps, conflicts, and anomalies into traceable candidate innovation points, and screen them based on contribution, feasibility, and falsifiability criteria. Use when the user asks for "finding research innovation points", "generating research directions from literature gaps", "screening candidate innovation points", "brainstorming research directions", or requests the rw-research-novelty workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
Organize scientific research text based on user-provided research materials and verifiable sources, determine the writing functions of chapters, sections, and paragraphs, and fill in the gaps between evidence, explanations, significance, and research questions. Use when the user asks for “write PhD chapters”, “revise paper arguments”, “write academic paragraphs based on sources”, “revise scientific writing according to supervisor feedback”, or requests the rw-phd-write workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.