Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, components, data tools or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept. --- ## Core Dagster Concepts Brief definitions only (see reference files for detailed examples): - **Asset**: Persistent object (table, file, model) produced by your pipeline - **Component**: Reusable building block that generates definitions (assets, schedules, sensors, jobs, etc.) relevant to a particular domain. ## Integration Workflow When integrating with ANY external tool or service, read the [Integration libraries index](./references/integrations/INDEX.md). This contains information about which integration libraries exist, and references on how to create new custom integrations for tools that do not have a published library. ## dg CLI The `dg` CLI is the recommended way to programmatically interact with Dagster (adding definitions, launching runs, exploring project structure, etc.). It is installed as part of the `dagster-dg-cli` package. If a relevant CLI command for a given task exists, always attempt to use it. ONLY explore the existing project structure if it is strictly necessary to accomplish the user's goal. In many cases, existing CLI tools will have sufficient understanding of the project structure, meaning listing and reading existing files is wasteful and unnecessary. Almost all `dg` commands that return information have a `--json` flag that can be used to get the information in a machine-readable format. This should be preferred over the default table output unless you are directly showing the information to the user. ## UV
KQL language expertise for writing correct, efficient Kusto queries using the Fabric RTI MCP tools. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL, Kusto, ADX, Azure Data Explorer, Fabric Eventhouse, log analysis, data exploration, time series, anomaly detection, summarize, where clause, join, extend, project, let statement, parse operator, extract function, any mention of pipe-forward query syntax.
- 📁 .github/
- 📁 references/
- 📄 LICENSE
- 📄 README.md
- 📄 SKILL.md
Develop, debug, and manage Temporal applications across Python, TypeScript, Go, Java and .NET. Use when the user is building workflows, activities, or workers with a Temporal SDK, debugging issues like non-determinism errors, stuck workflows, or activity retries, using Temporal CLI, Temporal Server, or Temporal Cloud, or working with durable execution concepts like signals, queries, heartbeats, versioning, continue-as-new, child workflows, or saga patterns.
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Review code for best practices, bugs, and security issues.
Perform a structured code quality review of a single package in this monorepo.
Internal skill. Use cc10x-router for all development tasks.
Use when adding or changing any Approval prompt/presenter.
Build Stellar blockchain applications in Swift using stellar-ios-mac-sdk. Use when generating Swift code for transaction building, signing, Horizon API queries, Soroban RPC, smart contract deployment and invocation, XDR encoding/decoding, and SEP protocol integration. Covers 26+ operations, 50 Horizon endpoints, 12 RPC methods, and 17 SEP implementations with Swift async/await and callback-based streaming patterns. Full Swift 6 strict concurrency support (all types Sendable).
- 📄 __init__.py
- 📄 adrecs_skill.py
- 📄 example.py
Query the ADReCS (Adverse Drug Reaction Classification System) v3.3 database. Use whenever the user asks about adverse drug reactions, drug safety profiles, ADR classification, ADR severity/frequency, or wants to look up any entity (drug name, BADD Drug ID, DrugBank ID, ATC code, CAS RN, PubChem CID, KEGG ID, ADR term, ADReCS ID, MedDRA code, MeSH ID) in ADReCS. --- # ADReCS Query Skill Search ADReCS v3.3 records by any entity. Auto-detects type by prefix: | Input Pattern | Detected As | Example | |---|---|---| | `BADD_D00142` | BADD Drug ID | exact on drug_id column | | `DB00945` | DrugBank ID | resolved via Drug_information | | `A02BC01` | ATC code | resolved via Drug_information | | `50-78-2` | CAS RN | resolved via Drug_information | | `CID2244` or bare digits | PubChem CID | resolved via Drug_information | | `D00109` (5-digit) | KEGG ID | resolved via Drug_information | | `08.06.02.001` | ADReCS ID | substring on ADReCS_ID column | | `10003781` (8-digit) | MedDRA code | resolved via ADR_ontology | | `D######` (6+ digit) | MeSH ID | resolved via ADR_ontology | | anything else | free text | substring on drug_name OR ADR_term | ## API | Function | Input | Returns | |---|---|---| | `load_drug_adr(path)` | txt path | DataFrame (Drug–ADR pairs) | | `load_drug_info(path)` | xlsx path | DataFrame (drug metadata) | | `load_adr_ontology(path)` | xlsx path | DataFrame (ADR hierarchy) | | `search(entity)` | single entity string | DataFrame of matching Drug–ADR rows | | `search_batch(entities)` | list of entity strings | dict[str, DataFrame] | | `summarize(hits, entity)` | DataFrame + label | compact LLM-readable text | | `to_json(hits)` | DataFrame | list[dict] | ## Usage See `if __name__ == "__main__"` block in `62_ADReCS.py` for runnable examples covering: drug name lookup, BADD Drug ID, DrugBank ID, ADR term, ADReCS ID prefix, batch search, and JSON output. ## Data - **Source**: ADReCS v3.3 — [https://www.bio-add.org/ADReCS/](https://www.bio-add.org/ADReCS/) - **Primary
Search and retrieve scientific papers from ArXiv
- 📄 e2e-setup
- 📄 e2e-teardown
- 📄 SKILL.md
Use when you need to interactively test a nori-skillsets CLI subcommand end-to-end via tmux, with full filesystem isolation
