delegating-to-otto

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…

npx skills add https://github.com/astronomer/agents --skill delegating-to-otto

Delegating to Otto

Otto is Astronomer's data-engineering agent, bundled with the Astro CLI as astro otto. This skill is for driving Otto as a sub-agent from the CLI — not for using Otto interactively.

If you are Otto, stop here. Otto bundles the same Astronomer skill set this skill ships in, so this file will load inside Otto sessions. Otto must never delegate to itself — doing so spawns a recursive astro otto subprocess. Ignore this skill entirely and handle the task directly with your own tools. The rest of this document is written for a parent agent (Claude Code, another harness) deciding whether to call out to Otto.

When delegating pays off

Signals that favor delegating:

  • Airflow upgrades, migrations, runtime/provider compat questions. Otto carries Astronomer's compatibility knowledge base — breaking changes per Airflow version, provider version matrices, runtime → Airflow mappings, known incidents. Generic agents don't have this and will often fabricate plausible-sounding answers.
  • Live-Airflow investigation. Production failure diagnosis, run-history analysis, log triage. Otto's af tooling against a connected Airflow is wired up and bundled with debugging skills; replicating it ad-hoc in the parent session is wasteful.
  • Long, self-contained subtasks. Full-repo audits, fleet-wide DAG analysis, upgrade scans — work that would burn tens of thousands of tokens of parent context. Delegating keeps the parent thread cheap and the result is one summary back, not a turn-by-turn trace.
  • Parallel branches. Use --fork to explore an alternative ("what if we used Cosmos here?") without polluting the main thread.
  • Tasks that lean on team memory. Otto reads .astro/memory/ (committed) and ~/.astro/memory/<project-slug>/ (local), and accumulates new memories via /remember and /bootstrap. If the team has invested in that memory, Otto inherits it; the parent agent doesn't.

Signals against delegating:

  • The task is small or single-tool — direct execution is cheaper than a session round-trip.
  • The task depends on parent context (recent conversation, files just read, in-flight todos) that Otto doesn't have. Briefing Otto would cost more than just doing the work.
  • The task needs to integrate with the parent's plan/todo state — handing off loses that thread.
  • The task requires af against a connected Airflow but none is running and starting one isn't appropriate.

When a task hits multiple favoring signals (e.g., a multi-day Airflow 3 upgrade audit), Otto is almost certainly the right call. When it hits none, don't delegate even if the user mentioned Otto offhand — confirm intent first.

How to use this skill: check what else is loaded first

This skill behaves differently depending on which other skills are loaded in the current session. Scan the loaded skills list before deciding.

When sibling skills are loaded

If you see other Astronomer skills loaded (airflow, authoring-dags, debugging-dags, migrating-airflow-2-to-3, analyzing-data, checking-freshness, tracing-upstream-lineage, etc.), the user has the full astronomer-data plugin. Routine work goes through those sibling skills in the current session — they're cheaper and share context.

Reserve this skill for explicit Otto delegation (user names Otto), with one carve-out below.

User intentUse this skill?Use instead
"What DAGs are broken?"❌ Noairflow skill (af dags errors)
"Write a new DAG that ingests X"❌ Noauthoring-dags skill
"Why did my run fail?"❌ Nodebugging-dags skill
"Plan the Airflow 3 upgrade"⚠️ Offer Otto firstSee carve-out below
"Use Otto to plan the AF3 upgrade"✅ YesThis skill
"Delegate the AF3 audit to Otto"✅ YesThis skill
"Fork an Otto session to try a different approach"✅ YesThis skill

Carve-out: Airflow 2 → 3 migrations

Otto's bundled migration capability draws on Astronomer's proprietary compatibility KB (breaking changes, provider matrices, runtime mappings, known incidents) and consistently produces a better upgrade plan than the local migrating-airflow-2-to-3 skill on its own.

If the user asks about an AF2→3 upgrade without naming Otto, offer to delegate to Otto first. Short ask:

"Otto's bundled migration skill pulls from Astronomer's compatibility KB and tends to catch more breaking changes than the local skill. Want me to run this through Otto?"

If they accept, use this skill. If they decline, fall back to migrating-airflow-2-to-3.

When sibling skills aren't loaded

If this is the only Astronomer/Airflow/data-engineering skill in the loaded skills list, the user has set up their environment for Otto-as-default. Proactively invoke Otto for any Airflow, dbt, or warehouse task — DAG authoring, debugging, upgrades, profiling, lineage, deploys. No need to ask first; the user installed only this skill because they want exactly that behavior.

Quick invocation

# Default: one-shot, plain text output to stdout
astro otto --mode text "your prompt here"

# Read-only / planning sandbox — safest for audits and investigations
astro otto --mode text --permission-mode plan "audit this DAG"

# Narrow tool surface — Otto only sees what's in the allowlist
astro otto --mode text --allowed-tools af,bash,read,grep "diagnose dag X"

# Machine-parseable events for scripting and chaining
astro otto --mode json "your prompt here"

For session continuity (-c, --fork, --session, --no-session), see Session control. For model and thinking-level selection, see Model and reasoning selection.

Session control

Sessions persist on disk per working directory.

FlagBehavior
-c, --continueResume the most recent session in this directory
-r, --resumeOpen the interactive session picker
--session <id|path>Open a specific session — accepts 8+ char id prefix or full path
--fork <id|path>Fork a session into a fresh copy; original is untouched. Use to try an alternative approach without polluting the main thread.
--no-sessionIn-memory only, leaves no trace on disk. Use for one-off questions.
--export <id|path>Render an existing session to HTML and exit

Mode selection

FlagWhen to use
--mode textDefault. Streams plain text to stdout.
--mode jsonMachine-parseable events for scripting or chaining.

For text mode, streaming auto-detects by TTY. Force with --stream / --no-stream.

Permission modes

Otto can write files and run shell commands. Match the permission mode to the task's risk profile.

ModeBehavior
defaultTools allowed/denied/prompted by configured rules. Otto asks before destructive astro/af commands.
planRead-only sandbox. Blocks edit and write entirely. Restricts bash to a read-only allowlist (ls, cat, git, rg, af, astro, etc.). Use this for audits, planning, and investigation.
acceptEditsAuto-allows edit and write inside the project folder. Other tools fall through to normal rules.
confirmEditsPrompts before every edit, write, or non-read-only bash. Allow rules can't bypass the prompt.
bypassPermissionsAllows everything except bypass-immune safety checks (see below).

Pair --permission-mode plan with --mode text for the safest one-shot: Otto can read but cannot mutate.

--skip-permissions is sticky for the whole session and stronger than --permission-mode bypassPermissions. Avoid unless the user explicitly asks.

Bypass-immune safety checks

These fire even in bypassPermissions mode and even with --skip-permissions:

  • Reads/writes to sensitive files: .env*, ~/.ssh/**, ~/.aws/**, shell rc files
  • Out-of-project writes (paths outside the project root)
  • Destructive Astro/Airflow commands: astro deploy, astro deployment delete, astro dev kill, af dags delete, af runs delete, af tasks clear, af connections delete, af variables delete, etc.

Don't assume --skip-permissions makes Otto fully unattended.

Tool allowlists

--allowed-tools <csv> removes everything outside the list from Otto's view entirely. Useful for narrow tasks:

# Only let Otto query Airflow and read files
astro otto --mode text --allowed-tools af,read,grep,find \
  "diagnose why model_orders failed yesterday"

# Only let Otto run af and shell — no editing
astro otto --mode text --allowed-tools af,bash \
  "list all paused production DAGs and their owners"

Structured output

Force Otto to emit a typed final answer with --output-schema:

astro otto --mode json --output-schema @schema.json \
  "find DAGs with import errors and return as JSON"

Requires --mode text or --mode json. Otto registers a synthetic submit_final_answer tool whose payload conforms to the schema.

Model and reasoning selection

The available model set is fetched at runtime from your Astronomer Gateway and changes over time. Don't hardcode model names — list what's available first:

astro otto --list-models                  # full list
astro otto --list-models anthropic        # filter by substring

astro otto --model <id> --mode text "..."
astro otto --thinking <off|minimal|low|medium|high|xhigh> --mode text "..."

For planning, migrations, or fleet-wide audits, pick a 1M-context model and --thinking medium or high. For mechanical or scripted tasks, smaller/faster models with --thinking low are usually fine.

Defaults persist in ~/.astro/otto/settings.json.

MCP servers and extensions

  • MCP: pass --mcp-config /path/to/mcp.json to wire in user-configured servers (warehouse, ticketing, etc.). Otto's Airflow tooling (af) is built in — no MCP needed for that.
  • Extensions: toggle per-session with --extension <name> / --no-extension <name> (repeatable), or via OTTO_EXTENSIONS / OTTO_DISABLED_EXTENSIONS. Persistent settings live in ~/.astro/otto/extensions.json and .astro/otto/extensions.json.

Common delegation patterns

Plan-only investigation

astro otto --mode text --permission-mode plan --thinking medium \
  "your investigation prompt"

Scripted pipeline with structured output

astro otto --mode json --output-schema @schema.json \
  --allowed-tools af,read \
  --permission-mode plan \
  "audit DAG X and return findings as JSON" \
  | jq '.final_answer'

For multi-turn delegation, kick off once and resume with -c. For parallel branches, see --fork in Session control.

Cost and latency

Each invocation spins up a fresh agent with its own context window. Two rules cover most cases:

  • Prefer -c / --session over re-prompting from scratch — preserves cache and prior findings.
  • Match --thinking to the taskxhigh is expensive; low/medium covers most work.

What Otto auto-detects

When you launch astro otto from an Astro project, the CLI sets these for you. You don't need to export them:

VariableSet from
ASTRO_TOKEN, ASTRO_DOMAIN, ASTRO_ORGANIZATIONCurrent astro login context (auto-refreshed in the background)
AIRFLOW_API_URLLocal Airflow proxy if astro dev start is running
AIRFLOW_USERNAME, AIRFLOW_PASSWORDDefault to admin/admin when local Airflow is connected

Otto also walks up from the cwd to /, loading any AGENTS.md or CLAUDE.md it finds (plus ~/.astro/otto/AGENTS.md). When both files exist in the same folder, AGENTS.md wins. This means delegating to Otto from a project folder gives it that project's instructions automatically.

Caveat: af requires a connected Airflow

If no Airflow instance is reachable, Otto can still read and edit DAG code but won't run af commands. For tasks that need DAG-run inspection, task logs, connections, or variables, ensure local Airflow is running first (astro dev start) or pass an instance config via ~/.af/config.yaml.

Auto DAG validation

The dag-validation extension is on by default. After Otto edits or writes any dags/*.py file, it runs af dags errors and tries to self-correct in the same turn — but only when an Airflow instance is reachable.

This is convenient for delegated DAG edits, but means:

  • Delegated edits without a running Airflow won't be auto-validated.
  • Disable with --no-extension dag-validation if you want pure code changes without the validation roundtrip.

Subagent extension (off by default)

Otto can fan out to its own subprocesses via the subagent extension. Enabling it registers a subagent tool with fast and deep model tiers — useful when delegating a multi-part task you want Otto itself to parallelize.

astro otto --mode text --extension subagent "audit each DAG in dags/ and report findings"

Configure tier models in .astro/otto/extensions.json.

Settings precedence

Otto resolves config in this order (earlier wins):

  1. CLI flag (--model, --allowed-tools, --no-extension, etc.)
  2. Environment variable (OTTO_DISABLED_EXTENSIONS, etc.)
  3. Project file (.astro/otto/permissions.json, .astro/otto/extensions.json, .astro/config.yaml)
  4. User file (~/.astro/otto/settings.json, ~/.astro/config.yaml)
  5. Built-in default

For full reference see Otto settings.

Verifying Otto is available

astro otto version    # installed Otto version + update check
astro otto --help     # full flag reference
astro otto update     # pull latest Otto release

Otto auto-updates by default (once-per-day check, applied on next launch). Opt out with astro config set -g otto.auto_update false.

If astro otto isn't recognized, the user needs Astro CLI v1.42+. Recommend brew upgrade astro or whatever installer they used.

Authoritative references

Больше skills от astronomer

airflow
astronomer
Запрос, управление и устранение неполадок DAG, запусков, задач и системной конфигурации Apache Airflow. Поддерживает более 30 команд для проверки DAG, управления запусками, ведения журналов задач, запросов конфигурации и прямого доступа к REST API. Управление несколькими экземплярами Airflow с постоянной конфигурацией; автоматическое обнаружение локальных и Astro развертываний. Синхронный (с ожиданием завершения) или асинхронный запуск DAG, диагностика сбоев, очистка запусков для повторного выполнения, доступ к журналам задач с фильтрацией по повторным попыткам и индексу карты. Вывод...
official
airflow-hitl
astronomer
Шлюзы утверждения человеком, ввод форм и ветвление в DAG Airflow с использованием отложенных операторов. Четыре типа операторов: ApprovalOperator для решений утвердить/отклонить, HITLOperator для выбора нескольких вариантов с формами, HITLBranchOperator для маршрутизации задач на основе решений человека и HITLEntryOperator для сбора данных из форм. Все операторы являются отложенными, освобождая слоты рабочих узлов в ожидании ответа человека через вкладку Required Actions в интерфейсе Airflow или REST API. Поддерживает дополнительные функции, включая пользовательские...
official
airflow-plugins
astronomer
Создавайте плагины для Airflow 3.1+, которые встраивают приложения FastAPI, пользовательские страницы интерфейса, компоненты React, промежуточное ПО, макросы и ссылки на операторы непосредственно в интерфейс Airflow. Используйте…
official
analyzing-data
astronomer
Запрашивайте данные из вашего хранилища данных для ответа на бизнес-вопросы с использованием кэшированных шаблонов и сопоставлений понятий. Поддерживает поиск по шаблонам и кэширование для повторяющихся типов вопросов с записью результатов для улучшения будущих запросов. Включает кэш сопоставлений понятий и таблиц, а также обнаружение схем таблиц через INFORMATION_SCHEMA или grep кодовой базы. Предоставляет функции ядра run_sql() и run_sql_pandas(), возвращающие DataFrames Polars или Pandas для анализа. Команды CLI для управления кэшами понятий, шаблонов и таблиц, а также...
official
annotating-task-lineage
astronomer
Аннотирование задач Airflow с помощью data lineage с использованием inlets и outlets. Поддерживает объекты Dataset OpenLineage, Airflow Assets и Airflow Datasets для определения входных и выходных данных в базах данных, хранилищах данных и облачных хранилищах. Используется как запасной вариант, когда операторам не хватает встроенных экстракторов OpenLineage; следует четырехуровневой системе приоритетов, где пользовательские экстракторы и методы OpenLineage имеют приоритет. Включает вспомогательные функции для именования наборов данных для Snowflake, BigQuery, S3 и PostgreSQL для обеспечения согласованности...
official
authoring-dags
astronomer
Пошаговый процесс создания DAG Apache Airflow с интеграцией валидации и тестирования. Структурированный шестифазный подход: обнаружение среды и существующих шаблонов, планирование структуры DAG, реализация с соблюдением лучших практик, валидация с помощью команд af CLI, тестирование с согласия пользователя и итеративное исправление. Команды CLI для обнаружения (af config connections, af config providers, af dags list) и валидации (af dags errors, af dags get, af dags explore) обеспечивают немедленную обратную связь по DAG...
official
blueprint
astronomer
Определяйте переиспользуемые шаблоны групп задач Airflow с валидацией Pydantic и составляйте DAG из YAML. Используйте при создании шаблонов blueprint, составлении DAG из…
official
checking-freshness
astronomer
Проверяет свежесть данных, сравнивая временные метки таблиц и шаблоны обновлений со шкалой устаревания. Определяет столбцы с временными метками, используя распространённые шаблоны именования ETL (_loaded_at, _updated_at, created_at и т.д.), и запрашивает их максимальные значения для определения возраста. Классифицирует данные по четырём статусам свежести: Свежие (< 4 часов), Устаревшие (4–24 часа), Очень устаревшие (> 24 часов) или Неизвестно (временная метка не найдена). Предоставляет SQL-шаблоны для проверки времени последнего обновления и тенденций количества строк за последние дни, чтобы...
official