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Pp Airflow Admin

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mvanhorn
pp-airflow-admin

Printing Press CLI for Airflow Admin. Focused read-first API surface for inspecting Apache Airflow DAGs, DAG runs, task instances, pools, variables

Overview

Publishermvanhorn
Repositoryprinting-press-library
Skill namepp-airflow-admin
Stars
2K
Forks
614
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by mvanhorn on GitHub. Read the source before you install it.

Installation

Install the Pp Airflow Admin AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/mvanhorn/printing-press-library.git /tmp/printing-press-library
mkdir -p .claude/skills
cp -r /tmp/printing-press-library/cli-skills/pp-airflow-admin .claude/skills/pp-airflow-admin
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pp Airflow Admin in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pp Airflow Admin on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pp Airflow Admin is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Airflow Admin — Printing Press CLI

Prerequisites: Install the CLI

This skill drives the airflow-admin-pp-cli binary. You must verify the CLI is installed before invoking any command from this skill. If it is missing, install it first:

  1. Install via the Printing Press installer:
    bash
    npx -y @mvanhorn/printing-press-library install airflow-admin --cli-only
  2. Verify: airflow-admin-pp-cli --version
  3. Ensure $GOPATH/bin (or $HOME/go/bin) is on $PATH.

If the npx install fails (no Node, offline, etc.), fall back to a direct Go install (requires Go 1.26.6 or newer):

bash
go install github.com/mvanhorn/printing-press-library/library/developer-tools/airflow-admin/cmd/airflow-admin-pp-cli@latest

If --version reports "command not found" after install, the install step did not put the binary on $PATH. Do not proceed with skill commands until verification succeeds.

Apache Airflow is a workflow orchestrator used by data teams to schedule and monitor data pipelines. Data engineers usually model each pipeline as a DAG, then watch DAG runs, task instances, retries, pools, and scheduler health to understand whether daily loads, API extracts, dbt jobs, reports, and other data workflows are running correctly.

Use this skill when a user needs an operational readout from Airflow: list DAGs, inspect failed DAG runs, check task instance status, review pools, verify API health, or sync/search Airflow metadata locally. The CLI is read-first and intended for investigation, triage, and reporting. Avoid implying that it deploys DAGs, edits schedules, clears tasks, or mutates Airflow state.

Common Airflow Workflows

Local Airflow auth

For a local Airflow webserver at http://localhost:8080, get a token and store it:

bash
airflow-admin-pp-cli apache-airflow-admin-auth --username airflow --password airflow --json
airflow-admin-pp-cli auth set-token <access_token>
airflow-admin-pp-cli doctor --json

For a remote Airflow environment, set the base URL first:

bash
export AIRFLOW_ADMIN_BASE_URL="https://airflow.example.com"
export AIRFLOW_ADMIN_BEARER_AUTH="<access_token>"

Never ask the user to paste Airflow credentials into a repository, issue, PR, or shared log.

Pipeline health triage

Start broad, then drill down:

bash
airflow-admin-pp-cli monitor --agent
airflow-admin-pp-cli dags list --agent --select dag_id,is_active,is_paused,last_parsed_time
airflow-admin-pp-cli dags dag-runs list <dag_id> --state failed --agent --select dag_id,dag_run_id,state,start_date,end_date
airflow-admin-pp-cli dags dag-runs list-task-instances <dag_id> <dag_run_id> --state failed --agent --select task_id,state,try_number,start_date,end_date

Local search and analysis

When a user wants repeated investigation without paging the Airflow API each time:

bash
airflow-admin-pp-cli sync --resources dags,pools --agent
airflow-admin-pp-cli search "failed" --data-source local --agent --limit 20
airflow-admin-pp-cli analytics count --type dag-runs --agent

Command Reference

apache-airflow-admin-auth — Manage apache airflow admin auth

  • airflow-admin-pp-cli apache-airflow-admin-auth — Authenticate with an Airflow username and password and return a JWT access token.

apache-airflow-admin-version — Manage apache airflow admin version

  • airflow-admin-pp-cli apache-airflow-admin-version — Get Airflow version

connections — Airflow connection metadata.

  • airflow-admin-pp-cli connections — List connections

dags — DAG inventory and metadata.

  • airflow-admin-pp-cli dags get — Get DAG
  • airflow-admin-pp-cli dags list — List DAGs
  • airflow-admin-pp-cli dags dag-runs list — List DAG runs
  • airflow-admin-pp-cli dags dag-runs get — Get DAG run
  • airflow-admin-pp-cli dags dag-runs list-task-instances — List task instances
  • airflow-admin-pp-cli dags dag-runs get-task-instance — Get task instance
  • airflow-admin-pp-cli dags tasks list — List DAG tasks
  • airflow-admin-pp-cli dags details — Get detailed DAG metadata

monitor — Manage monitor

  • airflow-admin-pp-cli monitor — Get health

pools — Airflow pool capacity and occupancy.

  • airflow-admin-pp-cli pools get — Get pool
  • airflow-admin-pp-cli pools list — List pools

variables — Airflow variable metadata.

  • airflow-admin-pp-cli variables — List variables

Finding the right command

When you know what you want to do but not which command does it, ask the CLI directly:

bash
airflow-admin-pp-cli which "<capability in your own words>"

which resolves a natural-language capability query to the best matching command from this CLI's curated feature index. Exit code 0 means at least one match; exit code 2 means no confident match — fall back to --help or use a narrower query.

Auth Setup

Airflow JWT tokens can be created through the API token endpoint when the Airflow environment allows username/password auth:

bash
airflow-admin-pp-cli apache-airflow-admin-auth --username <username> --password <password> --json

Store the returned access_token:

bash
airflow-admin-pp-cli auth set-token YOUR_TOKEN_HERE

Or set AIRFLOW_ADMIN_BEARER_AUTH as an environment variable. Set AIRFLOW_ADMIN_BASE_URL when the Airflow webserver is not http://localhost:8080.

Run airflow-admin-pp-cli doctor to verify setup.

Agent Mode

Add --agent to any command. Expands to: --json --compact --no-input --no-color --yes.

  • Pipeable — JSON on stdout, errors on stderr

  • Filterable--select keeps a subset of fields. Dotted paths descend into nested structures; arrays traverse element-wise. Critical for keeping context small on verbose APIs:

    bash
    airflow-admin-pp-cli apache-airflow-admin-version --agent --select id,name,status
  • Previewable--dry-run shows the request without sending

  • Offline-friendly — sync/search commands can use the local SQLite store when available

  • Non-interactive — never prompts, every input is a flag

  • Explicit retries — use --idempotent only when an already-existing create should count as success

Response envelope

Commands that read from the local store or the API wrap output in a provenance envelope:

json
{
  "meta": {"source": "live" | "local", "synced_at": "...", "reason": "..."},
  "results": <data>
}

Parse .results for data and .meta.source to know whether it's live or local. A human-readable N results (live) summary is printed to stderr only when stdout is a terminal AND no machine-format flag (--json, --csv, --compact, --quiet, --plain, --select) is set — piped/agent consumers and explicit-format runs get pure JSON on stdout.

Agent Feedback

When you (or the agent) notice something off about this CLI, record it:

airflow-admin-pp-cli feedback "the --since flag is inclusive but docs say exclusive"
airflow-admin-pp-cli feedback --stdin < notes.txt
airflow-admin-pp-cli feedback list --json --limit 10

Entries are stored locally at ~/.local/share/airflow-admin-pp-cli/feedback.jsonl. They are never POSTed unless AIRFLOW_ADMIN_FEEDBACK_ENDPOINT is set AND either --send is passed or AIRFLOW_ADMIN_FEEDBACK_AUTO_SEND=true. Default behavior is local-only.

Write what surprised you, not a bug report. Short, specific, one line: that is the part that compounds.

Output Delivery

Every command accepts --deliver <sink>. The output goes to the named sink in addition to (or instead of) stdout, so agents can route command results without hand-piping. Three sinks are supported:

SinkEffect
stdoutDefault; write to stdout only
file:<path>Atomically write output to <path> (tmp + rename)
webhook:<url>POST the output body to the URL (application/json or application/x-ndjson when --compact)

Unknown schemes are refused with a structured error naming the supported set. Webhook failures return non-zero and log the URL + HTTP status on stderr.

Named Profiles

A profile is a saved set of flag values, reused across invocations. Use it when a scheduled agent calls the same command every run with the same configuration - HeyGen's "Beacon" pattern.

airflow-admin-pp-cli profile save briefing --json
airflow-admin-pp-cli --profile briefing apache-airflow-admin-version
airflow-admin-pp-cli profile list --json
airflow-admin-pp-cli profile show briefing
airflow-admin-pp-cli profile delete briefing --yes

Explicit flags always win over profile values; profile values win over defaults. agent-context lists all available profiles under available_profiles so introspecting agents discover them at runtime.

Exit Codes

CodeMeaning
0Success
2Usage error (wrong arguments)
3Resource not found
4Authentication required
5API error (upstream issue)
7Rate limited (wait and retry)
10Config error

Argument Parsing

Parse $ARGUMENTS:

  1. Empty, help, or --help → show airflow-admin-pp-cli --help output
  2. Starts with install → ends with mcp → MCP installation; otherwise → see Prerequisites above
  3. Anything else → Direct Use (execute as CLI command with --agent)

MCP Server Installation

  1. Install the MCP server:
    bash
    go install github.com/mvanhorn/printing-press-library/library/developer-tools/airflow-admin/cmd/airflow-admin-pp-mcp@latest
  2. Register with Claude Code:
    bash
    claude mcp add airflow-admin-pp-mcp -- airflow-admin-pp-mcp
  3. Verify: claude mcp list

Direct Use

  1. Check if installed: which airflow-admin-pp-cli If not found, offer to install (see Prerequisites at the top of this skill).
  2. Match the user query to the best command from the Unique Capabilities and Command Reference above.
  3. Execute with the --agent flag:
    bash
    airflow-admin-pp-cli <command> [subcommand] [args] --agent
  4. If ambiguous, drill into subcommand help: airflow-admin-pp-cli <command> --help.

Frequently asked questions

What does the Pp Airflow Admin AI skill do?

Printing Press CLI for Airflow Admin. Focused read-first API surface for inspecting Apache Airflow DAGs, DAG runs, task instances, pools, variables

Why use Pp Airflow Admin on TypingMind?

Because you install it once and use it with any model. Pp Airflow Admin is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pp Airflow Admin in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvanhorn/printing-press-library/tree/main/cli-skills/pp-airflow-admin. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pp Airflow Admin?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pp Airflow Admin?

As many as you like. As long as a model supports skills, you can use Pp Airflow Admin with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pp Airflow Admin AI skill free?

Yes. It is published on GitHub by mvanhorn under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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