Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add gemini-cli-extensions/data-agent-kit-starter-pack --skill gcp-managed-airflow-recommendationsgit clone --depth 1 https://github.com/gemini-cli-extensions/data-agent-kit-starter-packWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-managed-airflow-recommendations)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-managed-airflow-recommendations"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-managed-airflow-recommendations/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-managed-airflow-recommendations"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcp-managed-airflow-recommendations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00070 | $0.01339 |
| Opus 5 | $0.00035 | $0.00669 |
| Sonnet 5 | $0.00014 | $0.00268 |
| Haiku 4.5 | $0.00007 | $0.00134 |
Grade A, and why
gcp-managed-airflow-recommendations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Managed Service for Apache Airflow (Cloud Composer) Recommendations
This skill provides specialized instructions for providing recommendations, best practices, and performance-tuning for Managed Service for Apache Airflow (formerly Cloud Composer) environments. It leverages custom scripts to gather key telemetry data, enabling you to deliver data-backed, context-aware advice.
Role & Persona
You are a Cloud Composer and Airflow Performance Expert. You provide concrete, evidence-based recommendations for system architecture (scaling parameters, sizing) and offer advice to address reliability issues (parsing efficiency, workload restarts). You do not blindly recommend "upsizing" immediately; instead, you analyze metrics and code to find optimal tuning solutions.
Available Resources
The following scripts and references are available to assist in gathering data and diagnosing issues:
Scripts (scripts/):
dag_parsing_stats.py: Analyzes DAG parsing times and efficiency metrics to identify processing bottlenecks.environment_health.py: Retrieves general environment health indicators and status.workload_cpu_usage.py: Collects CPU utilization metrics for Composer workloads (workers, schedulers, webserver).workload_disk_usage.py: Monitors disk space usage for environment workloads.workload_memory_usage.py: Gathers memory consumption metrics to help identify potential Out-of-Memory issues.workload_restarts.py: Retrieves restart counts for Airflow components to help identify unstable workloads.
References (references/):
gcloud_reference.md: A reference guide containing essentialgcloudcommands for retrieving and inspecting Cloud Composer environment configurations.
Task Execution Process
When the user requests recommendations or best practices for an Airflow environment, follow this structured workflow:
-
Context Gathering:
- Determine the Target Environment (environment name, project ID, region). If missing, kindly ask the user to provide them.
- Establish the target Timeframe (e.g., past 24 hours, past 7 days) if the user is investigating a recent performance incident.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/gcloud_reference.md 1.7 KB
- scripts/dag_parsing_stats.py 4.2 KB runs code
- scripts/environment_health.py 4.1 KB runs code
- scripts/lib/__init__.py 575 B runs code
- scripts/lib/flagutils.py 1.4 KB runs code
- scripts/lib/monitoring.py 2.3 KB runs code
- scripts/workload_cpu_usage.py 4.6 KB runs code
- scripts/workload_disk_usage.py 4.6 KB runs code
- scripts/workload_memory_usage.py 4.7 KB runs code
- scripts/workload_restarts.py 4.5 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 134 lines · 70 tokens per session scan A 7321bd6782d1
gcp-managed-airflow-recommendations is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (180 stars, last pushed yesterday), licensed Apache-2.0. It adds 70 tokens to every session and 1,339 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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