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 gcs-security-assessmentgit 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/gcs-security-assessment)<a href="https://agentmods.dev/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcs-security-assessment"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/data-agent-kit-starter-pack/gcs-security-assessment.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00181 | $0.01506 |
| Opus 5 | $0.00090 | $0.00753 |
| Sonnet 5 | $0.00036 | $0.00301 |
| Haiku 4.5 | $0.00018 | $0.00151 |
Grade A, and why
gcs-security-assessment 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 4d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- gcs-security-assessment — 97% identical, 38 lines differ
- gcs-security-assessment — 89% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Security Posture Assessment Skill
You are a Google Cloud Storage security assessment agent trained on Google's Secure AI Framework (SAIF). Your job is to evaluate GCS bucket and project configurations, identify toxic combinations of vulnerabilities, and provide actionable remediation.
[!IMPORTANT]
You are NOT a generic security chatbot. You MUST ground every finding in telemetry signals you have actually gathered. NEVER hallucinate findings or assume configurations you have not verified. If you cannot gather a signal, say so explicitly and skip that check.
[!CAUTION]
CRITICAL: Never execute destructive commands (e.g., rm, rb, IAM policy changes) without first printing the exact command and explicitly asking the user for a Y/N confirmation.
Philosophy
Traditional security tools generate isolated alerts from static rules (e.g., "bucket is public"). You correlate multiple signals to detect toxic combinations — scenarios where individually low-risk configurations combine to create critical exposures. A public bucket storing marketing PDFs is very different from a public bucket storing ML training data with no CMEK, no VPC-SC, and no audit logging.
Phase Summary Table
| Phase | Inputs | Outputs | Reference |
|---|---|---|---|
| 1. Discover Scope & Telemetry | User input (Project ID/Buckets/Datasets) | Scope confirmation, Telemetry signals | references/phases/discover.md |
| 2. Bucket Classification | Telemetry signals | Bucket classifications | references/phases/classification.md |
| 3. Baseline Security Eval | Telemetry signals, Classifications | Baseline failures | references/phases/baseline.md |
| 4. Toxic Combo Analysis | Telemetry signals, Classifications | Toxic combination findings | references/phases/toxic_analysis.md |
| 5. Output | Findings from all phases | Formatted assessment report | references/phases/output.md |
What ships with it
18 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.
- examples/sample_assessment.md 6.9 KB
- references/baseline_security.md 4.2 KB
- references/bucket_classification.md 4.6 KB
- references/phases/baseline.md 810 B
- references/phases/classification.md 727 B
- references/phases/discover.md 8.3 KB
- references/phases/output.md 12 KB
- references/phases/toxic_analysis.md 1.8 KB
- references/saif_risk_factors.md 3.7 KB
- references/telemetry_signals.md 4.5 KB
- references/toxic_combinations.md 16 KB
- scripts/cloud_rest_helpers_nodeps.py 21 KB runs code
- scripts/evaluate_project_security_posture.py 18 KB runs code
- scripts/fetch_bucket_telemetry.py 7.1 KB runs code
- scripts/fetch_object_telemetry.py 5.7 KB runs code
- scripts/list_datasets.py 7.2 KB runs code
- scripts/preflight_permissions.py 12 KB runs code
- scripts/validation.py 3.0 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.
- 4d ago Changed · +10 lines · +96 tokens per session ef9c484bfb4b
- 7d ago First seen · 94 lines · 85 tokens per session scan A 4f196c01ef84
gcs-security-assessment is a skill published in the GitHub repository gemini-cli-extensions/data-agent-kit-starter-pack (179 stars, last pushed today), licensed Apache-2.0. It adds 181 tokens to every session and 1,506 once invoked, about $0.0009 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.
Other skills, from other repositories
gke-compute-classes
Configures, optimizes, and troubleshoots GKE ComputeClasses. Use when configuring Spot VMs with on-demand fallback, targeting specific accelerators (GPUs/TPUs) or machine families, restricting ComputeClass access, or debugging pending pods related to node pool auto-creation. Do not use for cluster-level Node Auto…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
azure-mgmt-botservice-dotnet
Azure Resource Manager SDK for Bot Service in .NET. Management plane operations for creating and managing Azure Bot resources, channels (Teams, DirectLine, Slack), and connection settings. Triggers: "Bot Service", "BotResource", "Azure Bot", "DirectLine channel", "Teams channel", "bot management .NET", "create bot".
cloud-architect
Designs cloud architectures, creates migration plans, generates cost optimization recommendations, and produces disaster recovery strategies across AWS, Azure, and GCP. Use when designing cloud architectures, planning migrations, or optimizing multi-cloud deployments. Invoke for Well-Architected Framework, cost…