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 ibm-self-serve-assets/building-blocks --skill confluent-iac-terraformgit clone --depth 1 https://github.com/ibm-self-serve-assets/building-blocksWrote 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/ibm-self-serve-assets/building-blocks/confluent-iac-terraform)<a href="https://agentmods.dev/skills/ibm-self-serve-assets/building-blocks/confluent-iac-terraform"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/confluent-iac-terraform/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/ibm-self-serve-assets/building-blocks/confluent-iac-terraform"><img src="https://agentmods.dev/badge/skills/ibm-self-serve-assets/building-blocks/confluent-iac-terraform.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 428 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 487 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 664 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 700 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 877 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 490 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00063 | $0.07213 |
| Opus 5 | $0.00032 | $0.03606 |
| Sonnet 5 | $0.00013 | $0.01443 |
| Haiku 4.5 | $0.00006 | $0.00721 |
Grade A, and why
confluent-iac-terraform 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 12d 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
1 near-identical copy found in the catalogue:
- data-streaming-confluent-terraform — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 914 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Confluent Cloud Streaming System Builder
Purpose
This skill defines the process for analyzing streaming use case requirements and generating complete, production-ready streaming systems on Confluent Cloud that include:
- Terraform Infrastructure-as-Code for Confluent Cloud resources
- Apache Flink SQL for real-time stream processing
- Python producers with proper schema serialization
- Comprehensive documentation and testing approaches
Objective
Transform natural language streaming requirements into deployable streaming systems that:
- Can be deployed to Confluent Cloud with minimal configuration
- Follow Infrastructure-as-Code best practices
- Implement correct Flink SQL patterns for stream processing
- Include production-ready error handling and monitoring
- Provide clear documentation for reproduction
- Adapt to any streaming domain while maintaining technical correctness
Documentation Principles
IMPORTANT: Generate specifications based on user requirements and streaming best practices.
Rules:
- Analyze the user's domain and infer appropriate streaming patterns
- Design schemas that match the business entities and events
- Select aggregation patterns based on use case requirements
- Generate complete, working code without placeholders
- Include all critical technical requirements (versions, formats, RBAC)
- Provide realistic sample data for the domain
- Document testing approaches with specific queries
- State assumptions clearly when inferring requirements
Scope
This skill applies to:
- Real-time data streaming use cases
- Event-driven architectures
- Stream processing and aggregation
- IoT sensor data processing
- Financial transaction processing
- Retail inventory management
- Healthcare vitals monitoring
- Logistics tracking systems
- Any domain requiring real-time data processing
The output is a complete streaming system with Terraform, Flink SQL, Python, and documentation.
Procedure
You are a Confluent Cloud streaming architect specializing in Infrastructure-as-Code and real-time data processing. When provided with a streaming use case, generate a complete streaming system following this two-phase workflow.
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.
- 12d ago First seen · 914 lines · 63 tokens per session scan A 2e5f67334eaa
confluent-iac-terraform is a skill published in the GitHub repository ibm-self-serve-assets/building-blocks (24 stars, last pushed today), licensed Apache-2.0. It adds 63 tokens to every session and 7,213 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.
Other skills, from other repositories
tensorrt-llm
High-throughput LLM inference on NVIDIA GPUs.
google-cloud-solution-guided-gke-ai-migration
Guides the migration of existing AI workloads (Cloud Run, Gemini API, Gemini Enterprise Agent Platform) to self-hosted GKE inference using gcloud and kubectl. Use when the user has an existing AI inference workload (on Cloud Run, the Gemini API, Gemini Enterprise Agent Platform, or a custom VM) and wants to move it to…
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for generic batch jobs or HPC task queues (use gke-batch-hpc instead).
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…
modal
Modal is a serverless cloud platform for running Python on demand, including on-demand GPUs. Use when deploying or serving AI/ML models, running GPU-accelerated workloads (training, fine-tuning, inference), serving web endpoints, scheduling batch jobs, or scaling Python code to cloud containers with the Modal SDK.