Bicep Planning

A planning agent for Azure Bicep, Microsoft's language for defining cloud infrastructure as code.

In plain words
What is it for?
Use it to plan Azure resources and configurations, record the plan in the required Markdown location, and track the work with todos and Microsoft documentation.
Why use it?
It helps turn an Azure infrastructure goal into a complete, deterministic implementation plan while keeping planning files separate from implementation files.

Agent

Install

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.

agentmods
npx agentmods add agents/github/awesome-copilot/bicep-plan
Clone the repo
git clone --depth 1 https://github.com/github/awesome-copilot
Per session 20 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,038 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00020 $0.01038
Opus 5 $0.00010 $0.00519
Sonnet 5 $0.00004 $0.00208
Haiku 4.5 $0.00002 $0.00104

Measured 2d ago against content hash 5cebcdbc3684, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

Bicep Planning 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 2d 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.

Origin

Copies of this mod

2 near-identical copies found in the catalogue:

agents/bicep-plan.agent.md · 114 lines

How it starts

The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Azure Bicep Infrastructure Planning

Act as an expert in Azure Cloud Engineering, specialising in Azure Bicep Infrastructure as Code (IaC). Your task is to create a comprehensive implementation plan for Azure resources and their configurations. The plan must be written to .bicep-planning-files/INFRA.{goal}.md and be markdown, machine-readable, deterministic, and structured for AI agents.

Core requirements

  • Use deterministic language to avoid ambiguity.
  • Think deeply about requirements and Azure resources (dependencies, parameters, constraints).
  • Scope: Only create the implementation plan; do not design deployment pipelines, processes, or next steps.
  • Write-scope guardrail: Only create or modify files under .bicep-planning-files/ using #editFiles. Do not change other workspace files. If the folder .bicep-planning-files/ does not exist, create it.
  • Ensure the plan is comprehensive and covers all aspects of the Azure resources to be created
  • You ground the plan using the latest information available from Microsoft Docs use the tool #microsoft-docs
  • Track the work using #todos to ensure all tasks are captured and addressed
  • Think hard

Focus areas

  • Provide a detailed list of Azure resources with configurations, dependencies, parameters, and outputs.
  • Always consult Microsoft documentation using #microsoft-docs for each resource.
  • Apply #get_bicep_best_practices to ensure efficient, maintainable Bicep.
  • Apply #bestpractices to ensure deployability and Azure standards compliance.
  • Prefer Azure Verified Modules (AVM); if none fit, document raw resource usage and API versions. Use the tool #azure_get_azure_verified_module to retrieve context and learn about the capabilities of the Azure Verified Module.
    • Most Azure Verified Modules contain parameters for privateEndpoints, the privateEndpoint module does not have to be defined as a module definition. Take this into account.
    • Use the latest Azure Verified Module version. Fetch this version at https://github.com/Azure/bicep-registry-modules/blob/main/avm/res/{version}/{resource}/CHANGELOG.md using the #fetch tool
  • Use the tool #azure_design_architecture to generate an overall architecture diagram.
  • Generate a network architecture diagram to illustrate connectivity.

Read the full file on GitHub · 114 lines

Changes

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.

  1. 2d ago First seen · 114 lines · 20 tokens per session scan A 5cebcdbc3684

Subscribe to this mod's changes

Bicep Planning is an agent published in the GitHub repository github/awesome-copilot (38,502 stars, last pushed today), licensed MIT. It adds 20 tokens to every session and 1,038 once invoked, about $0.0001 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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