azure-functions-skills: Command for GitHub Copilot

.github/prompts/cross-review.prompt.md

cross-review is a command for GitHub Copilot from Azure/azure-functions-skills. It costs 18 tokens per session (157 once invoked), scanned A, original, MIT.

A review process that asks a different AI model family to independently inspect an implementation. It uses a second perspective to find issues the first model may miss.

In plain words
What is it for?
It is for cross-checking completed implementations with another model and deciding which findings to fix.
Why use it?
It reduces blind spots in correctness, error handling, design, and test coverage before work is finalized.

Command for GitHub Copilot ✓ vendor

Written for GitHub Copilot: a Copilot chat mode or prompt.

This is Azure/azure-functions-skills's own configuration. It tells GitHub Copilot how to work on azure-functions-skills itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything azure-functions-skills configures →

About the project

Azure Functions Skills is a repository of agent skills, prompts, reusable definitions, MCP configuration, and telemetry hooks that give coding agents context about Azure Functions development and operations. Developers use it for guidance on triggers, bindings, runtimes, deployment, and code checks, while the catalogue contains its agent-facing add-ons.

Azure/azure-functions-skills · 20 stars · on GitHub

Reuse

Borrowing it

Nothing to install: this file belongs to Azure/azure-functions-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Azure/azure-functions-skills/main/.github/prompts/cross-review.prompt.md
Clone the repo
git clone --depth 1 https://github.com/Azure/azure-functions-skills

Made for: GitHub Copilot.

Wrote 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.

agentmods badge for cross-review

README.md
[![agentmods](https://agentmods.dev/badge/commands/azure/azure-functions-skills/cross-review/github.svg)](https://agentmods.dev/commands/azure/azure-functions-skills/cross-review)
Your own site
<a href="https://agentmods.dev/commands/azure/azure-functions-skills/cross-review"><img src="https://agentmods.dev/badge/commands/azure/azure-functions-skills/cross-review/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.

agentmods 80×15 button for cross-review

Your own site · 80×15
<a href="https://agentmods.dev/commands/azure/azure-functions-skills/cross-review"><img src="https://agentmods.dev/badge/commands/azure/azure-functions-skills/cross-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 18 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 157 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00018 $0.00157
Opus 5.5 $0.00007 $0.00063
Sonnet 5.5 $0.00004 $0.00031
Haiku 4.5 $0.00002 $0.00016

Measured 1mo ago against content hash 21a7aa796704, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

cross-review 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 1mo 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.

.github/prompts/cross-review.prompt.md · 19 lines

What it actually says

Perform a cross-model self-review after implementation:

  1. Check the current model with /model.
  2. Switch to a different model family for independent validation:
    • If currently using Claude (Ops family) → switch to the latest top-tier GPT model.
    • If currently using GPT → switch to the latest top-tier Claude model.
  3. Run /rubber-duck to walk through the implementation and catch blind spots.
  4. Review the feedback for:
    • Correctness and robustness
    • Edge cases and error handling
    • Architectural soundness
    • Test coverage gaps
  5. Address any valid findings before finalizing.
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. 1mo ago First seen · 19 lines · 18 tokens per session scan A 21a7aa796704

Subscribe to this mod's changes

cross-review is a command published in the GitHub repository Azure/azure-functions-skills (20 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 157 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-09.