Azure Developer CLI (azd) is a command-line tool for building, deploying, and operating applications on Azure. Developers use it to move through application workflows such as coding, building, deployment, and monitoring with Azure templates and resources. Its catalogue entries help AI agents work with azd and Azure application projects.
Borrowing it
Nothing to install: this file belongs to Azure/azure-dev. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Azure/azure-dev/main/.github/skills/weekly-report/SKILL.mdgit clone --depth 1 https://github.com/Azure/azure-devWrote 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/azure/azure-dev/weekly-report)<a href="https://agentmods.dev/skills/azure/azure-dev/weekly-report"><img src="https://agentmods.dev/badge/skills/azure/azure-dev/weekly-report/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/azure/azure-dev/weekly-report"><img src="https://agentmods.dev/badge/skills/azure/azure-dev/weekly-report.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 | $0.00122 | $0.00772 |
| Opus 5.5 | $0.00049 | $0.00309 |
| Sonnet 5.5 | $0.00024 | $0.00154 |
| Haiku 4.5 | $0.00012 | $0.00077 |
Grade A, and why
weekly-report 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.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weekly Executive Report Generator
Generates weekly executive reports for the Azure Developer CLI (azd) team leadership.
Prerequisites
| Tool | Purpose |
|---|---|
gh |
GitHub CLI — authenticated with repo access |
jq |
JSON filtering for release queries |
git |
Repository data |
Report Format (strict order)
📝 TLDR
📊 Metrics
⚠️ Risks + Blockers
🎯 Changelog
📋 This Week
🔜 Next Week
🚩 Learnings
🔥 Demos/Links
Execution Flow
Step 1: Confirm date range
Cadence is Thursday to Thursday. Confirm with the user if unclear.
Step 2: Pull repo data
Replace START and END below with the dates from Step 1.
cd <repo-root> # the azure-dev repository root
git checkout main && git pull --rebase
PRs merged:
gh pr list --repo Azure/azure-dev --state merged --search "merged:START..END" --limit 200 --json number,title,mergedAt
Issues closed:
gh issue list --repo Azure/azure-dev --state closed --search "closed:START..END" --limit 200 --json number,title,closedAt
Releases:
gh api repos/Azure/azure-dev/releases --paginate | jq '[.[] | select(.published_at >= "START" and .published_at <= "END") | {name, tag_name, published_at}]'
Step 3: Read changelogs
Changelog data is maintained by the changelog-generation skill.
- Core:
cli/azd/CHANGELOG.md - Agents:
cli/azd/extensions/azure.ai.agents/CHANGELOG.md - Finetuning:
cli/azd/extensions/azure.ai.models/CHANGELOG.md
Step 4: Wait for team notes
Do NOT assemble the report until the user provides team notes from emails, Teams, meetings, etc. Only product and engineering content.
Step 5: Assemble report
Combine repo data with team notes. Follow all rules below.
Step 6: Iterate
Expect 5–15 rounds of edits. Apply changes surgically. After removals, check for related content elsewhere (risks → next week → learnings → TLDR) and offer to clean up.
Output
Save reports to the Copilot CLI session workspace (~/.copilot/session-state/<session-id>/files/) as weekly-report-{month}{startday}-{endday}.md.
What ships with it
2 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.
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.
- 1mo ago First seen · 110 lines · 122 tokens per session scan A 43eb7738ab79
weekly-report is a skill published in the GitHub repository Azure/azure-dev (570 stars, last pushed today), licensed MIT. It adds 122 tokens to every session and 772 once invoked, about $0.0005 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 2026-08-30.
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