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 agentmods add commands/joshuaramirez/ms-ado-az-claude-code-plugin/setupgit clone --depth 1 https://github.com/JoshuaRamirez/ms-ado-az-claude-code-pluginWrote 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/commands/joshuaramirez/ms-ado-az-claude-code-plugin/setup)<a href="https://agentmods.dev/commands/joshuaramirez/ms-ado-az-claude-code-plugin/setup"><img src="https://agentmods.dev/badge/commands/joshuaramirez/ms-ado-az-claude-code-plugin/setup.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 | $0.00008 | $0.00329 |
| Opus 5 | $0.00004 | $0.00164 |
| Sonnet 5 | $0.00002 | $0.00066 |
| Haiku 4.5 | $0.00001 | $0.00033 |
Grade A, and why
setup 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.
What it actually says
Azure DevOps Setup
Guide the user through Azure DevOps CLI configuration.
Step 1: Check Azure CLI
Verify Azure CLI is installed and check if devops extension is present:
az --version
az extension list --query "[?name=='azure-devops']"
If extension not installed:
az extension add --name azure-devops
Step 2: Authentication
Check current login status:
az account show
If not logged in, guide user through:
az login
For PAT-based auth (alternative):
az devops login
Step 3: Configure Defaults
Ask user for their organization URL and default project:
az devops configure --defaults organization=https://dev.azure.com/{ORG} project={PROJECT}
Verify configuration:
az devops configure --list
Step 4: Test Connection
Run a simple query to verify everything works:
az devops project show --project {PROJECT}
Troubleshooting
If commands fail:
- Verify org URL format:
https://dev.azure.com/{orgname}(not{orgname}.visualstudio.com) - Check project name is correct (case-sensitive)
- Verify user has access to the project
- Try
az account clearandaz loginagain
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 First seen · 68 lines · 8 tokens per session scan A d9c90693e8a6
setup is a command published in the GitHub repository JoshuaRamirez/ms-ado-az-claude-code-plugin (5 stars, last pushed today), licensed MIT. It adds 8 tokens to every session and 329 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.