Borrowing it
Nothing to install: this file belongs to julianobarbosa/azure-finops-mcp-server. 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/julianobarbosa/azure-finops-mcp-server/main/.claude/commands/BMad/tasks/nfr-assess.mdgit clone --depth 1 https://github.com/julianobarbosa/azure-finops-mcp-serverWrote 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/julianobarbosa/azure-finops-mcp-server/nfr-assess)<a href="https://agentmods.dev/commands/julianobarbosa/azure-finops-mcp-server/nfr-assess"><img src="https://agentmods.dev/badge/commands/julianobarbosa/azure-finops-mcp-server/nfr-assess/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/commands/julianobarbosa/azure-finops-mcp-server/nfr-assess"><img src="https://agentmods.dev/badge/commands/julianobarbosa/azure-finops-mcp-server/nfr-assess.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00000 | $0.01895 |
| Opus 5 | $0.00000 | $0.00948 |
| Sonnet 5 | $0.00000 | $0.00379 |
| Haiku 4.5 | $0.00000 | $0.00189 |
Grade A, and why
nfr-assess 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 11d 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.
This is a copy
100% identical to nfr-assess — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 350 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/nfr-assess Task
When this command is used, execute the following task:
nfr-assess
Quick NFR validation focused on the core four: security, performance, reliability, maintainability.
Inputs
required:
- story_id: '{epic}.{story}' # e.g., "1.3"
- story_path: `bmad-core/core-config.yaml` for the `devStoryLocation`
optional:
- architecture_refs: `bmad-core/core-config.yaml` for the `architecture.architectureFile`
- technical_preferences: `bmad-core/core-config.yaml` for the `technicalPreferences`
- acceptance_criteria: From story file
Purpose
Assess non-functional requirements for a story and generate:
- YAML block for the gate file's
nfr_validationsection - Brief markdown assessment saved to
qa.qaLocation/assessments/{epic}.{story}-nfr-{YYYYMMDD}.md
Process
0. Fail-safe for Missing Inputs
If story_path or story file can't be found:
- Still create assessment file with note: "Source story not found"
- Set all selected NFRs to CONCERNS with notes: "Target unknown / evidence missing"
- Continue with assessment to provide value
1. Elicit Scope
Interactive mode: Ask which NFRs to assess Non-interactive mode: Default to core four (security, performance, reliability, maintainability)
Which NFRs should I assess? (Enter numbers or press Enter for default)
[1] Security (default)
[2] Performance (default)
[3] Reliability (default)
[4] Maintainability (default)
[5] Usability
[6] Compatibility
[7] Portability
[8] Functional Suitability
> [Enter for 1-4]
2. Check for Thresholds
Look for NFR requirements in:
- Story acceptance criteria
docs/architecture/*.mdfilesdocs/technical-preferences.md
Interactive mode: Ask for missing thresholds Non-interactive mode: Mark as CONCERNS with "Target unknown"
No performance requirements found. What's your target response time?
> 200ms for API calls
No security requirements found. Required auth method?
> JWT with refresh tokens
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.
- 11d ago First seen · 350 lines · 0 tokens per session scan A b9c68a16a776
nfr-assess is a command published in the GitHub repository julianobarbosa/azure-finops-mcp-server (2 stars, last pushed 8mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,895 tokens. A static security scan graded it A with 0 findings. It is 100% identical to nfr-assess, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.