nfr-jira-epic

nfr-jira-epic is a skill for Claude Code from DavidROliverBA/ArchitectKB. It costs 27 tokens per session (1,438 once invoked), scanned A, original, MIT.

A command that turns applicable non-functional requirements—such as security, privacy, and performance rules—into a Jira Epic with individual stories.

In plain words
What is it for?
Use it to create Jira stories containing each requirement, evidence guidance, and acceptance criteria for a specified system and compliance scope.
Why use it?
It removes the need to copy requirements into sprint work by hand and keeps compliance work trackable in Jira.

Skill for Claude Code

Written for Claude Code: arguments in frontmatter. Also seen: model in frontmatter; reads .claude/ paths.

Good fit Use it to create Jira stories containing each requirement, evidence guidance, and acceptance criteria for a specified system and compliance scope.

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Install with agentmods
npx agentmods add skills/davidroliverba/architectkb/nfr-jira-epic
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.

Any agent
npx skills add DavidROliverBA/ArchitectKB --skill nfr-jira-epic
Clone the repo
git clone --depth 1 https://github.com/DavidROliverBA/ArchitectKB

Made for: Claude Code.

Wrote this? Show the measurements

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README.md
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Your own site · 80×15
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Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,438 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 $0.00027 $0.01438
Opus 5 $0.00014 $0.00719
Sonnet 5 $0.00005 $0.00288
Haiku 4.5 $0.00003 $0.00144

Measured 11d ago against content hash b907d9703470, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

nfr-jira-epic 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.

.claude/skills/nfr-jira-epic/SKILL.md · 173 lines

How it starts

The opening of the file, as written. The whole thing — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/nfr-jira-epic

Create a Jira Epic with one story per applicable NFR, turning the NFR compliance table into trackable sprint work. Each story includes the requirement, evidence guidance, and acceptance criteria.

Usage

/nfr-jira-epic ERPSystem CS1 all,gdpr
/nfr-jira-epic "AlertHub" CS1 all,gdpr ARCH
/nfr-jira-epic DataPlatform CS2 all,pci,gdpr,caa_nis

Data Source

All NFR data is read from .claude/data/nfr-reference.yaml — the single source of truth for all 66 NFRs. Do NOT hard-code NFR content; always read from the YAML.

Prerequisites

  • Atlassian MCP tools must be available (Jira access via createJiraIssue)
  • User must have permissions to create issues in the target Jira project

Instructions

Phase 1: Load NFR Data

  1. Read .claude/data/nfr-reference.yaml
  2. Parse sections and NFRs
  3. Read .claude/data/nfr-evidence-rules.yaml for automated check references

Phase 2: Filter NFRs

  1. Parse the types argument into a list (split on comma)
  2. Always include all in the types list
  3. Filter sections by applicability (same logic as /nfr-capture)
  4. Map CS tier to SL tier: CS1→SL1, CS2→SL2, CS3→SL3, CS4→SL4

Phase 3: Determine Jira Project

If project argument is provided, use it. Otherwise, ask the user:

Which Jira project should the NFR Epic be created in?
Enter the Jira project key (e.g., ARCH, ENG, OPS):

Phase 4: Create Epic

Use the Atlassian MCP createJiraIssue tool to create the Epic:

  • Issue Type: Epic
  • Project: [project key]
  • Summary: NFR Compliance — [System Name] ([CS tier]/[SL tier])
  • Description:
h2. NFR Compliance Epic

*System:* [System Name]
*Classification:* [CS tier] / [SL tier]
*Applicability:* [types list]
*Sections:* [included count] of 13
*NFRs:* [included NFR count] of 66

This epic tracks NFR compliance for [System Name] as defined in the BA NFR Template (Confluence page 664765269, v0.2).

Each story represents one NFR requirement. Stories close when evidence is attached and reviewed.

*Generated by:* /nfr-jira-epic skill
*NFR Reference:* .claude/data/nfr-reference.yaml

Read the full file on GitHub · 173 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. 11d ago First seen · 173 lines · 27 tokens per session scan A b907d9703470

Subscribe to this mod's changes

nfr-jira-epic is a skill published in the GitHub repository DavidROliverBA/ArchitectKB (52 stars, last pushed 6mo ago), licensed MIT. It adds 27 tokens to every session and 1,438 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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