DavidROliverBA/nfr-jira-epic
.claude/skills/nfr-jira-epic/SKILL.md
Create a Jira Epic with one story per applicable NFR for tracking NFR compliance as sprint work
$ install --global
skillsauthnpx skillsauth add DavidROliverBA/ArchitectKB nfr-jira-epicInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 16, 2026, 6:24 PM7.7s1 file scanned
SKILL.md
- name:
- nfr-jira-epic
- skill:
- nfr-jira-epic
- description:
- Jira project key (e.g., "ARCH", "ENG") — if omitted, skill will ask
- context:
- fork
- - name:
- project
- required:
- false
- model:
- opus
/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
- Read
.claude/data/nfr-reference.yaml - Parse sections and NFRs
- Read
.claude/data/nfr-evidence-rules.yamlfor automated check references
Phase 2: Filter NFRs
- Parse the
typesargument into a list (split on comma) - Always include
allin the types list - Filter sections by applicability (same logic as
/nfr-capture) - 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
- Labels:
nfr-compliance,nfr-epic
Phase 5: Create Stories
For each applicable NFR, create a Jira story linked to the Epic:
- Issue Type: Story
- Project: [project key]
- Summary:
[NFR ID] — [NFR title] - Description:
h2. [NFR ID]: [NFR title]
h3. Requirement
[nfr.requirement]
h3. Guidance
[nfr.guidance]
h3. Target ([SL tier])
[tier_values for SL tier if tiered, else "Not tiered — applies uniformly"]
h3. Evidence Guidance
[nfr.evidence_guidance]
h3. Evidence Type
[nfr.evidence_type] — [If automated: "Automated checks available via nfr-evidence-collect.sh"]
h3. Acceptance Criteria
* Evidence is documented and linked to this story
* Evidence matches the format described in Evidence Guidance
* Status is confirmed as Met, Partial, or N/A with justification
[If evidence_type == automated]:
* Automated check results attached (AWS Config / CLI output)
- Labels:
nfr-compliance,nfr-[section-id lowercase](e.g.,nfr-sec,nfr-rel) - Priority: Mapped from CS tier:
- CS1 → Critical
- CS2 → High
- CS3 → Medium
- CS4 → Low
- Epic Link: Link to the Epic created in Phase 4
Rate limiting: Pause briefly between story creation calls to avoid Jira API rate limits. Create stories section by section.
Phase 6: Summary
After creating all stories, print a summary:
NFR Jira Epic Created for [System Name] ([CS tier]/[SL tier])
Epic: [PROJ]-[ID] — NFR Compliance — [System Name] ([CS tier]/[SL tier])
URL: [epic URL]
Stories created: [count] of [total applicable NFRs]
| Section | Stories | IDs |
|---------|---------|-----|
| [Section Name] | [count] | [PROJ-ID, PROJ-ID, ...] |
| ... | ... | ... |
Priority: [CS1→Critical/CS2→High/CS3→Medium/CS4→Low]
Labels: nfr-compliance, nfr-[section-ids]
Next steps:
1. Assign stories to team members or squads
2. Add to sprint backlog
3. Use /nfr-capture with-evidence-prompts for guidance on completing each NFR
4. Close stories when evidence is attached and reviewed
Related
.claude/data/nfr-reference.yaml— NFR single source of truth.claude/data/nfr-evidence-rules.yaml— Automated AWS evidence checks.claude/skills/nfr-capture/SKILL.md— Generate NFR tables.claude/skills/nfr-review/SKILL.md— Gap analysis against existing HLDs
Related Skills
DavidROliverBA/.claude/skills/youtube
tools
VerifiedTrustedCommunity
--- context: fork --- # /youtube Save a YouTube video as both a Weblink (quick reference) and a detailed Page (full analysis). ## Usage ``` /youtube <url> /youtube <url> <optional title override> ``` ## Examples ``` /youtube https://www.youtube.com/watch?v=0TpON5T-Sw4 /youtube https://youtu.be/abc123 AWS re:Invent Keynote ``` ## Prerequisites This skill uses the MCP Docker YouTube tools: - `mcp__MCP_DOCKER__get_video_info` - Video metadata - `mcp__MCP_DOCKER__get_transcript` - Full trans
41SKILL.mdUpdated Apr 16, 2026
DavidROliverBA/worktree
data-ai
VerifiedTrustedCommunity
Create and manage git worktrees for parallel agent sessions
41SKILL.mdUpdated Apr 16, 2026
DavidROliverBA/.claude/skills/wipe
testing
VerifiedTrustedCommunity
--- context: fork --- # /wipe Generate a context handoff summary, clear the session, and resume in a fresh conversation. Detects environment and provides automated (tmux) or manual workflow. ## Usage ``` /wipe /wipe quick # Minimal handoff, just essentials /wipe detailed # Comprehensive handoff with full context ``` ## Instructions When the user invokes `/wipe`: ### Phase 1: Detect Environment First, check the terminal environment: ```bash echo "Environment Detection:"
41SKILL.mdUpdated Apr 16, 2026
DavidROliverBA/.claude/skills/weekly-summary
data-ai
VerifiedTrustedCommunity
--- context: fork --- # /weekly-summary Generate comprehensive weekly summary from daily notes, meetings, tasks, and project updates using parallel sub-agents. ## Usage ``` /weekly-summary /weekly-summary --last-week /weekly-summary --from 2026-01-01 --to 2026-01-07 /weekly-summary --output page # Create Page note instead of just outputting ``` ## Instructions This skill uses **5 parallel sub-agents** to gather data concurrently from different vault areas, then synthesizes a comprehensi
41SKILL.mdUpdated Apr 16, 2026