final-report
Final engagement report generation — executive summary, technical report, findings aggregation, attack path narrative, detection gap matrix, remediation roadmap.
Best for Security engagement teamsWorks with GitHubLow risk
⌘source
- author
@PurpleAILAB- language
- Python
★2.1kstars
366forks
Apr 18, 2026
✦overview.md
Key Features
- ·Generates executive summary and technical report
- ·Aggregates findings from multiple sources
- ·Creates remediation roadmap
- ·Builds attack path narrative
- ·Produces detection gap matrix
- ·Outputs structured Markdown documents
Use Cases
- →Compiling final reports after security engagement completion
- →Creating executive summaries for stakeholder presentations
- →Generating technical documentation for remediation teams
- →Aggregating findings from multiple penetration testing phases
Best for
- ✓Security engagement teams
- ✓Penetration testers
- ✓Red team operations
Not ideal for
- !Real-time reporting
- !One-off document generation
- !Non-security contexts
FAQs
name
final-report
description
Final engagement report generation — executive summary, technical report, findings aggregation, attack path narrative, detection gap matrix, remediation roadmap.
allowed-tools:Read Write
metadata:{"subdomain":"orchestration","when_to_use":"final report, generate report, engagement complete, all objectives done, executive summary, technical report","tags":"report, final, executive-summary, technical-report, remediation, detection-gap","mitre_attack":[]}
Final Engagement Report Generation
Execute when all OPPLAN objectives are in passed or blocked status. Output is two Markdown documents in report/.
Report Generation Workflow
1. Read all findings/*.md (parse YAML frontmatter for metadata)
2. Read all findings/attack-paths/PATH-*.md
3. Read timeline.jsonl (activity log)
4. Read plan/roe.json + plan/conops.json (engagement context)
5. Read plan/opplan.json (objective status summary)
6. Generate report/executive-summary.md
7. Generate report/technical-report.md
8. (Optional) Refresh findings.md with final summary
Step 1: Initialize Output Directory
bash(command="mkdir -p /workspace/<slug>/report")
Step 2: Aggregate Findings
Read every findings/*.md (named {severity}-{slug}.md) and extract frontmatter fields:
for each FIND-xxx.md:
- id, title, severity, cvss, target, phase, technique (ATT&CK ID), status, confidence
Sorting rules:
- Primary: severity (
critical→high→medium→low→informational) - Secondary: CVSS score descending
$install
1-click copynpx skills add PurpleAILAB/Decepticon --skill final-reportSafety assessment
★
Clarity score
How clear and easy to understand the SKILL.md instructions are, rated from 1 to 5.
5/ 5
excellentVery clear and well structured, with almost no room for misunderstanding.
◎
Actionability score
How directly an agent can act on the SKILL.md instructions, rated from 1 to 5.
5/ 5
very highHighly actionable with clear, concrete steps that an agent can follow directly.