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Perseus:Report

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kaivyy
perseus:report

Use when generating the final executive security report (Phase 4)

Overview

Publisherkaivyy
Repositoryperseus
Skill nameperseus:report
Stars
68
Forks
14
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by kaivyy on GitHub. Read the source before you install it.

Installation

Install the Perseus:Report AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/kaivyy/perseus.git /tmp/perseus
mkdir -p .claude/skills
cp -r /tmp/perseus/skills/perseus/report .claude/skills/kaivyy-perseus-report
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Perseus:Report in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Perseus:Report on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Perseus:Report is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Perseus Report (Phase 4)

Context & Authorization

IMPORTANT: This skill generates a professional security report for the user's own codebase. The report is intended to help developers and stakeholders understand and remediate security issues in their own software.


Multi-Language Support

Report generation adapts remediation guidance based on detected language/framework:

LanguageFrameworks
JavaScript/TypeScriptExpress, Fastify, Next.js, Nest.js
GoGin, Echo, Fiber, Chi
PHPLaravel, Symfony, Slim
PythonFastAPI, Django, Flask
RustActix-web, Axum, Rocket
JavaSpring Boot, Quarkus
RubyRails, Sinatra
C#ASP.NET Core

Overview

This skill executes the Reporting Phase of the Perseus framework. It synthesizes findings from Scan, Audit, Exploit, and Specialist phases into a professional executive report.

Goal: Communicate verified risks effectively to stakeholders and drive remediation.

Methodology:

  1. Collect: Gather all deliverables from previous phases.
  2. Verify: Prioritize verified exploits over theoretical risks.
  3. Contextualize: Explain business impact, not just technical flaws.
  4. Remediate: Provide language-specific, actionable fixes for each finding.

Execution Instructions

Step 1: Collect All Deliverables

Read all files in deliverables/:

Core Phase Deliverables:

  • engagement_profile.md (Mode, scope, constraints)
  • code_analysis_deliverable.md (Scan)
  • All *_analysis.md files (Audit)
  • exploitation_report.md (Exploit)
  • verification_scope.md (Verification limits and approved window)

Specialist Deliverables (if present):

  • api_security_analysis.md (API + GraphQL + WebSocket + OAuth)
  • injection_deep_analysis.md (NoSQL + SSTI + Log4j)
  • crypto_security_analysis.md (JWT + Hashing + Encryption)
  • supply_chain_analysis.md (CVEs + Typosquatting)
  • file_security_analysis.md (Path Traversal + XXE + Zip Slip)
  • business_logic_analysis.md (Race Conditions + AI Security)
  • client_side_analysis.md (React/Vue/Angular + SSR)
  • config_security_analysis.md (Docker + CI/CD + Cloud + K8s)

Step 2: Calculate Risk Metrics

Severity Scoring (CVSS-inspired):

SeverityCriteriaScore
CriticalRCE, Auth Bypass, SQLi with data access, Admin takeover, Container escape, AI prompt injection leading to RCE9.0-10.0
HighStored XSS, SSRF to internal, Privilege Escalation, Sensitive data exposure, CI/CD injection, Public cloud storage7.0-8.9
MediumReflected XSS, CSRF, Information disclosure, Missing security headers, Outdated dependencies, Docker misconfig4.0-6.9
LowMinor info leak, Best practice violations, Verbose errors, License issues0.1-3.9

Exploitability Factor:

  • VERIFIED: Multiply by 1.0 (confirmed exploitable)
  • POTENTIAL: Multiply by 0.7 (likely exploitable)
  • THEORETICAL: Multiply by 0.4 (needs specific conditions)

Confidence Factor:

  • High: Multiply by 1.0
  • Medium: Multiply by 0.75
  • Low: Multiply by 0.5

Verification Context Factor:

  • VERIFIED in PRODUCTION_SAFE: Multiply by 1.0
  • VERIFIED in STAGING_ACTIVE: Multiply by 0.9
  • VERIFIED in LAB_FULL: Multiply by 0.85
  • VERIFIED in LAB_RED_TEAM: Multiply by 0.8
  • POTENTIAL-PROD-BLOCKED: Multiply by 0.7

Step 3: Generate Report

Create deliverables/SECURITY_REPORT.md using this structure:

markdown
# Security Assessment Report

**Project:** [Project Name]
**Assessment Date:** [Date]
**Methodology:** Perseus Security Framework v2.0
**Scope:** [Repository/Application name]
**Engagement Mode:** [PRODUCTION_SAFE/STAGING_ACTIVE/LAB_FULL/LAB_RED_TEAM]

---

## Executive Summary

### Assessment Status
[Complete/Partial] - [X] phases completed, [Y] specialists run

### Technologies Analyzed
| Category | Detected |
|----------|----------|
| Language | [e.g., TypeScript, Go, Python] |
| Framework | [e.g., Next.js 14, Gin, FastAPI] |
| Database | [e.g., PostgreSQL, MongoDB, Redis] |
| Infrastructure | [e.g., Docker, Kubernetes, AWS] |
| CI/CD | [e.g., GitHub Actions, GitLab CI] |
| AI/LLM | [e.g., OpenAI, Anthropic, LangChain] |

### Risk Overview
| Severity | Verified | Potential | Total |
|----------|----------|-----------|-------|
| Critical | X | Y | Z |
| High | X | Y | Z |
| Medium | X | Y | Z |
| Low | X | Y | Z |

### Verification Coverage
| Category | Count |
|----------|-------|
| Verified | X |
| Failed Verification | Y |
| Potential (Prod Blocked) | Z |
| Aborted (Safety Kill-Switch) | W |

### Key Findings
1. **[Most Critical Finding]** - Brief description and impact
2. **[Second Critical Finding]** - Brief description and impact
3. **[Third Critical Finding]** - Brief description and impact

### Business Impact
- **Data Breach Risk:** [High/Medium/Low] - [Explanation]
- **Service Disruption:** [High/Medium/Low] - [Explanation]
- **Compliance Impact:** [Regulations affected: GDPR, PCI-DSS, HIPAA, SOC2, etc.]
- **Reputation Risk:** [High/Medium/Low] - [Explanation]

### Top 3 Recommendations
1. [Highest priority fix]
2. [Second priority fix]
3. [Third priority fix]

---

## Attack Surface Summary

### Technology Stack
- **Language:** [e.g., TypeScript]
- **Framework:** [e.g., Next.js 14 (App Router)]
- **Database:** [e.g., MongoDB, PostgreSQL]
- **Authentication:** [e.g., JWT, NextAuth, OAuth]
- **Infrastructure:** [e.g., Docker, Kubernetes, Vercel]

### Entry Points Analyzed
| Type | Count | Critical Paths |
|------|-------|----------------|
| API Endpoints | X | `/api/auth/*`, `/api/admin/*` |
| GraphQL | X | `mutation { ... }` |
| WebSocket | X | `/ws/chat` |
| File Upload | X | `/upload` |
| Server Actions | X | `app/actions/*` |

### Dependencies
- **Total Packages:** X
- **With Known CVEs:** Y
- **Critical CVEs:** Z (list package names)

### Infrastructure
- **Docker Images:** X
- **CI/CD Pipelines:** Y
- **Cloud Resources:** Z

---

## Critical Findings (Verified Exploits)

> Only findings verified in the Exploit Phase appear here.

### CRITICAL-001: [Title]

**Severity:** Critical (9.5)
**Status:** VERIFIED EXPLOITABLE
**Category:** [Injection/Auth/Config/AI/etc.]
**Language:** [Node.js/Go/Python/etc.]

#### Description
[Clear explanation of the vulnerability]

#### Location
- **File:** `path/to/file.js`
- **Line:** 42-48
- **Endpoint:** `POST /api/vulnerable`

#### Proof of Concept

[Minimal reproduction steps or payload]


#### Evidence
[Screenshot, response, or output proving exploitation]

#### Impact
- [Specific impact: data access, RCE, etc.]
- [Business impact: what could attacker do?]

#### Remediation
```javascript
// Vulnerable code
[code snippet]

// Fixed code (language-specific)
[code snippet]
References
  • [OWASP Link]
  • [CWE Link]

CRITICAL-002: ...


Infrastructure Security Findings

Findings from Docker, CI/CD, Cloud, and Kubernetes analysis.

Docker Security

CheckStatusIssue
Non-root userPASS/FAIL[Details]
Pinned base imagePASS/FAIL[Details]
No secrets in imagePASS/FAIL[Details]
Minimal basePASS/FAIL[Details]

CI/CD Security

CheckStatusIssue
No command injectionPASS/FAIL[Details]
Minimal permissionsPASS/FAIL[Details]
Secrets not in logsPASS/FAIL[Details]

Cloud Security

ResourceIssueSeverity
S3 BucketPublic accessCritical
Security GroupOpen portsHigh

Kubernetes Security

CheckStatus
Non-root podsPASS/FAIL
Network policiesPASS/FAIL
RBAC configuredPASS/FAIL
Secrets encryptedPASS/FAIL

AI/LLM Security Findings

Findings from AI security analysis (if applicable).

AI Security Status

CheckStatusRisk
Prompt Injection ProtectionPASS/FAIL[Details]
Output FilteringPASS/FAIL[Details]
Tool Use ValidationPASS/FAIL[Details]
RAG Access ControlPASS/FAIL[Details]
Rate LimitingPASS/FAIL[Details]

AI-VULN-001: [Title]

Type: [Prompt Injection/Data Leakage/Tool Abuse] ...


High Severity Findings

HIGH-001: ...


Medium Severity Findings

MEDIUM-001: ...


Low Severity Findings

LOW-001: ...


Potential Vulnerabilities (Unverified)

Findings from Audit that could not be verified but remain risky.

POTENTIAL-001: [Title]

Severity: [Estimated] Reason Not Verified: [Why exploitation wasn't confirmed] Mode Constraint: [Why blocked in current mode] Recommendation: [What to do about it]


Supply Chain Summary

Vulnerability Overview

SeverityCountNotable Packages
CriticalX[packages]
HighX[packages]
MediumX[packages]

License Issues

PackageLicenseRisk
[pkg]GPL-3.0Copyleft in proprietary

Outdated Dependencies

PackageCurrentLatestGap
[pkg]X.Y.ZA.B.C[major/minor]

Secure Components

Components analyzed and found to be properly secured.

ComponentSecurity MeasuresNotes
Authenticationbcrypt, rate limiting, MFAProperly implemented
SQL QueriesParameterized queriesNo injection found
DockerNon-root, minimal imageBest practices followed
.........

Strategic Recommendations

Immediate Actions (0-7 days)

  1. [Critical fix 1] - [Language-specific guidance]
  2. [Critical fix 2] - [Language-specific guidance]

Short-term (1-4 weeks)

  1. [High priority improvements]
  2. [Security configuration changes]
  3. [Dependency updates]

Long-term (1-3 months)

  1. [Architectural improvements]
  2. [Security tooling implementation]
  3. [Training recommendations]

Infrastructure Hardening

  1. Docker: [Specific recommendations]
  2. CI/CD: [Specific recommendations]
  3. Cloud: [Specific recommendations]
  4. Kubernetes: [Specific recommendations]

Defense-in-Depth Suggestions

  1. Input Validation: Implement schema validation (Zod, Joi, Pydantic)
  2. Output Encoding: Use context-aware encoding
  3. Security Headers: Implement CSP, HSTS, X-Frame-Options
  4. Monitoring: Add security logging and alerting
  5. Dependencies: Implement automated vulnerability scanning in CI/CD
  6. AI Security: Implement prompt injection detection and output filtering

Appendix

A. Tools Used

  • Perseus Security Framework v2.0
  • Static Analysis: Code pattern matching, AST analysis
  • Dynamic Testing: Safe payload verification

B. Languages & Frameworks Analyzed

  • [List all detected with versions]

C. Scope Exclusions

  • [What was not tested and why]

D. Glossary

  • SQLi: SQL Injection
  • XSS: Cross-Site Scripting
  • SSRF: Server-Side Request Forgery
  • SSTI: Server-Side Template Injection
  • XXE: XML External Entity
  • IDOR: Insecure Direct Object Reference
  • BOLA: Broken Object Level Authorization
  • RCE: Remote Code Execution

## Tone & Style

*   **Professional:** Objective and factual. No hyperbole or fear-mongering.
*   **Actionable:** Every finding has a language-specific remediation.
*   **Developer-Focused:** Code examples for fixes in the detected language.
*   **Business-Aware:** Explain impact in business terms, not just technical.
*   **Infrastructure-Aware:** Include Docker, CI/CD, Cloud, K8s findings.

## Quality Checklist

Before finalizing the report, verify:
- [ ] All verified exploits are documented with evidence
- [ ] All findings have language-specific remediation guidance
- [ ] Severity ratings are consistent
- [ ] Infrastructure findings are included (Docker, CI/CD, Cloud, K8s)
- [ ] AI/LLM findings are included (if applicable)
- [ ] Supply chain summary is complete
- [ ] No sensitive data (real credentials, PII) is included
- [ ] Report can be shared with stakeholders

**Assessment Complete.** Report saved to `deliverables/SECURITY_REPORT.md`.

Frequently asked questions

What does the Perseus:Report AI skill do?

Use when generating the final executive security report (Phase 4)

Why use Perseus:Report on TypingMind?

Because you install it once and use it with any model. Perseus:Report is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Perseus:Report in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/kaivyy/perseus/tree/main/skills/perseus/report. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Perseus:Report?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Perseus:Report?

As many as you like. As long as a model supports skills, you can use Perseus:Report with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Perseus:Report AI skill free?

Yes. It is published on GitHub by kaivyy under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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