Security Audit — Smart Orchestrator

Overview

This is the master orchestrator skill. It auto-detects the project's technology stack and selectively invokes the appropriate security scanning skills. It produces a unified report with context-aware remediation tailored to the project's language, framework, and existing code patterns.

The orchestrator is language-agnostic — it dynamically determines what to scan and how based on what it finds in the project directory.

Available Sub-Skills

Workflow

Step 1: Project Context Detection

Scan the project directory to build a complete picture of the technology stack. Use the Read tool to load references/project_detection.md for comprehensive detection patterns. Use the Glob tool to find manifest and config files.

1a. Language and Runtime Detection

Check for manifest files to identify the primary language(s):

If multiple manifest files exist, the project is polyglot — note all detected languages.

1b. Framework Detection

Read the manifest files and entry points to identify the framework(s) in use:

• Node.js: Express, Koa, Fastify, NestJS, Hapi, Restify
• Python: Flask, Django, FastAPI, Tornado, Sanic, Starlette
• Java: Spring Boot, Micronaut, Quarkus, Jakarta EE, Dropwizard
• Ruby: Rails, Sinatra, Hanami, Grape
• Go: Gin, Echo, Fiber, Chi, net/http (stdlib)
• PHP: Laravel, Symfony, Slim, Lumen, CodeIgniter
• Rust: Actix-web, Axum, Rocket, Warp, Tide
• .NET: ASP.NET Core, Blazor, Minimal APIs

1c. Running Services Detection

Use the Bash tool to probe common development ports for live HTTP services:

Ports to probe: 3000, 3001, 4000, 5000, 5173, 8000, 8080, 8443, 9000
Command: curl -s -o /dev/null -w "%{http_code}" --max-time 2 http://localhost:PORT/
Running if: HTTP status is 200, 301, 302, 403, or 404
Not running if: connection refused or timeout

Also check for HTTPS on ports 443, 8443.

1d. API Specification Detection

Search the project for API specification files:

• OpenAPI: openapi.yaml, openapi.json, openapi.yml
• Swagger: swagger.yaml, swagger.json, swagger.yml
• GraphQL: schema.graphql, *.graphql, *.gql
• RAML: *.raml
• Postman: *.postman_collection.json

1e. Configuration File Detection

Scan for infrastructure and configuration files:

• Containers: Dockerfile, docker-compose.yml, docker-compose.yaml, .dockerignore
• CI/CD: .github/workflows/*.yml, .gitlab-ci.yml, Jenkinsfile, .circleci/config.yml, .travis.yml, bitbucket-pipelines.yml
• Environment: .env, .env.*, .env.local, .env.production
• Cloud IaC: terraform/, *.tf, cloudformation/, pulumi/, cdk.json, serverless.yml
• Security: .helmet, cors.json, csp.json, security middleware configs

1f. Source Structure Analysis

Identify the project's code organization:

• Route/controller files (where HTTP handlers are defined)
• Middleware files (authentication, authorization, validation)
• Model/schema files (data structures, database schemas)
• Configuration files (app settings, database connections)
• Test files (existing security tests)

Step 2: Skill Selection

Based on the detection results from Step 1, determine which sub-skills are applicable:

Before proceeding, display the detection results and selected skills to the user:

## Project Detection Results

| Attribute | Detected |
|-----------|----------|
| Language(s) | {languages} |
| Framework(s) | {frameworks} |
| Package Manager | {package_manager} |
| Running Services | {ports or "None detected"} |
| API Specifications | {specs or "None found"} |
| Config Files | {list of config files found} |
| Source Structure | {summary of code organization} |

## Skills Selected for Execution

| # | Skill | Reason |
|---|-------|--------|
| 1 | secrets-scanner | Always runs |
| 2 | llm-sast-scanner | {language} source code detected |
| ... | ... | ... |

Use the AskUserQuestion tool to confirm the detection results and skill selection with the user before proceeding to Step 3.

Step 3: Sequential Skill Execution

Run selected skills in this optimal order (fastest and most critical first):

1. secrets-scanner — Fast scan, catches critical credential leaks immediately
2. llm-sast-scanner — Comprehensive source code analysis across all vulnerability classes
3. dependency-audit — Check packages against CVE databases
4. security-config-review — Review infrastructure and framework configurations
5. api-contract-testing — Validate API behavior against specifications (if spec found or server running)
6. dast-scanner — Active probing against running endpoints (most time-intensive, requires live server)
7. compliance-mapper — Aggregate all findings and map to compliance frameworks (always runs last)

Execution process for each skill:

1. Use the Read tool to load the skill's SKILL.md file from .claude/skills/{skill-name}/SKILL.md
2. Follow the workflow defined in that skill's SKILL.md
3. Collect all findings in a structured format (ID, title, severity, location, evidence, remediation)
4. Track execution status: completed, skipped (condition not met), or failed (error during execution)
5. Note the finding count per severity level

Between skills, provide a brief progress update:

[2/7] SAST Scanner complete — found X findings (C critical, H high, M medium, L low)
[3/7] Starting Dependency Audit...

Step 4: Findings Aggregation

After all skills have executed, aggregate and normalize the findings.

4a. Deduplication

Apply deduplication rules (see references/report_format.md for full logic):

• Same CWE + same file:line from different skills → merge into single finding, note all source skills
• Same vulnerability type at different locations → keep separate (each is independently exploitable)
• SAST finding + DAST confirmation of same issue → merge, mark confidence as "Confirmed"
• Dependency CVE + SAST confirming vulnerable function usage → merge, note "Vulnerable function confirmed in use"

4b. Correlation and Confidence

Boost confidence when multiple skills corroborate:

• Single skill finding → confidence: "Likely"
• Two skills confirming → confidence: "Confirmed"
• SAST + DAST confirming → confidence: "Confirmed (static + dynamic)"

4c. Severity Normalization

Ensure consistent severity across all skills:

• Use a unified Critical / High / Medium / Low / Info scale
• Apply severity escalation: if 3+ High findings exist, escalate overall risk to Critical
• Corroborated findings (multi-skill confirmation) → increase severity by one level (up to Critical)

4d. Counting

Tally final deduplicated findings by severity:

• Total Critical, High, Medium, Low, Informational
• Determine overall risk rating based on the highest individual finding severity (with escalation rules)

Step 5: Unified Report Generation

Produce a single comprehensive report following the structure in references/report_format.md.

Section 1: Executive Summary

# Security Audit Report

## Executive Summary

| Attribute | Value |
|-----------|-------|
| **Project** | {name} |
| **Stack** | {language} / {framework} |
| **Scan Date** | {date} |
| **Skills Executed** | {count} of {total available} |
| **Overall Risk** | {Critical/High/Medium/Low} |

### Finding Totals

| Critical | High | Medium | Low | Info |
|----------|------|--------|-----|------|
| X | Y | Z | W | V |

### Top 3 Critical Issues

1. **SEQ-001**: {one-line description} — {location}
2. **SEQ-002**: {one-line description} — {location}
3. **SEQ-003**: {one-line description} — {location}

Section 2: Detection Summary

Table showing what was detected and which skills were run, including any skills that were skipped and why.

Section 3: Findings (grouped by severity)

For each finding, present:

• ID: SEQ-001, SEQ-002, etc. (sequential across all skills)
• Title: One-line vulnerability description
• Severity: Critical / High / Medium / Low / Info
• Confidence: Confirmed / Likely / Possible
• Source: Which skill(s) identified this finding
• Location: file:line for code issues, endpoint URL for runtime issues
• Evidence: Code snippet, request/response pair, or configuration excerpt
• Impact: What an attacker can achieve by exploiting this vulnerability
• Framework References:
  • OWASP Top 10 category (e.g., A03:2021 Injection)
  • CWE ID (e.g., CWE-89: SQL Injection)
  • NIST 800-53 control (e.g., SI-10: Information Input Validation)
  • PCI DSS requirement (e.g., 6.5.1: Injection Flaws)
• Context-Aware Remediation:
  • Reference the exact file and line where the fix should be applied
  • Use the project's detected language, framework, and library versions
  • Show before/after code using the project's actual patterns and imports
  • If the project contains secure counterparts (e.g., // SECURE VERSION:), reference them
  • Suggest framework-native solutions (e.g., helmet() for Express, csrf_protect for Django)
  • Start with the simplest fix, then mention more comprehensive alternatives
• Effort Estimate: Low / Medium / High

Section 4: Compliance Scorecard

### OWASP Top 10 (2021)

| Category | Status | Finding Count |
|----------|--------|---------------|
| A01: Broken Access Control | {PASS/FAIL/N/A} | X |
| A02: Cryptographic Failures | {PASS/FAIL/N/A} | X |
| ... | ... | ... |

### CWE Top 25

Matched: X of 25 most dangerous software weaknesses

### NIST 800-53

| Control Family | Findings |
|----------------|----------|
| AC (Access Control) | X |
| ... | ... |

### PCI DSS v4.0

| Requirement | Status |
|-------------|--------|
| 6.2.4: Software engineering techniques | {PASS/FAIL/N/A} |
| ... | ... |

Section 5: Remediation Roadmap

Prioritize fixes by impact and effort:

List each finding under its priority tier with the specific remediation action.

Section 6: Gap Analysis

Document what was NOT tested and why:

• Skills that were skipped (e.g., "DAST scanner skipped — no running server detected on probed ports")
• Vulnerability classes not fully covered (e.g., "Business logic flaws require manual review")
• Recommended follow-up actions:
  • Start the application server and re-run with DAST enabled
  • Add API specification files for contract testing
  • Perform manual penetration testing for logic flaws
  • Review third-party integrations not covered by automated scanning
  • After applying fixes, run /remediation-validator to confirm vulnerabilities are resolved and detect regressions