본문으로 건너뛰기
cccskills
무료GitHub에 공개

security-audit

Comprehensive security audit covering OWASP Top 10, secrets detection, supply chain security, threat modeling, and language-specific vulnerability patterns. Investigates actual code paths rather than grep-matching keywords. Generates a scored SECURITY_AUDIT.md with prioritized remediation. Use when assessing application security, preparing for a security review, or onboarding to a codebase with security concerns.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(6)

  • SKILL.md5.7 KB
  • references/language-security.md3.8 KB
  • references/owasp-patterns.md4.5 KB
  • references/report-template.md3.1 KB
  • references/secrets-and-supply-chain.md2.9 KB
  • references/threat-model.md3.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

<!-- Copyright 2025-2026 Richard Shade. Licensed under Apache-2.0. --> <!-- SPDX-License-Identifier: Apache-2.0 -->

Security Audit

Investigate the codebase for security vulnerabilities across OWASP Top 10 categories, secrets exposure, supply chain risks, and language-specific patterns. Produces a scored SECURITY_AUDIT.md with prioritized remediation actions.

Core principle: Investigate, do not just grep. Read surrounding code to determine if a match is a genuine vulnerability, a false positive, or already mitigated. Only flag findings with specific file:line evidence and confirmed risk.

Prerequisite check

git --version 2>/dev/null && git rev-parse --is-inside-work-tree 2>/dev/null

Check for available security tools (optional but enhance analysis):

command -v gitleaks 2>/dev/null && echo "gitleaks: available"
command -v semgrep 2>/dev/null && echo "semgrep: available"
command -v trivy 2>/dev/null && echo "trivy: available"
command -v govulncheck 2>/dev/null && echo "govulncheck: available"

Proceed without tools — manual investigation still works. Note which tools are missing in the report.

Step 1: Scope and context

Determine what to audit and understand the application:

  1. Detect tech stack — scan for go.mod, package.json, pyproject.toml, Cargo.toml, *.csproj to identify languages
  2. Identify security-sensitive areas — search for files related to authentication, authorization, database access, API endpoints, cryptography, file handling
  3. Determine scope — full codebase audit, specific module, or recent changes only (based on user input)
  4. Read project docs — CLAUDE.md, README.md, CONTEXT.md for architectural context and known boundaries

Step 2: Automated scanning

Run available security tools before manual investigation. Tool findings guide where to focus manual review.

# Secrets in current code and git history
gitleaks detect -v 2>&1

# Static analysis (language-aware rule sets)
semgrep --config=auto --severity=ERROR . 2>&1

# Dependency vulnerabilities
trivy fs --severity HIGH,CRITICAL . 2>&1
# or: govulncheck ./... / npm audit / pip-audit / cargo audit

If tools find issues, verify each one — automated scanners produce false positives. Read the flagged code in context before including in the report.

Step 3: OWASP Top 10 analysis

Systematically investigate each OWASP category relevant to the project. Not all categories apply to all projects — a CLI tool does not need XSS checks; a library does not need CSRF protection.

For each applicable category:

  1. Identify the code paths where this vulnerability could exist
  2. Read the actual implementation (not just grep for keywords)
  3. Check if mitigations are in place
  4. Flag only confirmed or strongly suspected vulnerabilities

For detailed investigation patterns per OWASP category, see references/owasp-patterns.md.

Step 4: Secrets and supply chain

Secrets: Scan current code AND git history for hardcoded credentials, API keys, tokens, and connection strings. A secret removed from current code but present in git history is still compromised — flag as CRITICAL.

Supply chain: Check lockfile integrity, dependency vulnerabilities, dependency confusion risk, and maintenance signals for critical dependencies.

For detailed scanning commands and analysis patterns, see references/secrets-and-supply-chain.md.

Step 5: Threat modeling

Apply STRIDE to the actual codebase:

  1. Map entry points — every way data enters the application
  2. Identify trust boundaries — where untrusted data crosses into trusted zones
  3. Apply STRIDE categories — Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege
  4. Score threats using DREAD (Damage, Reproducibility, Exploitability, Affected users, Discoverability)

For detailed threat modeling steps and DREAD scoring, see references/threat-model.md.

Step 6: Language-specific review

Check for vulnerability patterns specific to the detected languages. Load only the relevant language sections.

For Go, JavaScript/TypeScript, Python, Rust, and .NET patterns, see references/language-security.md.

Step 7: Score and classify findings

For each finding, assign:

  • Severity: CRITICAL / HIGH / MEDIUM / LOW
    • CRITICAL: exploitable now, data breach or RCE risk
    • HIGH: exploitable with moderate effort, significant impact
    • MEDIUM: requires specific conditions, moderate impact
    • LOW: theoretical risk, minimal impact
  • Effort to fix: S (< 1 day) / M (1-5 days) / L (1-2 weeks)
  • Evidence: specific file:line reference and explanation

Step 8: Generate SECURITY_AUDIT.md

Write the report using the template in references/report-template.md. Include executive summary, detailed findings with evidence, threat model summary, and prioritized remediation actions.

Run markdownlint on the generated file.

Step 9: Present summary

Show the user:

  1. Finding counts by severity
  2. Top 3 most critical items with evidence
  3. Tools that were available vs missing
  4. Remediation priority list
  5. Ask which findings to address first

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Prepares Go applications to work effectively with AI coding agents. Use when setting up a new Go project or retrofitting an existing one to ensure: structured JSON logging (slog/Zap/ZeroLog/Logrus), machine-readable command output, thorough golangci-lint configuration, non-interactive CLI design with --yes flags, structured error handling with meaningful exit codes, proper context.Context propagation, graceful shutdown, health check endpoints, and a standardized Makefile. Triggers when a user asks to make their Go app "agent-ready," "AI-friendly," wants to improve agent tooling/observability in a Go project, or needs to audit an existing Go project against agent-readiness best practices.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dirien/yet-another-agent-harness202026년 5월 31일 업데이트

Use when creating or updating AGENTS.md files, .github/copilot-instructions.md, or other AI agent rule files, onboarding AI agents to a project, standardizing agent documentation, or when anyone mentions AGENTS.md, agent rules, project onboarding, or codebase documentation for AI agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dirien/yet-another-agent-harness202026년 5월 31일 업데이트

Use when designing new high-level system architecture, reviewing existing designs, or making architectural decisions. Invoke to create architecture diagrams, write Architecture Decision Records (ADRs), evaluate technology trade-offs, design component interactions, and plan for scalability. Use for system design, architecture review, microservices structuring, ADR authoring, scalability planning, and infrastructure pattern selection — distinct from code-level design patterns or database-only design tasks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dirien/yet-another-agent-harness202026년 5월 31일 업데이트

Use when building CLI tools, implementing argument parsing, or adding interactive prompts. Invoke for parsing flags and subcommands, displaying progress bars and spinners, generating bash/zsh/fish completion scripts, CLI design, shell completions, and cross-platform terminal applications using commander, click, typer, or cobra.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dirien/yet-another-agent-harness202026년 5월 31일 업데이트

Convert an AWS CloudFormation stack or template to Pulumi. This skill MUST be loaded whenever a user requests migration or conversion of CloudFormation to Pulumi.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dirien/yet-another-agent-harness202026년 5월 31일 업데이트

Analyzes code diffs and files to identify bugs, security vulnerabilities (SQL injection, XSS, insecure deserialization), code smells, N+1 queries, naming issues, and architectural concerns, then produces a structured review report with prioritized, actionable feedback. Use when reviewing pull requests, conducting code quality audits, identifying refactoring opportunities, or checking for security issues. Invoke for PR reviews, code quality checks, refactoring suggestions, review code, code quality. Complements specialized skills (security-reviewer, test-master) by providing broad-scope review across correctness, performance, maintainability, and test coverage in a single pass.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

dirien/yet-another-agent-harness202026년 5월 31일 업데이트

dirien의 스킬 모두 보기

이 스킬의 문제 신고