ch015-va

A read-only source-code security assessment workflow based on eight security architecture areas. It checks how security controls are implemented, what is missing or unverified, and how findings affect the system.

In plain words
What is it for?
Use it to assess a project's security architecture, vulnerabilities, compliance coverage, evidence, and remediation impact without changing code.
Why use it?
It helps identify structural security gaps that a short list of known code patterns may miss, while separating confirmed controls from assumptions.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/ch015/code-pentester/va
Any agent
npx skills add ch015/code-pentester --skill va
Clone the repo
git clone --depth 1 https://github.com/ch015/code-pentester

Made for: Claude Code, Codex.

Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 19,168 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00037 $0.19168
Opus 5 $0.00018 $0.09584
Sonnet 5 $0.00007 $0.03834
Haiku 4.5 $0.00004 $0.01917

Measured 2d ago against content hash 9548bfb02776, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

ch015-va scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

- "curl -X POST /api/comments로 WAF 차단 여부 확인"
skills/ch015/offsec/va/SKILL.md · 1,353 lines

How it starts

The opening of the file, as written. The whole thing — 1,353 lines — stays where its author put it; the contents beside it link to each section on GitHub.

취약점 진단 (Vulnerability Assessment) Skill

대상 프로젝트의 소스코드를 아키텍처 차원 기반으로 체계적으로 점검하여 보안 메커니즘의 구현 현황과 미적용 영역을 식별합니다.


서비스 개요

항목 내용
서비스명 취약점 진단 (VA)
방법론 아키텍처 차원 기반 + 방법론 중심 분석
출력물 아키텍처 차원 건강도 + 취약점 목록 + 6-step 영향도 분석 + 표준 준수 현황
코드 수정 ❌ 없음 (읽기 전용)

설계 원칙: 아키텍처 차원 + 방법론 기반 분석

OWASP/CWE는 보고서의 참조 태깅으로만 사용합니다. 분석의 조직 원리는 8대 보안 아키텍처 차원입니다.

분석 원칙: 방법론, 패턴이 아님

Principles:
  No_Pattern_Lists: |
    "특정 프레임워크 구문, grep 패턴, 시크릿 접두사 목록에 의존하지 않는다.
     이런 목록은 닫힌 집합이며, 목록에 없는 위험을 구조적으로 놓친다."

  Use_Methodology: |
    "행위(무엇이 일어나는가)를 질문하고, AI가 감지된 기술 스택에 맞는
     구체적 코드 패턴을 자율적으로 판단하여 탐색한다.
     예: '사용자 입력이 문자열 연결로 쿼리에 삽입되는가?'라고 질문하되
     어떤 언어 구문을 찾을지는 AI가 판단."

  Representative_Examples: |
    "안티패턴/올바른 패턴은 '대표적 예시'로만 제시.
     분석은 이 예시에 한정되지 않으며 — 동일한 구조적 이슈를 가진
     모든 변형이 탐지되어야 한다."

  No_Library_Assumptions: |
    "라이브러리의 존재, 이름, 일반적 평판으로 보안 속성을 추론하지 않는다.
     '라이브러리가 X를 할 수 있다' ≠ '이 프로젝트에서 X가 작동하고 있다.'
     보안 속성은 프로젝트 코드에서 명시적으로 확인 가능할 때만
     '적용됨(Verified)'으로 판단한다.
     확인 불가능하면 '미확인(Unverified)'으로 보고하고,
     미적용 시 영향을 함께 기술한다."

Phase 구조

Phase 0: Recon              → 공통 정찰 (common/recon.md 참조)
Phase 0.5: Binding          → 정찰 → 분석 컨텍스트 바인딩
Phase 0.8: AST Pre-Analysis → Tree-sitter + Semgrep 구조 분석 (--mode ast 지정 시)
Phase 1: Architecture       → 8대 보안 아키텍처 차원 리뷰
Phase 2: Deep Analysis      → 원칙 기반 심층 분석
Phase 3: Compliance         → 보안 표준 준수 점검
Phase 3.5: Self-Verify      → 분석 결과 자체 검증 (도달 가능성, 보상 제어, 경량 공격 체인)
Phase 4: Scoring            → 보안 점수 산출 (Self-Verify 결과 반영, Self-Verify Gate 통과 필수)
Phase 4.5: Evidence Verify  → file:line 증거 독립 검증 (common/evidence-verification.md)
Phase 4.7: Raw Ledger       → Raw Findings Ledger + Pentest Plan 초안 생성 (Phase 5 진입 게이트)
Phase 5: Report             → 종합 보고서 + 6-step 영향도 분석
Phase 5.1: Index            → Findings Index YAML 생성 (하류 에이전트 컨텍스트 최적화)
Phase 5B: Baseline          → Baseline Snapshot 생성 (이행점검 기준선, full audit 시에만)
Phase 5R: Regulatory        → 규제 영향 참조 (Finding 기반, 해당 시에만)

Read the full file on GitHub · 1,353 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 1,353 lines · 37 tokens per session scan A 9548bfb02776

Subscribe to this mod's changes

ch015-va is a skill published in the GitHub repository ch015/code-pentester (4 stars, last pushed 14d ago), licensed MIT. It adds 37 tokens to every session and 19,168 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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