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.
npx skills add Insajin/autopus-adk --skill dddgit clone --depth 1 https://github.com/Insajin/autopus-adkWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/insajin/autopus-adk/ddd)<a href="https://agentmods.dev/skills/insajin/autopus-adk/ddd"><img src="https://agentmods.dev/badge/skills/insajin/autopus-adk/ddd.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.00615 |
| Opus 5 | $0.00009 | $0.00308 |
| Sonnet 5 | $0.00003 | $0.00123 |
| Haiku 4.5 | $0.00002 | $0.00061 |
Grade A, and why
ddd scanned grade A with 0 findings 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 8d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
DDD (Disciplined Design Development) Skill
기존 코드의 동작을 분석하고 보존하면서 점진적으로 개선하는 방법론입니다.
ANALYZE-PRESERVE-IMPROVE 사이클
ANALYZE 단계: 기존 동작 분석
변경 전에 반드시 현재 동작을 완전히 이해합니다:
1. 코드 목적 파악 — 무엇을 하는 코드인가?
2. 호출자 파악 — 누가 이 코드를 사용하는가? (fan_in)
3. 사이드 이펙트 식별 — 어떤 부수 효과가 있는가?
4. 테스트 현황 파악 — 어떤 테스트가 존재하는가?
5. 경계 조건 파악 — 어떤 엣지 케이스가 있는가?
도구 사용:
git log --follow -p [file]— 변경 이력 확인grep -r "[function_name]"— 호출자 검색go test -run [test_pattern] -v— 기존 테스트 실행
PRESERVE 단계: 기존 동작 보존
기존 동작을 테스트로 고정합니다 (Characterization Tests):
// 특성 테스트: 현재 동작을 그대로 문서화
func TestLegacyBehavior_CharacterizationTest(t *testing.T) {
// 이 테스트는 현재 동작을 문서화한다
// 동작이 "올바른지" 여부와 관계없이 현재 상태를 기록
result := legacyFunction(existingInput)
assert.Equal(t, knownOutput, result)
}
금지 사항:
- 테스트 없이 인터페이스 변경 금지
- 기존 함수 시그니처 변경 금지 (새 함수 추가는 허용)
- 사이드 이펙트 제거 전 의존 코드 확인 필수
IMPROVE 단계: 점진적 개선
작은 단계로 나누어 개선합니다:
1. 최대 변환 크기: small (50줄 미만)
2. 각 변환 후 테스트 실행
3. 기존 동작 유지 확인
4. 리팩토링과 기능 변경 분리
적용 패턴
Strangler Fig Pattern
기존 코드를 점진적으로 대체:
- 새 구현체 병행 운영
- 새 구현체로 트래픽 이전
- 구 구현체 제거
Branch by Abstraction
인터페이스로 추상화하여 교체:
- 인터페이스 추출
- 신규 구현체 작성
- 의존성 주입으로 교체
완료 기준
- 기존 테스트 모두 통과
- 특성 테스트 추가됨
- 변경 전후 동작 동일
- fan_in >= 3 함수에 @AX:ANCHOR 태그
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.
- 8d ago First seen · 80 lines · 17 tokens per session scan A 4fb164ed7df9
ddd is a skill published in the GitHub repository Insajin/autopus-adk (110 stars, last pushed yesterday), licensed MIT. It adds 17 tokens to every session and 615 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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