adhd
Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming
공급 자산 프로필
코딩 및 개발 Agent
코드 리뷰, 저장소 분석, 테스트, CI, GitHub, DevOps 및 개발 워크플로용 스킬입니다.
시나리오
코딩 Agent
저장소를 이해하고 코드를 수정하며 Pull Request를 검토할 수 있는 코딩 Agent가 필요합니다.
Agent 적합도
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI 또는 맞춤형 Agent에 적합합니다.
설치
준비됨
npx skills add darkroomengineering/cc-settings --skill adhd
유지보수
최신
마지막 푸시 후 2일
위험
검토 필요
Dependency or permission surface needs review
GitHub 품질
42
63/100 품질 · 73/100 신뢰
커버리지 태그
검토 메모
Dependency or permission surface needs review · Permission surface may require sandboxing
Agent 채택 스코어카드
신뢰, 감사, 설치 준비 상태를 한눈에 확인하세요
이 점수는 공개 저장소 메타데이터, OpenAgentSkill 검토 신호, 유지보수 최신성, 설치 준비 상태를 결합합니다. 후보 선정 신호일 뿐, 사람의 검토를 대체하지 않습니다.
품질
유망유용한 후보이지만 채택 전에 대안과 비교하세요.
신뢰
샌드박스 전용신뢰 신호가 부족하거나 혼재된 유용한 후보입니다. 결과 루프가 작업 적합성을 입증할 때까지 격리된 작업 공간에서 사용하세요.
감사
검토 필요설치 준비 상태, 보안 메타데이터, 유지보수 및 채택 위험에 대한 기계 판독형 검토입니다.
OpenAgentSkill 신뢰 점수 v5
설치 전 사람 검토
실제 작업에 사용하기 전 샌드박스에서만 실행하고 유사 대안과 비교하세요.
스타
GitHub 스타 42
저장소 활동
스타 42, 포크 3
유지보수
마지막 푸시 후 2일
라이선스
MIT
설치
npx skills add darkroomengineering/cc-settings --skill adhd
설치 안전성
표준 패키지 또는 런타임 설치 경로
권한 범위
shell or command execution, network or browser access
Agent 결과
아직 Agent 결과 데이터가 없습니다
문서
README/SKILL.md 맥락이 충분합니다
위험 요약
프로덕션 전 검토
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
설치 준비 상태
설치 경로 사용 가능
- 설치 경로를 사용할 수 있습니다
- 저장소 근거를 사용할 수 있습니다
- 라이선스가 명시되었습니다
- 아직 Agent 검증 결과 근거가 없습니다
Agent 읽기용 메타데이터
이 스킬의 기계 판독형 의사결정 데이터.
이 블록 또는 포함된 JSON을 사용해 Agent가 이 스킬을 설치할지, 대안을 고를지, 먼저 사람의 검토를 요청할지 판단할 수 있습니다.
적합한 작업
- 코딩 Agent 워크플로
- Claude Code 팀
- builders willing to evaluate younger projects
- Inspect source files
적합한 Agent
설치 결정
- 명령어
- npx skills add darkroomengineering/cc-settings --skill adhd
- 정책
- 검토
- 사람 검토
- 예
신뢰와 위험
- 신뢰
- 65/100
- 감사
- 77/100
- 위험 수준
- 검토 필요
결과 루프
- 엔드포인트
- /api/agent/outcome
- 이벤트 ID
- resolve
- 결과
- 5
사용하지 말아야 할 경우
- 벤더 지원 SLA가 필요한 팀
- production agents without a repository review
- Low GitHub adoption signal
- 고위험 권한 힌트: Shell 또는 명령 실행
- Dependency or permission surface needs review
대체 스킬
Frontend Design
170.9K 스타
npx skills add anthropics/skills --skill frontend-design
대체 스킬
Taste Skill: Anti-Slop Frontend
79.0K 스타
npx skills add Leonxlnx/taste-skill --skill design-taste-frontend
대체 스킬
Canvas Design
170.9K 스타
npx skills add anthropics/skills --skill canvas-design
대체 스킬
Anthropic Brand Guidelines
170.9K 스타
npx skills add anthropics/skills --skill brand-guidelines
Agent 안전 v2
45/100 · 자동 설치 피하기
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
높음
Shell 또는 명령 실행
Skill 메타데이터가 터미널, CLI, Shell, 하위 프로세스 또는 명령 실행 워크플로를 참조합니다.
중간
Browser automation
Skill may drive a browser or interact with web pages.
중간
네트워크 접근
Skill은 원격 페이지, API, 저장소 또는 외부 서비스에 접근할 수 있습니다.
중간
데이터베이스 접근
Skill은 스키마를 검사하고 데이터베이스를 질의하거나 영구 저장소를 다룰 수 있습니다.
- 고위험 권한 힌트: Shell 또는 명령 실행
- Dependency or permission surface needs review
설치 대상
Agent 워크플로에 이 스킬 설치
공개 설치 엔드포인트에서 명령어, 안전 체크리스트, 대상 프롬프트와 정규 링크를 가져옵니다.
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install darkroomengineering-adhdAgent 해결 계획
설치 전에 Agent가 적합성을 검증하게 하세요.
Resolve API는 최우선 스킬, 대안, 안전 정책, 감사 메모, 설치 대상 및 Agent가 페이지를 스크래핑하지 않고 사용할 수 있는 프롬프트를 반환합니다.
JSON 열기
/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 텍스트
/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
설치 핸드오프
/api/skills/darkroomengineering-adhd/install
Agent가 확인할 항목
- Resolve API에서 작업 적합도와 대안을 확인합니다.
- 감사 점수, 신뢰 점수 및 안전 정책 경고를 확인합니다.
- Codex, Claude Code, Cursor 또는 CLI의 설치 대상 호환성을 확인합니다.
프롬프트 복사
Task: Use adhd in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20adhd%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/darkroomengineering-adhd/install
Install command: npx skills add darkroomengineering/cc-settings --skill adhd
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 핸드오프
또 다른 디렉터리 페이지 대신 설치 경로를 Agent에게 제공합니다.
공개 설치 엔드포인트에서 명령어, 안전 체크리스트, 대상 프롬프트와 정규 링크를 가져옵니다.
설치 핸드오프
/api/skills/darkroomengineering-adhd/install
LLM 텍스트 형식
/api/skills/darkroomengineering-adhd/install?format=text
대안 찾기
/api/skills/search?q=adhd&limit=3
Agent 프롬프트
Use adhd for this task. Review https://www.openagentskill.com/api/skills/darkroomengineering-adhd/install, then install with: npx skills add darkroomengineering/cc-settings --skill adhdRegistry 메타데이터
자동 스킬 선택을 위한 Agent 읽기용 프로필.
Registry API를 통해 동일한 결정, 신뢰, 감사, 사용 사례, 설치 신호를 제공하므로 Agent가 UI를 스크래핑하지 않고도 순위를 매길 수 있습니다.
Agent 결정 패널
Fallback candidate for Coding agents
먼저 이 스킬로 프로토타입을 만들고 대체 후보를 준비하세요.
스택 내 역할
대체 후보
주요 적합도
코딩 Agent
신뢰 라벨
먼저 프로토타입
설치 경로
명령어 준비됨
사용 시점
- 코딩 Agent 워크플로
- Claude Code 팀
- builders willing to evaluate younger projects
근거
- 최근 저장소 활동
- 설치 명령 또는 GitHub 저장소를 사용할 수 있습니다
- 품질 프로필 63/100
- OpenAgentSkill 상호작용 3건
먼저 검토
- Low GitHub adoption signal
구현 경로
- 1샌드박스 Agent에 설치하고 코딩 Agent 작업을 처음부터 끝까지 한 번 실행하세요.
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
신뢰 프로필
샌드박스 전용
신뢰 신호가 부족하거나 혼재된 유용한 후보입니다. 결과 루프가 작업 적합성을 입증할 때까지 격리된 작업 공간에서 사용하세요.
GitHub 채택도
확인GitHub 스타 42
스타/포크 활동
확인스타 42, 포크 3; 현재 메타데이터에서 이슈 활동을 확인할 수 없습니다
최근 유지보수
통과마지막 푸시 후 2일
라이선스 명확성
통과MIT
긍정 신호
- AI 검토 승인됨
- 설치 경로를 사용할 수 있습니다
- 저장소 근거를 사용할 수 있습니다
- 최근 유지보수된 저장소
- 설치 명령에서 뚜렷한 고위험 패턴이 발견되지 않았습니다
- 결과 루프는 준비되었지만 첫 실제 Agent 실행이 필요합니다
설치 전 검토
- Financial research output is not financial advice; require human review before any live investment decision.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: shell or command execution, network or browser access
- GitHub adoption: 42 GitHub stars
- Stars/forks activity: 42 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, network or browser surface
- Permission surface: shell or command execution, network or browser access
- 아직 실제 Agent 결과 보고서가 없습니다
- 무인 설치 전에 사람 검토가 필요합니다
권장 작업
실제 작업에 사용하기 전 샌드박스에서만 실행하고 유사 대안과 비교하세요.
품질 프로필
유망 Agent 워크플로용 후보
유용한 후보이지만 채택 전에 대안과 비교하세요.
워크플로 적합도
이 스킬을 사용할 시나리오
Build and ship code
Coding agents
I need a coding agent that can understand a repository, edit code, and review pull requests.
Manage repositories
GitHub automation
I need my agent to triage GitHub issues, review pull requests, and summarize repository changes.
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
워크플로 적합도
완전한 워크플로에 추가
Inspect, patch, and verify code
Coding review agent
A workflow for software agents that inspect repositories, review pull requests, generate tests, and turn findings into shippable patches.
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
대안 후보
설치 전 비교
이 작업에 적합할 수 있는 유사 스킬입니다.
Frontend Design
Guidance for distinctive, intentional UI design, typography, visual direction, and non-template-like product interfaces.
Taste Skill: Anti-Slop Frontend
Design and implementation guidance for distinctive landing pages, portfolios, product demos, and purposeful redesigns.
Canvas Design
Create original visual art, posters, PNG assets, and PDF documents through a clear design philosophy.
Anthropic Brand Guidelines
Apply Anthropic official brand colors, typography, and visual standards to appropriate Anthropic-related artifacts.
개요
--- name: adhd argument-hint: "[problem]" description: Parallel divergent ideation — spawns N isolated generator agents under different cognitive frames (regulator, biology, speedrunner, 10-year-old, zero-budget), then a critic pass scores, clusters, prunes traps, and deepens the top 3. Use for open-ended design, architecture, naming, API/SDK surface, and fuzzy debugging where the obvious answer is expensive to get wrong. Triggers "/adhd", "adhd mode", "brainstorm", "ideate", "widen the option space", "divergent ideas", "we keep landing on the same idea". Skip for lookups, syntax, bugs with a known root cause, or closed phrasing ("quick", "standard", "canonical", "textbook"). Use /oracle compare to evaluate options you already have — adhd generates the option space; use /plan-ceo-review to challenge whether to build at all. context: main license: MIT ---
# ADHD
Stop picking the textbook answer. The first three answers the model would give are the answers a senior engineer would give in thirty seconds. Correct. Forgettable. The interesting answers live past number three, in the awkward middle nobody walks into. This skill makes the model walk there.
## When to use vs siblings
- `/adhd` — **generate** the option space when you don't have candidates yet. - `/oracle` (compare mode) — **evaluate** options you already have. - `/plan-ceo-review` — challenge whether the thing should be built at all. - `/verify` — adversarially check a conclusion you've already reached.
They compose: `/adhd` to widen, `/oracle` to weigh the shortlist.
## Pre-flight (run before Phase 1)
This skill is expensive. About 10 Agent calls, 30 to 90 seconds wall clock, 5 to 10x a single answer. Do not pay that cost when a direct answer is better. Run this gate before Phase 1.
**Step 1. Explicit invocation check.**
If the user typed `/adhd` or explicitly asked for ADHD mode, "use the adhd skill", or "run ADHD on this", **SKIP the rest of this section and go straight to Phase 1**. The user opted in. Do not second-guess.
**Step 2. Self-judge (only if Step 1 did not match).**
Ask yourself three questions. If the answer to any is no, ABORT.
1. **Open-ended?** Would a senior engineer give multiple viable answers here, or is there one canonical answer? If canonical, abort. 2. **High-stakes?** Is the cost of the obvious answer being wrong actually high? Architecture decisions, public API surfaces, naming a real product, fuzzy bugs with no known root cause, schema design = yes. Side project at 11pm = no. 3. **Open phrasing?** Did the user avoid words like "quick", "standard", "canonical", "textbook", "just", "one-line"? If they used any of those, they want the direct answer. Abort.
If all three checks pass, proceed to Phase 1.
If any fails, ABORT and answer the question directly. Optionally append one sentence: *"If you want a wider exploration under parallel cognitive frames with explicit trap detection, run `/adhd <your problem>`."*
## The loop
Two strict phases. Mixing them kills idea quality, because the critic strangles the generator.
### Phase 1 — Diverge (no critic)
For the problem P:
1. Pick 5 cognitive frames from the table below. Bias toward engineering tags when the problem is code-shaped. Always include at least one wild frame to keep range.
2. Spawn 5 **parallel** Agent tool calls in ONE message. One per frame. Each Agent gets only: - the problem P - any context the user provided - the chosen frame's vantage prompt - a system instruction that forbids evaluation
The exact instruction to give each Agent:
> You are in DIVERGENT mode. You are a generator, not a critic. > Generate 6 short distinct ideas under this frame. Each idea is one > phrase or one sentence. Do not evaluate. Do not rank. Do not hedge. > The first three obvious answers everyone would give are banned. > Push past them into the awkward middle. > Output a JSON array only. No prose before or after. > `[{"text": "...", "rationale": "..."}, ...]`
3. **Critical invariant.** The Agent calls must be parallel and isolated. Do NOT serialize them. Do NOT pass one branch's output as context to another. Branches that see each other anchor each other and the whole method collapses to a wider single thought.
### Phase 2 — Focus (critic on)
After all branches return:
1. **Score.** Rate each idea on three axes 0 to 10: novelty (distance from the obvious default), viability (could it actually ship), fit (does it address the stated problem). For any idea that looks attractive but is a trap (hidden cost, false economy, will not scale, premature abstraction), flag it with a one-line reason.
2. **Cluster.** Group ideas into 3 to 6 clusters by their underlying angle, not by surface keywords. Label clusters by angle: "remove the server plays", "cache-shaped plays", "batched-window plays", "race-multiple- backends plays".
3. **Deepen the top 3.** Rank by weighted score (novelty 0.35 + viability 0.40 + fit 0.25), exclude traps, take top 3. For each, spawn one Agent call that produces: - a 4 to 8 sentence sketch of how the idea works - the load-bearing risk - the first concrete step a builder would take - 3 to 5 child ideas (variations, hybrids, unlocks)
Deepen Agent instruction:
> You are in FOCUS mode. Take one promising idea and connect dots. > Sketch how it would actually work in 4 to 8 sentences. Name the > load-bearing risk. Name the first concrete step a coder would take. > Then generate 3 to 5 sub-ideas that branch off (variations, > combinations with other domains, things this unlocks). > Output JSON only.
Scoring, clustering, and the final synthesis stay in the main session — that is judgment work and belongs on the session's top-tier model. The generator and deepen agents are fan-out subagents and inherit `CLAUDE_CODE_SUBAGENT_MODEL` (Sonnet), which fits the quota doctrine: roomy pools carry volume, the scarce pool does the judging.
## Frames
Pick 5 per run.
| Frame | Vantage prompt | Tags | |---|---|---| | **hardware engineer** | You think in latency, memory layout, and physical constraints. Re-ask this as a hardware/firmware problem. What does the bus topology, cache, timing budget tell you? | code, wild | | **regulator** | You audit systems for compliance and failure modes. What must be provable, traceable, or refusable here? | design, general | | **10-year-old** | You are a curious 10 year old who has never seen software. Describe naive but unencumbered approaches. Ignore convention. | general, wild | | **competitor trying to break it** | You are a hostile competitor or attacker. Generate approaches that exploit, fail, or sabotage the obvious solution. Then invert into ideas. | code, design | | **biology** | Transplant a mechanism from biology (immune systems, neural plasticity, cell signaling, evolution, gut flora). Force-fit it onto this engineering problem. | code, wild | | **logistics** | Steal mechanisms from logistics: queues, batching, just-in-time, hub-and-spoke, returns, last-mile. Apply them literally. | code, design | | **game design** | Approach this as a game designer. What are the loops, rewards, friction, save-states, speedrun tricks? Treat the user as a player. | design, general | | **markets** | Treat the problem as a market. Buyers, sellers, market-makers. What does an auction, a futures contract, a clearing house look like here? | design, wild | | **inversion** | Ask the OPPOSITE question. If goal is X, brainstorm how to guarantee NOT X. Then negate each answer back. | code, design, general | | **extreme: $0 budget, 1 hour** | No money, no team, one hour. What is the crudest version that still does the load-bearing thing? | code, general | | **extreme: infinite budget, 10 years** | Infinite compute, infinite engineers, a decade. What is the maximalist version? | design, wild | | **remove the load-bearing assumption** | Name the thing everyone treats as fixed (framework, database, request-response model, network). Imagine it is gone. What is possible? | code, design, wild | | **speedrunner** | You are a speedrunner. Find glitches, skips, out-of-bounds tricks, frame-perfect shortcuts. What is the abusive-but-legal path? | code, wild | | **ant colony** | No central planner. Many dumb agents, local rules, pheromone trails. How does the problem solve itself emergently? | code, wild | | **3am on-call** | You are the on-call engineer woken at 3am when this breaks. What design would let you not get paged? | code, design |
### Picking frames
For code-shaped problems: pick 4 frames tagged `code` or `design`, plus 1 tagged `wild`. For open product or strategy problems: a mix from all tags. Vary the picks across sessions so the same problem produces different candidate sets when re-run.
## Output shape
After Phase 2, render in this order. Do not collapse it into a wall of prose. The structure is the point.
1. **Brief.** One or two lines confirming the problem and any reframe used. 2. **Wide set.** Full pool grouped by cluster. Each cluster labeled by underlying angle. Each idea is one short phrase. Show score chips like `[N7 V8 F9]` next to each. 3. **Converge.** A 2 to 4 idea shortlist. State why each is on the list. Mark the non-obvious-but-viable pick explicitly with ★. List traps separately, each with the one-line reason it is a trap. 4. **Focus.** The 3 deepened branches. For each: the sketch, the load- bearing risk, the first concrete step, and the child ideas. 5. **Provocation.** One wildcard question or idea that opens a new direction the user can push into if nothing landed.
## Anti-patterns
These are how this skill goes wrong. Watch for them.
- **Convergence disguised as divergence.** Ten minor variations of one idea is not breadth. If every candidate shares the same underlying assumption, you have not diverged. You have decorated. - **Weird-for-weird's-sake with no convergence.** A pile of 30 unsorted absurdities is as useless as one safe answer. Always converge. - **Walls of equally-weighted prose.** Cluster, label, pull out the best. Structure is half the value. - **Refusing to commit.** After diverging, take a position on what is actually promising. "Here are 20 ideas, you decide" is a cop-out. Generate wide, but converge with a real opinion. - **Skipping the isolation invariant.** If you simulate parallel branches by writing them sequentially in one context, you have not done ADHD. You have done a wider single thought. The Agent tool gives each branch a fresh context. Use it.
## Calibration
- **How many ideas?** Scale to stakes. Quick "name this function" = 3 frames × 4 ideas. "How should I position this product" = 5 frames × 8 ideas. Default is 5 × 6 = 30. - **How weird?** Read the room. Serious strategy work: flag the wild cards clearly so they do not read as unserious. Open brainstorming or play: let it run loose. Absurd ideas earn their place by seeding viable ones. - **When to stop diverging?** Stop when new candidates start repeating the shape of existing ones. The space is mapped. Do not pad to hit a number.
## Cost
5 diverge + 1 score + 1 cluster + 3 deepen ≈ 10 Agent calls per run. About 5 to 10x a single-shot answer. Not for every keystroke. For decision points where the cost of the obvious answer is high. Diverge/deepen agents run on the Sonnet subagent pool, so the Opus/Fable cost of a run is one synthesis pass.
## Attribution
Ported from [`UditAkhourii/adhd`](https://github.com/UditAkhourii/adhd) (MIT). Upstream ships the same loop as an npm CLI (`adhd-agent`) plus evals and a source spec on divergent ideation; this port keeps the skill-only form (no install) and adds the cc-settings sibling-skill routing and subagent model notes.
기술 세부 사항
- 버전
- 1.0.0
- 라이선스
- MIT
- 최근 업데이트
- 2026년 8월 20일
- 게시일
- 2026년 8월 20일
결정 스냅샷
대체 후보
최근 저장소 활동
Agent 검증 증거
Agent 검증 증거
Resolve, 검토, 설치 및 한 번의 제한된 실행 후 결과 보고서입니다.
- 성공률
- —
- 최근 실패
- —
- 결과
- 0
- 출력 품질
- —
- 실패
- 0
- 관련 없음
- 0
- 설치
- 0
- 위험 차단
- 0
- 설정 필요
- 0
- 프로덕션
- 0
아직 Agent 결과 데이터가 없습니다. 첫 실행은 /api/agent/outcome을 통해 성공, 설정 필요, 위험 차단, 실패 또는 비관련 결과를 보고할 수 있습니다.
성장 루프
공유 키트
adhd용 시나리오 기반 초안입니다. X에 수동으로 게시할 수 있습니다.
adhd: Parallel divergent ideation — spawns N isolated generator agents under different cognitive fr... 42 stars https://www.openagentskill.com/skills/darkroomengineering-adhd?ref=x
선택 사항: 설치 명령이 포함된 답글
Listing + install path for adhd: https://www.openagentskill.com/skills/darkroomengineering-adhd?ref=x Install: npx skills add darkroomengineering/cc-settings --skill adhd
등록 출처
Registry 색인
이 등록은 공개 소스에서 색인되었으며 유지보수자 소유권 주장이 승인될 때까지 공식으로 표시되지 않습니다.
- 색인 주체
- OpenAgentSkill 커뮤니티 인덱스
귀속은 공개 저장소 또는 제작자 프로필에 연결됩니다. 제작자는 등록을 주장하여 소유권 신호를 업데이트할 수 있습니다.
이 스킬 소유권 주장소유자 소유권 주장
이 스킬 등록 소유권 주장
이 Registry 색인 등록은 darkroomengineering에게 귀속되어 있지만 아직 공식으로 표시되지 않았습니다. 소유권을 주장하면 확인된 소유자 신호가 추가되어 이후 출시, 설치 및 감사 업데이트를 더 신뢰할 수 있습니다.
크리에이터 백링크 키트
README에 증거 배지 추가
개발자가 저장소를 평가하는 위치에 정규 등록, 현재 신뢰 및 감사 신호, 실제 Agent-Proven 증거를 표시합니다.
[](https://www.openagentskill.com/skills/darkroomengineering-adhd)
[](https://www.openagentskill.com/skills/darkroomengineering-adhd)
[](https://www.openagentskill.com/skills/darkroomengineering-adhd/audit)
[](https://www.openagentskill.com/skills/darkroomengineering-adhd)작성자
darkroomengineering
@darkroomengineering
플랫폼 적합도
상태 신호
- GitHub 스타
- 42
- 품질 점수
- 35/100
- 최근 GitHub 푸시
- 2026년 8월 20일
- 프레임워크 힌트
- 알 수 없음
- OpenAgentSkill 조회수
- 3
- 설치 명령 복사
- 0
- 외부 클릭
- 0
커뮤니티 신호
이 스킬이 Agent 워크플로에 유용한지 알려 주세요. 집계된 피드백은 시간이 지날수록 순위를 개선합니다.
신뢰와 안전
샌드박스 전용
- GitHub 채택도GitHub 스타 42확인
- 스타/포크 활동스타 42, 포크 3; 현재 메타데이터에서 이슈 활동을 확인할 수 없습니다확인
- 최근 유지보수마지막 푸시 후 2일통과
- 라이선스 명확성MIT통과
- README/SKILL.md 완성도메타데이터에 충분한 사용 및 워크플로 맥락이 포함되어 있습니다통과
- 의존성/런타임 위험command execution surface, network or browser surface확인
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