sero-humanize
Audit or edit Sero Markdown documentation and other product prose to remove AI-writing patterns while preserving technical meaning, product terminology, links, examples, and the repository voice. Use when the user asks to humanize, de-slop, tighten, simplify, rewrite, or audit Se
공급 자산 프로필
리서치 및 지식 작업
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
시나리오
리서치 Agent
I need my agent to research a topic, compare sources, and produce a concise report.
Agent 적합도
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI 또는 맞춤형 Agent에 적합합니다.
설치
준비됨
npx skills add sero-labs/sero --skill sero-humanize
유지보수
최신
마지막 푸시 후 3일
위험
검토 필요
Permission surface may require sandboxing
GitHub 품질
19
60/100 품질 · 67/100 신뢰
커버리지 태그
검토 메모
Permission surface may require sandboxing · SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
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
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
감사
검토 필요설치 준비 상태, 보안 메타데이터, 유지보수 및 채택 위험에 대한 기계 판독형 검토입니다.
OpenAgentSkill 신뢰 점수 v5
설치 전 사람 검토
Choose a stronger alternative or inspect the source manually before any install attempt.
스타
GitHub 스타 19
저장소 활동
스타 19, 포크 1
유지보수
마지막 푸시 후 3일
라이선스
Apache-2.0
설치
npx skills add sero-labs/sero --skill sero-humanize
설치 안전성
표준 패키지 또는 런타임 설치 경로
권한 범위
secrets or environment access, shell or command execution
Agent 결과
아직 Agent 결과 데이터가 없습니다
문서
README/SKILL.md 맥락이 충분합니다
위험 요약
프로덕션 전 검토
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
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
- GitHub automation 워크플로
- Claude Code 팀
- builders willing to evaluate younger projects
- Inspect repository metadata
적합한 Agent
설치 결정
- 명령어
- npx skills add sero-labs/sero --skill sero-humanize
- 정책
- 차단
- 사람 검토
- 예
신뢰와 위험
- 신뢰
- 59/100
- 감사
- 74/100
- 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
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
- 고위험 권한 힌트: Shell or command execution, Secrets or environment access
Agent 안전 v2
34/100 · 자동 설치 피하기
This skill should not be selected by an agent without explicit human security review.
Do not auto-install. Inspect the source, dependencies, and permission surface first.
높음
Shell 또는 명령 실행
Skill 메타데이터가 터미널, CLI, Shell, 하위 프로세스 또는 명령 실행 워크플로를 참조합니다.
중간
네트워크 접근
Skill은 원격 페이지, API, 저장소 또는 외부 서비스에 접근할 수 있습니다.
중간
파일 시스템 접근
Skill은 프로젝트 파일, 문서, 생성 산출물 또는 로컬 작업 공간 상태를 읽거나 쓸 수 있습니다.
높음
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- 고위험 권한 힌트: Shell or command execution, Secrets or environment access
- Permission surface may require sandboxing
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 sero-labs-sero-humanizeAgent 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%20sero-humanize%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 텍스트
/api/agent/resolve?task=Use%20sero-humanize%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
설치 핸드오프
/api/skills/sero-labs-sero-humanize/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 sero-humanize in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20sero-humanize%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/sero-labs-sero-humanize/install
Install command: npx skills add sero-labs/sero --skill sero-humanize
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/sero-labs-sero-humanize/install
LLM 텍스트 형식
/api/skills/sero-labs-sero-humanize/install?format=text
대안 찾기
/api/skills/search?q=sero-humanize&limit=3
Agent 프롬프트
Use sero-humanize for this task. Review https://www.openagentskill.com/api/skills/sero-labs-sero-humanize/install, then install with: npx skills add sero-labs/sero --skill sero-humanizeRegistry 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 GitHub automation
먼저 이 스킬로 프로토타입을 만들고 대체 후보를 준비하세요.
스택 내 역할
대체 후보
주요 적합도
GitHub automation
신뢰 라벨
먼저 프로토타입
설치 경로
명령어 준비됨
사용 시점
- GitHub automation 워크플로
- Claude Code 팀
- builders willing to evaluate younger projects
근거
- 최근 저장소 활동
- 설치 명령 또는 GitHub 저장소를 사용할 수 있습니다
- 품질 프로필 60/100
- OpenAgentSkill 상호작용 6건
먼저 검토
- Low GitHub adoption signal
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
구현 경로
- 1샌드박스 Agent에 설치하고 GitHub automation 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
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub 채택도
수정GitHub 스타 19
스타/포크 활동
수정스타 19, 포크 1; 현재 메타데이터에서 이슈 활동을 확인할 수 없습니다
최근 유지보수
통과마지막 푸시 후 3일
라이선스 명확성
통과Apache-2.0
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
- SKILL.md excerpt is truncated in the provided documentation, but the visible portion is thorough and well-structured.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 19 GitHub stars
- Stars/forks activity: 19 stars, 1 forks; issue activity unavailable in current metadata
- Permission surface: secrets or environment access, shell or command execution
- 아직 실제 Agent 결과 보고서가 없습니다
- 무인 설치 전에 사람 검토가 필요합니다
권장 작업
Choose a stronger alternative or inspect the source manually before any install attempt.
품질 프로필
유망 Agent 워크플로용 후보
유용한 후보이지만 채택 전에 대안과 비교하세요.
워크플로 적합도
이 스킬을 사용할 시나리오
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.
Process rich media
Multimodal media
I need my agent to process images, video, or audio and extract useful information.
워크플로 적합도
완전한 워크플로에 추가
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
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.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
대안 후보
설치 전 비교
이 작업에 적합할 수 있는 유사 스킬입니다.
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Nuclei is a fast, customizable vulnerability scanner powered by the global security community and built on a simple YAML-based DSL, enabling collaboration to tackle trending vulnerabilities on the internet. It helps you find vulnerabilities in your applications, APIs, networks, DNS, and cloud configurations.
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개요
--- name: sero-humanize description: | Audit or edit Sero Markdown documentation and other product prose to remove AI-writing patterns while preserving technical meaning, product terminology, links, examples, and the repository voice. Use when the user asks to humanize, de-slop, tighten, simplify, rewrite, or audit Sero documentation, README text, UI copy, release notes, plans, or specifications for AI tells. Also use when prose needs ASD-STE100 Simplified Technical English. Do not use for code review or for creative and promotional writing. ---
# Sero Humanize
Make Sero prose direct, specific, and useful. Human writing in technical documentation does not need personality. It needs clear decisions, concrete facts, and respect for the reader's time.
## Follow the requested mode
- For an audit or review, report the material patterns and do not edit files. - For an edit, rewrite the named files in place. - For a new document, apply these rules while drafting it. - If the request does not specify a mode, infer it from the requested action. Do not turn a request to assess prose into permission to change it.
## Establish the voice and preservation set
Before editing:
1. Read every file in scope in full. 2. Read the nearest repository instructions that apply to those files. 3. Use adjacent, clearly human-edited Sero documentation as the voice sample when the named files do not establish a consistent voice. 4. Record what must not change: - technical meaning and product behaviour; - product names, canonical terms, and exact UI labels; - commands, code, file paths, numbers, limits, and factual claims; - the documentation type, useful narrative flow, and balance between prose, lists, tables, examples, and callouts; - frontmatter, anchors, link targets, image paths, and screenshot order; - the purpose and placement of each image, diagram, and other media asset; - quotations and user input examples, unless the user asks to edit them.
Identify the intended reader. Unless the page states otherwise, assume the reader knows neither Sero nor the feature. Do not assume that simpler grammar fixes an explanation that requires missing product knowledge.
Do not add a fact to make a sentence more vivid. Verify a doubtful claim from the repository or leave it unchanged and report the doubt.
## Audit structure before wording
Look for clusters and repeated patterns. Do not treat one punctuation mark or one common word as proof of AI writing.
Prioritize these defects:
- Meta narration that announces the next explanation instead of giving it. - Repeated tutorial staging such as "what you are about to learn" and "what you have learned." - Fixed enumerations such as "three things are worth noticing" when a direct heading or short list is clearer. - Several paragraphs that can change order without changing the argument. - A heading followed by a sentence that only repeats the heading. - A conclusion that repeats the introduction without adding an action or fact. - Repeated summaries of the same screen, process, or result. - Forced contrasts such as "not only X, but Y" or "not X; rather Y." - Groups of three used for rhythm instead of meaning. - Mechanical bold lead-ins, excessive inline bold, or lists that should be short prose. - Promotional adjectives, vague importance claims, and unsupported praise. - Vague actors, passive constructions, filler, stacked hedges, and abstract nouns where an action is available. - Synonym cycling for one product concept. Repeat the canonical term. - Long sentences that mix instructions, exceptions, and background. - Em dashes used repeatedly to join thoughts that need separate sentences. - Headings that narrate the demo or expose implementation language instead of naming the reader's task, such as "The finish" or "Answer the gate." - Examples that depend on an unexplained demo domain and therefore do not help the reader understand the feature. - Tutorials that start using the product before they give prerequisites, sample data, expected starting state, or required sign-ins. - Result sections that recite one captured run instead of telling the reader what to inspect and verify.
Keep useful structure. A list, summary, warning, question heading, or em dash is not a defect by itself.
Do not normalize a page or site to one format. Humanize the defective passages, not every paragraph. If a page already explains a concept well in prose, keep it as prose.
## Rewrite for Sero documentation
Apply ASD-STE100 Simplified Technical English where it fits the material:
- Put the action or answer first. - Use active voice when the actor matters. - Give one main instruction per sentence. - Put a condition before the action when the reader must know it first. - Prefer common, precise words over formal or promotional alternatives. - Use the same term for the same thing. - Keep paragraphs focused on one subject. - Keep necessary limits, cautions, and exceptions close to the action. - Use contractions only when the established local voice requires them. - Keep exact UI text in bold when the documentation uses bold for controls. - Keep code identifiers and paths in code formatting. - Retain a summary only when it helps the reader decide or act.
Use lists only when the content is naturally a sequence, set of choices, checklist, or compact reference. Do not:
- convert explanatory prose into bullet points only to make it shorter; - turn each sentence or paragraph into a list item; - replace transitions and reasoning with disconnected bullets; - use repeated lists where a short paragraph gives the reader necessary context; or - make several pages share the same mechanical list structure.
After the sentence pass, read the page as a whole. If lists now dominate a page that previously used useful prose, restore the prose. Clear technical writing needs connected explanation as well as scannable reference material.
For an overview page:
- Explain the feature in familiar words before using its product terms. - State what the user gives Sero, what Sero does, and what the user reviews. - When comparing features, give one plain decision rule. Use examples that a reader can understand without knowing the tutorial repository or a specialist software domain.
For a tutorial:
- Put setup before the first product action. Include required software, accounts, sign-ins, repository or sample-data setup, and a command or visible result that confirms the expected starting state. - Prefer a stable sample repository over instructions that ask an agent to generate approximate sample data. Verify the repository contents and commands before documenting them. - Use task-based headings such as "Review the plan," "Change the plan," and "Check the result." A heading must describe the full purpose of its section; do not narrow a general control to one example case. - End with checks the reader can perform. Do not use a captured run's cost, duration, names, or outcome as a substitute for verification instructions.
For feature language:
- Use the visible object name: icon, button, tab, question, or approval request. Do not call an icon a mark or expose internal terms such as gate, fan-out, or feedback route when plain behaviour is enough. - Keep exact UI labels unchanged, but explain them with common words. - Put high-value quality-of-life features where readers will find them. Give them enough space to explain when the control appears, how to use it, what it changes, and what remains under user control.
Compress or merge only the passages that contain a verified structural defect. Keep useful depth, examples, transitions, and paragraph structure. A shorter page is not automatically a better page.
Do not manufacture a human voice with:
- anecdotes, opinions, jokes, sensory details, or personal asides; - fragments, one-word sentences, or dramatic punch lines; - arbitrary sentence-length variation; - unusual synonyms chosen only to make wording less predictable; - metaphors that replace a precise technical explanation; - deliberate imperfections or tangents; - an invented AI probability or numerical slop score.
## Preserve Markdown and product accuracy
- Do not change fenced code, commands, URLs, link targets, image targets, or frontmatter unless the request requires it. - Do not rename a heading if another page links to its generated anchor without updating that link. - Do not change a UI label to improve prose. Rewrite the surrounding sentence. - Do not remove repetition that is required for independent reference sections. - Do not convert a walkthrough into reference documentation, or reference documentation into a narrative tutorial, without user approval. - Do not infer product behaviour from the prose alone when the edit changes a technical claim. Check the implementation or an authoritative reference. - Treat contradictions between prose, screenshots, capture metadata, sample repositories, and implementation as accuracy defects. Resolve them from the authoritative source instead of rewriting around them. - When a page title changes, update the sidebar, index, related-page labels, and in-scope links that display the old title.
## Preserve images and other media
Treat every existing image, diagram, video, and asset as preserved content. Humanizing prose does not authorize media removal or replacement.
- Do not delete an asset, remove its reference, change its order, or replace it unless the user explicitly approves that action. - Do not use "task value," brevity, a stale appearance, or a text explanation as automatic reasons to remove an image. - Do not bulk-delete assets during a prose revision. - If an image is stale, private, inaccurate, decorative, or duplicated, report the issue and propose one action: keep, recapture, move, or remove. Wait for approval before changing it. - If an image exposes a credential or other active secret, stop publication and report it immediately. Do not silently make a wider set of image changes. - When a replacement is approved, capture or obtain the replacement before removing the current asset. Preserve the route and layout while replacement work is pending. - Check non-doc consumers before changing an asset. README files, homepages, package pages, and other applications can import docs-site images directly.
A decision not to add a new screenshot is not permission to remove an existing screenshot.
## Control the size of the rewrite
For a large documentation set, work in reviewed vertical slices. Complete and review one representative page before applying the approach to the rest of a slice. Do not perform a site-wide structural rewrite from an audit summary.
Pause and ask for approval when the work would:
- change the dominant format of a page, such as prose to lists; - remove substantial explanation, examples, or media; - merge, tombstone, redirect, or delete a page; - change many pages through the same structural template; or - produce a much larger diff than the factual and prose defects require.
When several agents contribute, give them the same preservation set and require a central review of format balance and media changes before integration.
## Use a two-pass edit
### Pass 1: structure
Remove redundant framing, merge repeated explanations, order information by the reader's task, and keep prerequisites before dependent actions. Give prominent placement to features that materially improve repeated use; do not give every feature equal weight merely because the source page did.
Keep the smallest effective structural change. Do not rewrite a complete page when a heading, transition, or paragraph edit fixes the defect.
### Pass 2: sentences
Remove filler and AI mannerisms. Simplify grammar. Keep terminology and facts stable. Read the result as technical documentation, not as marketing copy.
Then compare the result with th
기술 세부 사항
- 버전
- 1.0.0
- 라이선스
- Apache-2.0
- 최근 업데이트
- 2026년 8월 19일
- 게시일
- 2026년 8월 19일
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
sero-humanize용 시나리오 기반 초안입니다. X에 수동으로 게시할 수 있습니다.
A practical pick for design or creative work: sero-humanize: Audit or edit Sero Markdown documentation and other product prose to remove AI-writing patterns while preserving technical... 19 stars https://www.openagentskill.com/skills/sero-labs-sero-humanize?ref=x
선택 사항: 설치 명령이 포함된 답글
Listing + install path for sero-humanize: https://www.openagentskill.com/skills/sero-labs-sero-humanize?ref=x Install: npx skills add sero-labs/sero --skill sero-humanize
등록 출처
Registry 색인
이 등록은 공개 소스에서 색인되었으며 유지보수자 소유권 주장이 승인될 때까지 공식으로 표시되지 않습니다.
- 제작자
- sero-labs
- 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 sero-labs에게 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/sero-labs-sero-humanize)
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize)
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize/audit)
[](https://www.openagentskill.com/skills/sero-labs-sero-humanize)작성자
sero-labs
@sero-labs
플랫폼 적합도
상태 신호
- GitHub 스타
- 19
- 품질 점수
- 33/100
- 최근 GitHub 푸시
- 2026년 8월 19일
- 프레임워크 힌트
- 알 수 없음
- OpenAgentSkill 조회수
- 6
- 설치 명령 복사
- 0
- 외부 클릭
- 0
커뮤니티 신호
이 스킬이 Agent 워크플로에 유용한지 알려 주세요. 집계된 피드백은 시간이 지날수록 순위를 개선합니다.
신뢰와 안전
Do not auto-install
- GitHub 채택도GitHub 스타 19수정
- 스타/포크 활동스타 19, 포크 1; 현재 메타데이터에서 이슈 활동을 확인할 수 없습니다수정
- 최근 유지보수마지막 푸시 후 3일통과
- 라이선스 명확성Apache-2.0통과
- README/SKILL.md 완성도메타데이터에 충분한 사용 및 워크플로 맥락이 포함되어 있습니다통과
- 의존성/런타임 위험자격 증명 또는 환경 변수 접근정보
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