lt2md
Convert born-digital, scanned, or mixed PDFs into auditable Markdown while preserving reading order, equations, source-page anchors, and information-bearing images as adjacent non-original text descriptions. Use this skill whenever a user asks to transcribe, OCR, understand, or c
공급 자산 프로필
리서치 및 지식 작업
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
시나리오
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent 적합도
Claude Code + CLI + Codex
Codex, Claude Code, Cursor, CLI 또는 맞춤형 Agent에 적합합니다.
설치
준비됨
npx skills add libnyx/LT2MD --skill lt2md
유지보수
최신
오늘 푸시됨
위험
검토 필요
Permission surface may require sandboxing
GitHub 품질
33
62/100 품질 · 68/100 신뢰
커버리지 태그
검토 메모
Permission surface may require sandboxing · The SKILL.md references external files (references/*.md, scripts/*.py) that are not fully included in the excerpt, but they are part of the repository and the skill is self-contained within the repo.
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 33
저장소 활동
스타 33, 포크 1
유지보수
오늘 푸시됨
라이선스
AGPL-3.0
설치
npx skills add libnyx/LT2MD --skill lt2md
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
- The SKILL.md references external files (references/*.md, scripts/*.py) that are not fully included in the excerpt, but they are part of the repository and the skill is self-contained within the repo.
- 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
- Document processing 워크플로
- Claude Code 팀
- builders willing to evaluate younger projects
- Read uploaded files
적합한 Agent
설치 결정
- 명령어
- npx skills add libnyx/LT2MD --skill lt2md
- 정책
- 차단
- 사람 검토
- 예
신뢰와 위험
- 신뢰
- 60/100
- 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
- The SKILL.md references external files (references/*.md, scripts/*.py) that are not fully included in the excerpt, but they are part of the repository and the skill is self-contained within the repo.
- 아직 OpenAgentSkill 사용 피드백 데이터가 없습니다
Agent 안전 v2
30/100 · 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 libnyx-lt2mdAgent 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%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 텍스트
/api/agent/resolve?task=Use%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
설치 핸드오프
/api/skills/libnyx-lt2md/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 lt2md in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/libnyx-lt2md/install
Install command: npx skills add libnyx/LT2MD --skill lt2md
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/libnyx-lt2md/install
LLM 텍스트 형식
/api/skills/libnyx-lt2md/install?format=text
대안 찾기
/api/skills/search?q=lt2md&limit=3
Agent 프롬프트
Use lt2md for this task. Review https://www.openagentskill.com/api/skills/libnyx-lt2md/install, then install with: npx skills add libnyx/LT2MD --skill lt2mdRegistry 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.
Manifest
/api/registry/manifest/libnyx-lt2md
LLM 텍스트
/api/registry/manifest/libnyx-lt2md?format=text
설치 별칭
/api/registry/install/libnyx-lt2md
추천
/api/registry/recommend?task=Use%20lt2md%20in%20an%20agent%20workflow&limit=3
Agent 적합도
Document processing
플랫폼
Claude Code
Agent 결정 패널
Fallback candidate for Document processing
먼저 이 스킬로 프로토타입을 만들고 대체 후보를 준비하세요.
스택 내 역할
대체 후보
주요 적합도
Document processing
신뢰 라벨
먼저 프로토타입
설치 경로
명령어 준비됨
사용 시점
- Document processing 워크플로
- Claude Code 팀
- builders willing to evaluate younger projects
근거
- 최근 저장소 활동
- 설치 명령 또는 GitHub 저장소를 사용할 수 있습니다
- 품질 프로필 62/100
먼저 검토
- Low GitHub adoption signal
- The SKILL.md references external files (references/*.md, scripts/*.py) that are not fully included in the excerpt, but they are part of the repository and the skill is self-contained within the repo.
- 아직 OpenAgentSkill 사용 피드백 데이터가 없습니다
구현 경로
- 1샌드박스 Agent에 설치하고 Document processing 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 33
스타/포크 활동
확인스타 33, 포크 1; 현재 메타데이터에서 이슈 활동을 확인할 수 없습니다
최근 유지보수
통과오늘 푸시됨
라이선스 명확성
통과AGPL-3.0
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
- The SKILL.md references external files (references/*.md, scripts/*.py) that are not fully included in the excerpt, but they are part of the repository and the skill is self-contained within the repo.
- Low GitHub adoption signal
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 33 GitHub stars
- Stars/forks activity: 33 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
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
Parse messy files
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Search private knowledge
RAG and knowledge
I need my agent to build a RAG workflow over documents and retrieve reliable context.
Process rich media
Multimodal media
I need my agent to process images, video, or audio and extract useful information.
워크플로 적합도
완전한 워크플로에 추가
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Scrape, clean, and reuse web data
Web data pipeline
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대안 후보
설치 전 비교
이 작업에 적합할 수 있는 유사 스킬입니다.
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개요
--- name: lt2md description: Convert born-digital, scanned, or mixed PDFs into auditable Markdown while preserving reading order, equations, source-page anchors, and information-bearing images as adjacent non-original text descriptions. Use this skill whenever a user asks to transcribe, OCR, understand, or convert a PDF into Markdown, especially for scanned PDFs, image-heavy pages, formulas, multi-column layouts, page or section ranges, or token-efficient reuse. LT2MD (Long Transcribe to Markdown) is a workflow contract, not a replacement for a PDF parser or OCR/VLM backend. ---
# LT2MD — Long Transcribe to Markdown
LT2MD turns observable PDF content into Markdown that an agent or a person can audit later. It is designed for born-digital, scanned, and mixed PDFs. The goal is not merely to obtain text: preserve reading order, formulas, figure meaning, scope boundaries, and a path back to the source page.
The PDF remains the only authority for content. OCR, extracted text, model guesses, and formatting preferences are candidates or transformations, never evidence that can overrule the rendered page.
## Read before doing the task
1. Read [workflow.md](references/workflow.md) for roles, batches, two-pass visual reading, and write permissions. 2. Before rendering or reusing pages, read [page-cache.md](references/page-cache.md) and initialize/recover a local job with `scripts/manage_job.py`. 3. Before creating or changing a candidate Markdown file, read [markdown-contract.md](references/markdown-contract.md); for long documents also read [job-state.md](references/job-state.md) and [checkpoint-review.md](references/checkpoint-review.md). 4. Before a format review, read [format-review.md](references/format-review.md) and treat [示范文档.md](references/示范文档.md) as a read-only format fixture. 5. Use `scripts/validate_markdown.py`, `scripts/audit_markdown.py`, and `scripts/manage_job.py verify` as separate final gates. Do not place OCR, model calls, or PDF interpretation inside the static tools.
## Non-negotiable principles
- **Separate evidence, semantic target, and allowed transformation.** Evidence is the rendered page, PDF page number, readable text layer, and Markdown markers. The semantic target is the author's text, mathematics, figure relationships, and reading order. Allowed transformations include merging print line breaks, removing page furniture, and applying the Markdown contract. - **Lock the scope before writing.** If no range is specified, process the whole PDF. If a range is specified, do not silently expand it. A range that ends mid-page includes only the requested semantic blocks. - **Reuse page evidence by byte identity.** Render through the content-addressed job cache. The same PDF bytes and render configuration must reuse verified page PNGs across chapters, restarts and renamed files; only missing or corrupt pages may be rerendered. - **Use the rendered page as the tie-breaker.** Text extraction and OCR are useful candidates. They do not settle reading order, formulas, captions, diagrams, or ambiguous glyphs without visual confirmation. - **Describe every information-bearing figure.** Keep the original caption when readable, then place an adjacent description explicitly marked as a LT2MD/transcriber supplement and not original text. Include objects, labels, directions, arrows, sequence, spatial relationships, subfigures, and relationships directly expressed by the figure without inventing outside conclusions. - **Keep provenance local.** Put one block-level `SOURCE` HTML comment on its own line before every complete paragraph, display equation, figure block, table or example block. Do not insert an anchor inside a word, sentence, inline formula, display-math block, table row, caption or image description. A cross-page block uses one physical-page range before the merged block. - **Keep content and format review separate.** Content corrections require evidence from the source PDF. A format reviewer may only report or apply style-only changes against the immutable fixture. Only the coordinator writes the final Markdown. - **Mark uncertainty instead of guessing.** When a glyph, page boundary, or reading order cannot be uniquely resolved, give the best source-grounded transcription and add a `转录注` with the exact page and ambiguity. Never silently normalize an uncertain value into a familiar one.
## Operating procedure
1. **Preflight.** Initialize or recover a job. Record the PDF SHA-256, physical page count, requested range, book-page mapping if readable, text-layer availability, render configuration, columns, formula/figure density, output path and task-requirement hash. Render the original pages before trusting OCR. If printed page numbers become visibly clear only after initialization and have a verified linear relation, record it before the first inventory with `manage_job.py set-book-page-offset <job> --offset <N>`; otherwise retain `unmapped` rather than guessing. Once recorded, that mapping is source evidence: the batch scaffold's `source_print_pages` and every `SOURCE` `BOOK_PAGE` must follow it, and manager review/checkpoint/finalization rejects contradictions. 2. **Batch.** Process continuous page ranges adaptively: 1–2 dense/low-quality pages, 2–4 ordinary pages, and at most 6 clear single-column pages. If the user did not choose groups, run `manage_job.py batch-plan <job> --json` after initialization, then visually lower any recommendation that contains formulas, tables, multi-column order, dense figures, poor legibility, or a cross-page semantic block. The raster-only plan is a conservative starting point, not visual proof. Prefer complete paragraphs, sections, or examples as cut points; keep a sentence crossing a page boundary with one transcriber. 3. **Inventory and transcribe.** Before trusting any existing Markdown candidate, visually inventory each source page's headings, prose, displayed equations, figures/captions, tables, footnotes, examples/exercises and cross-page continuations. For the current 1–6-page batch, create that source-only record first with `manage_job.py source-inventory-template`, fill only source objects and evidence, then freeze it with `manage_job.py seal-source-inventory`. Only after that seal may the transcriber use `manage_job.py batch-template --author-id <transcriber>` to create a fresh, non-overwriting batch-scoped candidate. This order is a hard gate: candidate block IDs, review decisions and candidate text must not be retrofitted into the source inventory. Never copy an unreviewed full-document V1 draft into the batch candidate and mistake a whole-document audit failure for a batch transcription attempt. Separate body text, equations, figures, captions, examples, headers, footers, and scan noise. Preserve literal Markdown backslashes while writing formulas: an escape-interpreting string layer must not turn a formula command into TAB, FF, or another C0 control byte. Merge only print line breaks and cross-page continuation; do not insert a page boundary inside a word, sentence, or LaTeX expression. An existing Markdown draft is an untrusted candidate, not evidence: visually re-check every retained block. If an inventory item has no source-grounded candidate block, leave the batch blocked; do not omit it merely because the candidate lacks an anchor. If a block is left unchanged, preserve page-specific review evidence; if the page cannot be read, stop there rather than calling the unchanged draft complete. 4. **Coordinate.** Merge candidate blocks in source-page order, attach page anchors, preserve equation tags and figure/example structure, and keep the locked range visible. 5. **Second visual read.** After the candidate passes its static contract and before generating a review template, record a handoff of its exact bytes with `manage_job.py reviewer-handoff`. The manager, not reviewer-supplied JSON, owns the reviewer actor ID, local security-principal record, candidate digest, and sealed-inventory binding. The default policy is an auditable process handoff: it does not prove subjective independence merely because labels differ. An optional `init --review-identity-policy os-security-principal-v1` also requires the reviewer process to use a different local OS security principal from the candidate and source-inventory authoring processes; it still cannot prove distinct people or model contexts. The handoff reviewer re-reads the rendered source and completes mappings against the already sealed source-only inventory. The reviewer may add candidate mappings, dispositions and risk closures, but may not rewrite sealed source facts. The coordinator changes content only after confirming the source. An omitted footnote, caption, heading, or cross-page continuation remains blocking even when static Markdown checks pass. 6. **Risk-driven third read.** Run the audit and re-check only real differences, low-resolution areas, dense formulas, multi-panel figures, cross-page joins, scope boundaries and risk hits. Use only the cached target/adjacent pages and targeted crops. 7. **Checkpoint and recover context.** Freeze every complete 1–6 page batch with a source-bound `checkpoint-review` JSON manifest through `manage_job.py checkpoint` before starting later pages. Use `manage_job.py review-template` only after the sealed-inventory-backed candidate passes the static contract **and** its exact-byte reviewer handoff is recorded; it produces a blocked identity/hash scaffold and does not replace source review. The manager rejects a missing handoff, a stale candidate digest, a forged reviewer label/principal, an indented-code pseudo-anchor, a review block spanning multiple SOURCE blocks, or a structural modification hidden by whitespace normalization. A failed static check, audit, source-inventory mapping, or independent review is a stop condition: repair the same batch or leave it explicitly incomplete; never treat a failure report as permission to continue. If a source object visibly continues to the next physical page before any independent review, do not accept the short batch or anchor a fragment. Use `manage_job.py extend-unclosed-source-inventory` only to preserve its sealed source facts and exact unclosed candidate while expanding the same-start range to at most six pages; then re-inventory every page, create a fresh candidate, and complete the normal independent review. This extension is blocked evidence, never acceptance, and cannot change a checkpointed range. When a reviewer supplies source-grounded omissions, misreads, ordering defects, or wrong-page anchors, return only that batch and the exact evidence to the transcriber, then obtain a new independent reread—never relabel the old review as accepted. If that review proves the **source-only inventory facts themselves** are incomplete or wrong, do not mutate the old seal: use `manage_job.py source-inventory-revision-template` with that independent blocked review, reread and seal the new source-only inventory, then create a fresh replacement candidate for the same range. The manager freezes a SHA-named copy of the blocked candidate and review; the new candidate receipt must bind the new active seal, and verification checks both the forward and backward revision chain. It rejects self-review, stale candidate replay, altered lineage, and unchanged source facts; a blocked review can never become acceptance. For jobs created before frozen-candidate evidence existed, use the strict `manage_job.py backfill-revision-evidence <job> --pages <range>` migration only when the preserved bytes, hashes, receipt, old seal and blocked review agree exactly. Every 16 accepted physical pages or 4 accepted batches, whichever comes first, actually reread task brief, render manifest, progress, frozen evidence and risk queue, then record the receipt with `manage_job.py reread`. This longer cadence supplements, rather than replaces, the per-batch evidence checkpoint; a cache-only or partial job must n
기술 세부 사항
- 버전
- 1.0.0
- 라이선스
- AGPL-3.0
- 최근 업데이트
- 2026년 8월 23일
- 게시일
- 2026년 8월 23일
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
lt2md용 시나리오 기반 초안입니다. X에 수동으로 게시할 수 있습니다.
A practical pick for design or creative work: lt2md: Convert born-digital, scanned, or mixed PDFs into auditable Markdown while preserving reading order, equations, source-page... 33 stars https://www.openagentskill.com/skills/libnyx-lt2md?ref=x
선택 사항: 설치 명령이 포함된 답글
Listing + install path for lt2md: https://www.openagentskill.com/skills/libnyx-lt2md?ref=x Install: npx skills add libnyx/LT2MD --skill lt2md
등록 출처
Registry 색인
이 등록은 공개 소스에서 색인되었으며 유지보수자 소유권 주장이 승인될 때까지 공식으로 표시되지 않습니다.
- 제작자
- libnyx
- 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 libnyx에게 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/libnyx-lt2md)
[](https://www.openagentskill.com/skills/libnyx-lt2md)
[](https://www.openagentskill.com/skills/libnyx-lt2md/audit)
[](https://www.openagentskill.com/skills/libnyx-lt2md)작성자
libnyx
@libnyx
플랫폼 적합도
상태 신호
- GitHub 스타
- 33
- 품질 점수
- 34/100
- 최근 GitHub 푸시
- 2026년 8월 23일
- 프레임워크 힌트
- 알 수 없음
- OpenAgentSkill 조회수
- 0
- 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 33확인
- 스타/포크 활동스타 33, 포크 1; 현재 메타데이터에서 이슈 활동을 확인할 수 없습니다확인
- 최근 유지보수오늘 푸시됨통과
- 라이선스 명확성AGPL-3.0통과
- README/SKILL.md 완성도메타데이터에 충분한 사용 및 워크플로 맥락이 포함되어 있습니다통과
- 의존성/런타임 위험credential or environment access, database surface정보
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