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
供给资产档案
研究与知识工作
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
场景
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
适配 Agent
Claude Code + CLI + Codex
适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。
安装
就绪
npx skills add libnyx/LT2MD --skill lt2md
维护状态
新鲜
今天有推送
风险
需审查
Permission surface may require sandboxing
GitHub 质量
33
62/100 质量 · 68/100 信任
覆盖标签
审查说明
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 采用评分卡
一眼查看信任、审计与安装准备度
这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。
质量
有潜力有用的候选项,但采用前应与替代方案比较。
信任
仅限沙盒有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
审计
需审查对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。
OpenAgentSkill 信任评分 v5
安装前需人工审查
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
Stars
33 个 GitHub Stars
仓库活跃度
33 个 Star,1 个 Fork
维护状态
今天有推送
许可证
AGPL-3.0
安装
npx skills add libnyx/LT2MD --skill lt2md
安装安全性
标准软件包或运行时安装路径
权限范围
secrets or environment access, shell or command execution
Agent 结果
暂未有 Agent 结果数据
文档
README/SKILL.md 上下文充分
风险摘要
生产前审查
- 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
安装准备度
安装路径可用
- 安装路径可用
- 仓库证据可用
- 已声明许可证
- 暂无 Agent 验证结果证据
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
适用任务
- Document processing 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
- Read uploaded files
适用 Agent
安装决策
- 命令
- npx skills add libnyx/LT2MD --skill lt2md
- 策略
- 阻止
- 人工审查
- 是
信任与风险
- 信任
- 60/100
- 审计
- 74/100
- 风险级别
- 需审查
结果闭环
- 端点
- /api/agent/outcome
- 事件 ID
- resolve
- 结果
- 5
不适用场景
- 需要厂商支持 SLA 的团队
- 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.
- 高风险权限提示:Shell or command execution, Secrets or environment access
Agent 安全 v2
30/100 · 避免自动安装
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.
高
Shell 或命令执行
Skill 元数据引用了终端、CLI、Shell、子进程或命令执行工作流。
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
文件系统访问
Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。
高
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- 高风险权限提示:Shell or command execution, Secrets or environment access
- Permission surface may require sandboxing
安装目标
在你的 Agent 工作流中安装此 Skill
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
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 解析计划
让 Agent 在安装前验证匹配度。
Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。
打开 JSON
/api/agent/resolve?task=Use%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/libnyx-lt2md/install
Agent 应检查
- 从 Resolve API 检查任务匹配与替代方案。
- 检查审计评分、信任评分和安全策略警告。
- 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。
复制提示词
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 交接
把安装路径交给 Agent,而不是再给一个目录页。
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
安装交接
/api/skills/libnyx-lt2md/install
LLM 文本格式
/api/skills/libnyx-lt2md/install?format=text
寻找替代方案
/api/skills/search?q=lt2md&limit=3
Agent 提示词
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 元数据
用于自动选择 Skill 的 Agent 可读档案。
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
Manifest
/api/registry/manifest/libnyx-lt2md
LLM 文本
/api/registry/manifest/libnyx-lt2md?format=text
安装别名
/api/registry/install/libnyx-lt2md
推荐
/api/registry/recommend?task=Use%20lt2md%20in%20an%20agent%20workflow&limit=3
适配 Agent
Document processing
平台
Claude Code
Agent 决策面板
Fallback candidate for Document processing
先用此 Skill 做原型验证,并保留备选方案。
栈中角色
备选候选
主要匹配
Document processing
信任标签
先做原型验证
安装路径
命令已就绪
适用场景
- Document processing 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
证据
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 62/100 质量档案
- 1 个 OpenAgentSkill 交互事件
先审查
- 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.
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次Document processing任务。
- 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.
信任档案
仅限沙盒
有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
GitHub 采用度
检查33 个 GitHub Stars
Star/Fork 活跃度
检查33 个 Star,1 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过今天有推送
许可证清晰度
通过AGPL-3.0
积极信号
- AI 审查已通过
- 安装路径可用
- 仓库证据可用
- 近期维护的仓库
- 安装命令未发现明显高风险模式
- 结果闭环已就绪,但需要首次真实 Agent 运行
安装前审查
- 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
- 暂未有真实 Agent 结果报告
- 无人值守安装前需要人工审查
建议操作
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
质量档案
有潜力 适用于 Agent 工作流的候选
有用的候选项,但采用前应与替代方案比较。
工作流匹配
在这些场景使用此 Skill
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.
工作流匹配
加入完整工作流
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.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Scrape, clean, and reuse web data
Web data pipeline
A practical workflow for agents that crawl public pages, extract clean content, normalize data, and hand it to downstream research or RAG workflows.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
Wazuh
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
Maigret
🕵️♂️ Collect a dossier on a person by username from 3000+ sites
Nuclei
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.
Infisical
Infisical is the open-source platform for secrets, certificates, and privileged access management.
概览
--- 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
技术详情
- 版本
- 1.0.0
- 许可证
- AGPL-3.0
- 最近更新
- 2026年8月23日
- 发布时间
- 2026年8月23日
决策摘要
备选候选
仓库近期活跃
Agent 验证证据
Agent 验证证据
来自解析、审查、安装和一次小范围运行后的结果报告。
- 成功率
- —
- 近期失败
- —
- 结果
- 0
- 输出质量
- —
- 失败
- 0
- 不相关
- 0
- 安装次数
- 0
- 风险拦截
- 0
- 需要配置
- 0
- 生产环境
- 0
暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。
增长闭环
分享工具包
为 lt2md 准备的场景化草稿,可手动发布到 X。
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
可选:带安装命令的回复
Listing + install path for lt2md: https://www.openagentskill.com/skills/libnyx-lt2md?ref=x Install: npx skills add libnyx/LT2MD --skill lt2md
收录来源
Registry 收录
此列表来自公开来源,维护者认领获批前不会标记为官方。
- 创作者
- libnyx
- 收录方
- OpenAgentSkill 社区索引
归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。
认领此 Skill所有者认领
认领此 Skill 页面
这条 Registry 收录 列表归属于 libnyx,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。
创作者外链工具包
将证据徽章加入你的 README
在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。
[](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)作者
libnyx
@libnyx
平台适配
健康信号
- GitHub Stars
- 33
- 质量评分
- 34/100
- 最近 GitHub 推送
- 2026年8月23日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 1
- 复制安装命令
- 0
- 跳转点击
- 0
社区信号
告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。
信任与安全
仅限沙盒
- GitHub 采用度33 个 GitHub Stars检查
- Star/Fork 活跃度33 个 Star,1 个 Fork; 当前元数据中没有议题活跃度信息检查
- 近期维护今天有推送通过
- 许可证清晰度AGPL-3.0通过
- README/SKILL.md 完整度元数据包含足够的用法与工作流上下文通过
- 依赖与运行时风险credential or environment access, database surface信息
相关 Skill
Wazuh
Wazuh - The Open Source Security Platform. Unified XDR and SIEM protection for endpoints and cloud workloads.
16.3K StarsMaigret
🕵️♂️ Collect a dossier on a person by username from 3000+ sites
32.9K StarsNuclei
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.
29.2K StarsInfisical
Infisical is the open-source platform for secrets, certificates, and privileged access management.
27.4K Stars