lt2md

Revisar · 60
Indexado en Registry

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

Verified installs0
Estrellas33
Versión1.0.0
Calidad62/100 · Prometedor
Confianza60/100 · Solo sandbox
Auditoría74/100 · Requiere revisión

Perfil del activo

Investigación y trabajo de conocimiento

Deep research, source comparison, literature review, RAG, knowledge search, and reports.

Ver categoría

Escenario

Document processing

I need my agent to read PDFs, extract tables, and turn documents into structured data.

Afinidad con Agent

Claude Code + CLI + Codex

Funciona con Codex, Claude Code, Cursor, CLI o Agents personalizados.

Instalar

Listo

npx skills add libnyx/LT2MD --skill lt2md

Mantenimiento

Actual

Actualizado hoy

Riesgo

Requiere revisión

Permission surface may require sandboxing

Calidad de GitHub

33

62/100 Calidad · 68/100 Confianza

Etiquetas de cobertura

InvestigaciónDocument processingSeguridadagent-skill

Notas de revisión

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.

Tarjeta de adopción del Agent

Confianza, auditoría y preparación de instalación de un vistazo

Estas puntuaciones combinan metadatos públicos del repositorio, señales de revisión de OpenAgentSkill, actualidad de mantenimiento y preparación de instalación. Sirven para preseleccionar; no sustituyen la revisión humana.

Calidad

Prometedor
62

Useful candidate, but compare it with alternatives before adopting.

Confianza

Solo sandbox
60

Candidata útil con señales de confianza incompletas o mixtas. Manténgala en un espacio aislado hasta que el ciclo de resultados demuestre el ajuste.

Auditoría

Requiere revisión
74

Revisión legible por máquina de la preparación de instalación, los metadatos de seguridad, el mantenimiento y el riesgo de adopción.

Trust Score de OpenAgentSkill v5

Revisión humana antes de instalar

Ejecute solo en un sandbox y compare alternativas cercanas antes de usarla en trabajo real.

CodexClaude CodeCursorOpenAgentSkill CLI

Estrellas

33 estrellas de GitHub

Actividad del repositorio

33 estrellas y 1 forks

Mantenimiento

Actualizado hoy

Licencia

AGPL-3.0

Instalar

npx skills add libnyx/LT2MD --skill lt2md

Seguridad de instalación

Ruta estándar de paquete o instalación en tiempo de ejecución

Superficie de permisos

secrets or environment access, shell or command execution

Resultados del Agent

Aún no hay datos de resultados del Agent

Documentación

Contexto sólido de README/SKILL.md

Resumen de riesgo

Revisar antes de producción

  • 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

Preparación de instalación

Ruta de instalación disponible

  • La ruta de instalación está disponible
  • La evidencia del repositorio está disponible
  • La licencia está declarada
  • Aún no hay evidencia de resultados Agent-Proven

Metadatos legibles por Agent

Datos de decisión legibles por máquina para este skill.

Usa este bloque o el JSON integrado para decidir si un Agent debe instalar este skill, elegir una alternativa o pedir revisión humana primero.

Abrir JSON

Tareas adecuadas

  • flujos de Document processing
  • Equipos de Claude Code
  • builders willing to evaluate younger projects
  • Read uploaded files

Agents adecuados

CodexClaude CodeCursorOpenAgentSkill CLICLI

Decisión de instalación

Comando
npx skills add libnyx/LT2MD --skill lt2md
Política
Bloquear
Revisión humana

Confianza y riesgo

Confianza
60/100
Auditoría
74/100
Nivel de riesgo
Requiere revisión

Ciclo de resultados

Endpoint
/api/agent/outcome
ID del evento
resolve
Resultados
5

Comando de instalación

npx skills add libnyx/LT2MD --skill lt2md

No usar cuando

  • Equipos que necesitan un SLA con soporte del proveedor
  • 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.
  • Indicios de permisos de alto riesgo: Shell or command execution, Secrets or environment access

Seguridad de Agent v2

30/100 · Evitar instalación automática

Blocked for auto-installBloquear

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.

Resolver con API

Alto

Ejecución de shell o comandos

Los metadatos del skill hacen referencia a terminal, CLI, shell, subprocesos o flujos de ejecución de comandos.

Medio

Acceso a red

El skill probablemente consulta páginas remotas, API, repositorios o servicios externos.

Medio

Acceso al sistema de archivos

El skill puede leer o escribir archivos de proyecto, documentos, artefactos generados o estado local.

Alto

Secrets or environment access

Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.

  • Indicios de permisos de alto riesgo: Shell or command execution, Secrets or environment access
  • Permission surface may require sandboxing

Destinos de instalación

Instala este skill en tu flujo de Agent

Usa el endpoint público para obtener el comando, la lista de seguridad, prompts y enlaces canónicos.

skill install

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-lt2md

Plan de resolución de Agent

Deja que un Agent valide el ajuste antes de instalar.

La API Resolve devuelve la skill elegida, alternativas, política de seguridad, notas de auditoría, destino de instalación y un prompt listo para usar.

Abrir plan de texto

Agent debe revisar

  • Task fit and alternatives from Resolve API.
  • Audit score, trust score, and safety policy warnings.
  • Install target compatibility for Codex, Claude Code, Cursor, or CLI.

Copiar prompt

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.

Traspaso de Agent

Da al Agent la ruta de instalación, no otro directorio.

Usa el endpoint público para obtener el comando, la lista de seguridad, prompts y enlaces canónicos.

Abrir API de instalación

Prompt de Agent

Use lt2md for this task. Review https://www.openagentskill.com/api/skills/libnyx-lt2md/install, then install with: npx skills add libnyx/LT2MD --skill lt2md

Metadatos del Registry

Perfil legible por Agent para seleccionar skills automáticamente.

La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.

Abrir Manifest

Afinidad con Agent

61/100

Document processing

Plataformas

Claude Code

Informe de auditoría

Requiere revisión · 74/100

Revisión legible por máquina de la preparación de instalación, los metadatos de seguridad, el mantenimiento y el riesgo de adopción.

Ver informe de auditoríaVer informe de evaluación

Panel de decisión de Agent

Fallback candidate for Document processing

Prototype with this skill first; keep a fallback candidate ready.

61
Preparación
Prototipo
Etapa

Rol en la pila

Candidata de respaldo

Ajuste principal

Document processing

Etiqueta de confianza

Prototipar primero

Ruta de instalación

Comando listo

Úsalo cuando

  • flujos de Document processing
  • Equipos de Claude Code
  • builders willing to evaluate younger projects

Evidencia

  • recent repository activity
  • install command or GitHub repo available
  • perfil de calidad 62/100
  • 1 eventos de interacción de OpenAgentSkill

revisar primero

  • 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.

Ruta de implementación

  1. 1Instálalo en un Agent de sandbox y ejecuta una tarea de Document processing de principio a fin.
  2. 2Compare output quality, latency, and failure behavior against at least one alternative.
  3. 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.

Perfil de confianza

Solo sandbox

Candidata útil con señales de confianza incompletas o mixtas. Manténgala en un espacio aislado hasta que el ciclo de resultados demuestre el ajuste.

60
Trust Score de OpenAgentSkill

Adopción en GitHub

Revisar

33 estrellas de GitHub

Actividad de stars/forks

Revisar

33 estrellas y 1 forks; la actividad de issues no está disponible en los metadatos actuales

Mantenimiento reciente

Aprobado

Actualizado hoy

Claridad de licencia

Aprobado

AGPL-3.0

Señales positivas

  • Revisión de IA aprobada
  • La ruta de instalación está disponible
  • La evidencia del repositorio está disponible
  • Repositorio mantenido recientemente
  • El comando de instalación no muestra un patrón de alto riesgo evidente
  • El ciclo de resultados está listo, pero necesita la primera ejecución real de Agent

Revisar antes de instalar

  • 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
  • Aún no hay informes reales de resultados del Agent
  • Se requiere revisión humana antes de una instalación desatendida

Acción recomendada

Ejecute solo en un sandbox y compare alternativas cercanas antes de usarla en trabajo real.

Perfil de calidad

Prometedor candidato para flujos de Agent

Useful candidate, but compare it with alternatives before adopting.

62
Estrellas de GitHub
33
Actualidad
Hoy
Listo para instalar
Licencia
AGPL-3.0
Revisar antes de instalar: 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.

Ajuste de flujo

Usa esta skill en estos escenarios

Ajuste de flujo

Añadir a un flujo completo

Lista de alternativas

Compara antes de instalar

Similar skills that may fit this task.

Comparar todo

Resumen

--- 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

Detalles técnicos

Versión
1.0.0
Licencia
AGPL-3.0
Última actualización
23 ago 2026
Publicado
23 ago 2026

Resumen de decisión

Candidata de respaldo

61
Listo
Prototipo
Etapa

recent repository activity

Auditoría

Revisión de instalación

Revisión de instalación y adopción

74
Requiere revisión
Seguridad
72/100
Mantenimiento
100/100
Instalar
92/100
Abrir auditoría completaVer informe de evaluación

Evidencia probada por Agent

Evidencia probada por Agent

Informes de resultados tras resolver, revisar, instalar y una ejecución limitada.

0
Probado
Needs first agent runAuto-instalación: revisar primeroÚltimo: Desconocido
Tasa de éxito
Fallo reciente
Resultados
0
Calidad de salida
Fallidos
0
No relevante
0
Instalaciones
0
Bloqueado por riesgo
0
Configuración necesaria
0
Producción
0

Aún no hay datos de resultados de Agent. La primera ejecución puede informar éxito, configuración necesaria, bloqueos de riesgo, fallo o irrelevancia mediante /api/agent/outcome.

Instalar

Añadir al flujo de Agent

Gratis y de código abierto. Revisa el informe antes de instalar en Agents de producción.

Bucle de crecimiento

Kit para compartir

X

Borrador basado en un caso para lt2md, listo para publicar manualmente en X.

Nota del curador
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
Abrir borrador de X
Respuesta opcional con comando de instalación
Listing + install path for lt2md:
https://www.openagentskill.com/skills/libnyx-lt2md?ref=x

Install: npx skills add libnyx/LT2MD --skill lt2md

Fuente de la ficha

Indexado por Registry

Reclamable

Esta ficha se indexó desde fuentes públicas y no está marcada como oficial hasta que se apruebe una reclamación de mantenedor.

Creador
libnyx
Indexado por
Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

Reclamar este skill

Reclamación del propietario

Reclamar esta ficha de skill

Esta ficha Indexado por Registry se atribuye a libnyx, pero aún no está marcada como oficial. Reclámala para añadir una señal de propietario verificado y hacer más fiables futuras actualizaciones de lanzamiento, instalación y auditoría.

Kit de enlaces para creadores

Añade las insignias de evidencia a tu README

Muestra la ficha canónica, las señales actuales de confianza y auditoría, y evidencia real de Agent-Proven donde los desarrolladores evalúan el repositorio.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/libnyx-lt2md?metric=listed&label=Listed)](https://www.openagentskill.com/skills/libnyx-lt2md)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/libnyx-lt2md?metric=trust&label=Trust)](https://www.openagentskill.com/skills/libnyx-lt2md)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/libnyx-lt2md?metric=audit&label=Audit)](https://www.openagentskill.com/skills/libnyx-lt2md/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/libnyx-lt2md?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/libnyx-lt2md)

Autor

L

libnyx

@libnyx

Etiquetas

Afinidad con plataforma

Señales de salud

Estrellas de GitHub
33
Puntuación de calidad
34/100
Último push de GitHub
23 ago 2026
Pistas del framework
Desconocido
Vistas de OpenAgentSkill
1
Copias de instalación
0
Clics externos
0

Señal de comunidad

Comparte si este skill resulta útil para tu flujo de Agent. Los comentarios agregados mejoran la clasificación con el tiempo.

Confianza y seguridad

Solo sandbox

60
  • Adopción en GitHub33 estrellas de GitHubRevisar
  • Actividad de stars/forks33 estrellas y 1 forks; la actividad de issues no está disponible en los metadatos actualesRevisar
  • Mantenimiento recienteActualizado hoyAprobado
  • Claridad de licenciaAGPL-3.0Aprobado
  • Completitud de README/SKILL.mdLos metadatos incluyen suficiente contexto de uso y flujo de trabajoAprobado
  • Riesgo de dependencias/runtimecredential or environment access, database surfaceInfo