lt2md

Revoir · 60
Indexé dans 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
Stars33
Version1.0.0
Qualité62/100 · Prometteur
Confiance60/100 · Sandbox uniquement
Audit74/100 · Revue nécessaire

Profil de l’actif

Recherche et travail de connaissance

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

Voir la catégorie

Scénario

Document processing

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

Adéquation Agent

Claude Code + CLI + Codex

Compatible avec Codex, Claude Code, Cursor, CLI ou des Agents personnalisés.

Installer

Prêt

npx skills add libnyx/LT2MD --skill lt2md

Maintenance

À jour

Mis à jour aujourd’hui

Risque

Revue nécessaire

Permission surface may require sandboxing

Qualité GitHub

33

62/100 Qualité · 68/100 Confiance

Tags de couverture

RechercheDocument processingSécuritéagent-skill

Notes de revue

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.

Carte d’adoption Agent

Confiance, audit et préparation à l’installation en un coup d’œil

Ces scores combinent les métadonnées publiques du dépôt, les signaux de revue OpenAgentSkill, la fraîcheur de maintenance et la préparation à l’installation. Ils servent à présélectionner et ne remplacent pas la revue humaine.

Qualité

Prometteur
62

Useful candidate, but compare it with alternatives before adopting.

Confiance

Sandbox uniquement
60

Candidate utile avec des signaux de confiance incomplets ou mixtes. Gardez-la dans un espace isolé jusqu’à ce que la boucle de résultats confirme son adéquation.

Audit

Revue nécessaire
74

Revue lisible par machine de la préparation à l’installation, des métadonnées de sécurité, de la maintenance et du risque d’adoption.

Trust Score OpenAgentSkill v5

Revue humaine avant installation

Exécutez uniquement dans un sandbox et comparez les alternatives proches avant usage réel.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

33 stars GitHub

Activité du dépôt

33 stars et 1 forks

Maintenance

Mis à jour aujourd’hui

Licence

AGPL-3.0

Installer

npx skills add libnyx/LT2MD --skill lt2md

Sécurité d’installation

Chemin d’installation standard de package ou runtime

Surface de permissions

secrets or environment access, shell or command execution

Résultats Agent

Pas encore de données de résultats Agent

Documentation

Contexte README/SKILL.md solide

Résumé des risques

Revoir avant production

  • 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

Préparation à l’installation

Chemin d’installation disponible

  • Le chemin d’installation est disponible
  • La preuve du dépôt est disponible
  • La licence est déclarée
  • Pas encore de preuve de résultat Agent-Proven

Métadonnées lisibles par Agent

Données de décision lisibles par machine pour ce skill.

Utilisez ce bloc ou le JSON intégré pour décider si un Agent doit installer ce skill, choisir une alternative ou demander d’abord une revue humaine.

Ouvrir JSON

Tâches adaptées

  • workflows Document processing
  • Équipes Claude Code
  • builders willing to evaluate younger projects
  • Read uploaded files

Agents adaptés

CodexClaude CodeCursorOpenAgentSkill CLICLI

Décision d’installation

Commande
npx skills add libnyx/LT2MD --skill lt2md
Politique
Bloquer
Revue humaine
Oui

Confiance et risque

Confiance
60/100
Audit
74/100
Niveau de risque
Revue nécessaire

Boucle de résultat

Endpoint
/api/agent/outcome
ID d’événement
resolve
Résultats
5

Commande d’installation

npx skills add libnyx/LT2MD --skill lt2md

Ne pas utiliser quand

  • Équipes qui nécessitent un SLA soutenu par le fournisseur
  • 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.
  • No OpenAgentSkill engagement data yet

Sécurité Agent v2

30/100 · Éviter l’installation automatique

Blocked for auto-installBloquer

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.

Résoudre via API

Élevé

Exécution shell ou de commande

Les métadonnées de la skill font référence à des workflows de terminal, CLI, shell, sous-processus ou exécution de commande.

Moyen

Accès réseau

La skill récupère probablement des pages distantes, API, dépôts ou services externes.

Moyen

Accès au système de fichiers

La skill peut lire ou écrire des fichiers de projet, documents, artefacts générés ou l’état local de l’espace de travail.

Élevé

Secrets or environment access

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

  • Indices de permissions à haut risque : Shell or command execution, Secrets or environment access
  • Permission surface may require sandboxing

Cibles d’installation

Installer ce skill dans votre workflow Agent

Utilisez le point de terminaison public pour récupérer la commande, la checklist, les prompts et les liens canoniques.

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 résolution Agent

Laissez un Agent vérifier la pertinence avant l’installation.

L’API Resolve renvoie la skill sélectionnée, des alternatives, la politique de sécurité, les notes d’audit, la cible d’installation et un prompt prêt à l’emploi.

Ouvrir le plan texte

L’Agent doit vérifier

  • 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.

Copier le 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.

Relais Agent

Donnez à l’Agent le chemin d’installation, pas un autre annuaire.

Utilisez le point de terminaison public pour récupérer la commande, la checklist, les prompts et les liens canoniques.

Ouvrir l’API d’installation

Prompt 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

Métadonnées Registry

Profil lisible par Agent pour la sélection automatique de skills.

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir Manifest

Adéquation Agent

61/100

Document processing

Plateformes

Claude Code

Rapport d’audit

Revue nécessaire · 74/100

Revue lisible par machine de la préparation à l’installation, des métadonnées de sécurité, de la maintenance et du risque d’adoption.

Voir le rapport d’auditVoir le rapport d’évaluation

Panneau de décision Agent

Fallback candidate for Document processing

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

61
Préparation
Prototype
Étape

Rôle dans la pile

Candidate de secours

Pertinence principale

Document processing

Libellé de confiance

Prototyper d’abord

Chemin d’installation

Commande prête

À utiliser lorsque

  • workflows Document processing
  • Équipes Claude Code
  • builders willing to evaluate younger projects

Preuves

  • recent repository activity
  • install command or GitHub repo available
  • profil qualité 62/100

revoir d’abord

  • 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.
  • No OpenAgentSkill engagement data yet

Chemin d’implémentation

  1. 1Installez-le dans un Agent en sandbox et exécutez une tâche de Document processing de bout en bout.
  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.

Profil de confiance

Sandbox uniquement

Candidate utile avec des signaux de confiance incomplets ou mixtes. Gardez-la dans un espace isolé jusqu’à ce que la boucle de résultats confirme son adéquation.

60
Trust Score OpenAgentSkill

Adoption GitHub

Vérifier

33 stars GitHub

Activité stars/forks

Vérifier

33 stars et 1 forks; l’activité des issues n’est pas disponible dans les métadonnées actuelles

Maintenance récente

Validé

Mis à jour aujourd’hui

Clarté de licence

Validé

AGPL-3.0

Signaux positifs

  • Revue IA approuvée
  • Le chemin d’installation est disponible
  • La preuve du dépôt est disponible
  • Dépôt maintenu récemment
  • La commande d’installation ne présente aucun motif de haut risque évident
  • La boucle de résultats est prête mais nécessite la première exécution réelle de l’Agent

Réviser avant installation

  • 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
  • Pas encore de rapports de résultats Agent réels
  • Une revue humaine est requise avant une installation sans surveillance

Action recommandée

Exécutez uniquement dans un sandbox et comparez les alternatives proches avant usage réel.

Profil qualité

Prometteur candidat pour les workflows Agent

Useful candidate, but compare it with alternatives before adopting.

62
Stars GitHub
33
Actualité
Aujourd’hui
Prêt à installer
Oui
Licence
AGPL-3.0
Réviser avant installation: 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.

Adéquation au workflow

Utilisez cette skill dans ces scénarios

Adéquation au workflow

Ajouter à un workflow complet

Liste d’alternatives

Comparer avant installation

Similar skills that may fit this task.

Tout comparer

Vue d’ensemble

--- 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

Détails techniques

Version
1.0.0
Licence
AGPL-3.0
Dernière mise à jour
23 août 2026
Publié
23 août 2026

Instantané de décision

Candidate de secours

61
Prêt
Prototype
Étape

recent repository activity

Audit

Revue d’installation

Revue d’installation et d’adoption

74
Revue nécessaire
Sécurité
72/100
Maintenance
100/100
Installer
92/100
Ouvrir l’audit completVoir le rapport d’évaluation

Preuves validées par Agent

Preuves validées par Agent

Rapports après resolve, revue, installation et une exécution limitée.

0
Validé
Needs first agent runAuto-installation: revoir d’abordDernier: Inconnu
Taux de réussite
Échec récent
Résultats
0
Qualité de sortie
Échecs
0
Non pertinent
0
Installations
0
Bloqué par le risque
0
Configuration requise
0
Production
0

Aucune donnée de résultat Agent pour l’instant. La première exécution peut signaler succès, besoin de configuration, blocage de risque, échec ou non-pertinence via /api/agent/outcome.

Installer

Ajouter au workflow Agent

Gratuit et open source. Examinez le rapport avant l’installation dans des Agents de production.

Boucle de croissance

Kit de partage

X

Brouillon guidé par scénario pour lt2md, prêt pour une publication manuelle sur X.

Note du curateur
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
Ouvrir le brouillon X
Réponse facultative avec commande d’installation
Listing + install path for lt2md:
https://www.openagentskill.com/skills/libnyx-lt2md?ref=x

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

Source de la fiche

Indexé par Registry

Revendiable

Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur
libnyx
Indexé par
Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

Revendiquer ce skill

Revendication du propriétaire

Revendiquer cette fiche de skill

Cette fiche Indexé par Registry est attribuée à libnyx, mais n’est pas encore marquée officielle. Revendiquez-la pour ajouter un signal de propriétaire vérifié et rendre les futures mises à jour de lancement, d’installation et d’audit plus fiables.

Kit de backlinks créateur

Ajoutez les badges de preuve à votre README

Affichez la fiche canonique, les signaux actuels de confiance et d’audit, ainsi que de vraies preuves Agent-Proven là où les développeurs évaluent le dépôt.

[![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)

Auteur

L

libnyx

@libnyx

Adéquation plateforme

Signaux de santé

Stars GitHub
33
Score de qualité
34/100
Dernier push GitHub
23 août 2026
Indications de framework
Inconnu
Vues OpenAgentSkill
0
Copies d’installation
0
Clics sortants
0

Signal de communauté

Indiquez si ce skill semble utile à votre workflow Agent. Les retours agrégés améliorent le classement au fil du temps.

Confiance et sécurité

Sandbox uniquement

60
  • Adoption GitHub33 stars GitHubVérifier
  • Activité stars/forks33 stars et 1 forks; l’activité des issues n’est pas disponible dans les métadonnées actuellesVérifier
  • Maintenance récenteMis à jour aujourd’huiValidé
  • Clarté de licenceAGPL-3.0Validé
  • Complétude README/SKILL.mdLes métadonnées incluent suffisamment de contexte d’usage et de workflowValidé
  • Risque dépendances/runtimecredential or environment access, database surfaceInfo