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
Asset-Profil
Recherche und Wissensarbeit
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Szenario
Document processing
I need my agent to read PDFs, extract tables, and turn documents into structured data.
Agent-Fit
Claude Code + CLI + Codex
Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.
Installieren
Bereit
npx skills add libnyx/LT2MD --skill lt2md
Wartung
Aktuell
Heute gepusht
Risiko
Prüfung nötig
Permission surface may require sandboxing
GitHub-Qualität
33
62/100 Qualität · 68/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
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-Adoptionskarte
Vertrauen, Audit und Installationsbereitschaft auf einen Blick
Diese Werte kombinieren öffentliche Repository-Metadaten, OpenAgentSkill-Reviewsignale, Wartungsaktualität und Installationsbereitschaft. Sie helfen bei der Vorauswahl, ersetzen aber keine menschliche Prüfung.
Qualität
VielversprechendUseful candidate, but compare it with alternatives before adopting.
Vertrauen
Nur SandboxNützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.
Audit
Prüfung nötigMaschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
OpenAgentSkill Trust Score v5
Menschliche Prüfung vor Installation
Nur in einer Sandbox ausführen und nahe Alternativen vergleichen, bevor sie produktiv eingesetzt wird.
Stars
33 GitHub-Stars
Repository-Aktivität
33 Stars und 1 Forks
Wartung
Heute gepusht
Lizenz
AGPL-3.0
Installieren
npx skills add libnyx/LT2MD --skill lt2md
Installationssicherheit
Standard-Paket- oder Laufzeit-Installationspfad
Berechtigungsfläche
secrets or environment access, shell or command execution
Agent-Ergebnisse
Noch keine Agent-Ergebnisdaten
Dokumentation
Starker README/SKILL.md-Kontext
Risikoübersicht
Vor Produktion prüfen
- 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
Installationsbereitschaft
Installationspfad verfügbar
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Lizenz ist angegeben
- Noch keine Agent-Proven-Ergebnisbelege
Agent-lesbare Metadaten
Maschinenlesbare Entscheidungsdaten für diesen Skill.
Nutze diesen Block oder das eingebettete JSON, um zu entscheiden, ob ein Agent diesen Skill installieren, eine Alternative wählen oder zuerst menschliche Prüfung anfordern soll.
Geeignete Aufgaben
- Document processing-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Read uploaded files
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add libnyx/LT2MD --skill lt2md
- Richtlinie
- Blockieren
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 60/100
- Audit
- 74/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add libnyx/LT2MD --skill lt2mdNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- 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.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
Agent-Sicherheit v2
30/100 · Automatische Installation vermeiden
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.
Hoch
Shell- oder Befehlsausführung
Die Skill-Metadaten verweisen auf Terminal-, CLI-, Shell-, Subprozess- oder Befehlsausführungs-Workflows.
Mittel
Netzwerkzugriff
Die Skill ruft wahrscheinlich Remote-Seiten, APIs, Repositories oder externe Dienste ab.
Mittel
Dateisystemzugriff
Die Skill kann Projektdateien, Dokumente, generierte Artefakte oder den lokalen Arbeitsbereich lesen oder schreiben.
Hoch
Secrets or environment access
Skill metadata references credentials, tokens, environment variables, or secret-bearing workflows.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
- Permission surface may require sandboxing
Installationsziele
Diesen Skill im Agent-Workflow installieren
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
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-Auflösungsplan
Lass einen Agent die Eignung vor der Installation prüfen.
Die Resolve API liefert die beste Skill, Alternativen, Sicherheitsrichtlinien, Auditnotizen, Installationsziel und einen direkt nutzbaren Prompt.
JSON öffnen
/api/agent/resolve?task=Use%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20lt2md%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/libnyx-lt2md/install
Agent sollte prüfen
- 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.
Prompt kopieren
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-Übergabe
Gib dem Agent den Installationspfad, nicht noch ein Verzeichnis.
Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.
Installationsübergabe
/api/skills/libnyx-lt2md/install
LLM-Textformat
/api/skills/libnyx-lt2md/install?format=text
Alternativen finden
/api/skills/search?q=lt2md&limit=3
Agent-Prompt
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-Metadaten
Agent-lesbares Profil für die automatische Skill-Auswahl.
Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.
Manifest
/api/registry/manifest/libnyx-lt2md
LLM-Text
/api/registry/manifest/libnyx-lt2md?format=text
Installationsalias
/api/registry/install/libnyx-lt2md
Empfehlen
/api/registry/recommend?task=Use%20lt2md%20in%20an%20agent%20workflow&limit=3
Agent-Fit
Document processing
Use-Case-Tags
Plattformen
Claude Code
Audit-Bericht
Prüfung nötig · 74/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Fallback candidate for Document processing
Prototype with this skill first; keep a fallback candidate ready.
Rolle im Stack
Fallback-Kandidat
Primäre Eignung
Document processing
Vertrauenslabel
Zuerst prototypisieren
Installationspfad
Befehl bereit
Verwenden wenn
- Document processing-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
Evidenz
- recent repository activity
- install command or GitHub repo available
- Qualitätsprofil 62/100
- 1 OpenAgentSkill-Interaktionen
zuerst prüfen
- 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.
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Document processing-Aufgabe vollständig aus.
- 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.
Vertrauensprofil
Nur Sandbox
Nützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.
GitHub-Akzeptanz
Prüfen33 GitHub-Stars
Star-/Fork-Aktivität
Prüfen33 Stars und 1 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
BestandenHeute gepusht
Lizenzklarheit
BestandenAGPL-3.0
Positive Signale
- KI-Prüfung genehmigt
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Kürzlich gewartetes Repository
- Der Installationsbefehl weist kein offensichtliches Hochrisikomuster auf
- Ergebniszyklus ist bereit, benötigt aber den ersten echten Agent-Lauf
Vor Installation prüfen
- 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
- Noch keine echten Agent-Ergebnisberichte
- Vor unbeaufsichtigter Installation ist menschliche Prüfung erforderlich
Empfohlene Aktion
Nur in einer Sandbox ausführen und nahe Alternativen vergleichen, bevor sie produktiv eingesetzt wird.
Qualitätsprofil
Vielversprechend Kandidat für Agent-Workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow-Eignung
Diese Skill in diesen Szenarien nutzen
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.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
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.
Alternativen-Shortlist
Vor Installation vergleichen
Similar skills that may fit this task.
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Infisical
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Übersicht
--- 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
Technische Details
- Version
- 1.0.0
- Lizenz
- AGPL-3.0
- Letzte Aktualisierung
- 23. Aug. 2026
- Veröffentlicht
- 23. Aug. 2026
Entscheidungsübersicht
Fallback-Kandidat
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 72/100
- Wartung
- 100/100
- Installieren
- 92/100
Von Agent belegte Evidenz
Von Agent belegte Evidenz
Ergebnisberichte nach Resolve, Prüfung, Installation und einem begrenzten Lauf.
- Erfolgsrate
- —
- Letzter Fehler
- —
- Ergebnisse
- 0
- Ausgabequalität
- —
- Fehlgeschlagen
- 0
- Nicht relevant
- 0
- Installationen
- 0
- Durch Risiko blockiert
- 0
- Einrichtung erforderlich
- 0
- Produktion
- 0
Noch keine Agent-Ergebnisdaten. Der erste Lauf kann Erfolg, Einrichtungsbedarf, Risikoblockaden, Fehler oder Irrelevanz über /api/agent/outcome melden.
Installieren
Zum Agent-Workflow hinzufügen
Kostenlos und Open Source. Bericht vor der Installation in Produktions-Agents prüfen.
Wachstums-Loop
Share-Kit
Szenariobasierter Entwurf für lt2md, bereit für einen manuellen X-Post.
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
Optionale Antwort mit Installationsbefehl
Listing + install path for lt2md: https://www.openagentskill.com/skills/libnyx-lt2md?ref=x Install: npx skills add libnyx/LT2MD --skill lt2md
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- libnyx
- Quelle
- libnyx/LT2MD
- Indexiert von
- OpenAgentSkill Community-Index
Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.
Diesen Skill beanspruchenEigentümeranspruch
Diesen Skill-Eintrag beanspruchen
Dieser Registry-indexiert-Eintrag wird libnyx zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.
Creator-Backlink-Kit
Evidenz-Badges in deine README einfügen
Zeige den kanonischen Eintrag, aktuelle Vertrauens- und Audit-Signale sowie echte Agent-Proven-Evidenz dort, wo Entwickler das Repository bewerten.
[](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)Autor
libnyx
@libnyx
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 33
- Qualitätswert
- 34/100
- Letzter GitHub-Push
- 23. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 1
- Installationskopien
- 0
- Externe Klicks
- 0
Community-Signal
Teile mit, ob dieser Skill für deinen Agent-Workflow nützlich ist. Zusammengefasstes Feedback verbessert das Ranking im Laufe der Zeit.
Vertrauen & Sicherheit
Nur Sandbox
- GitHub-Akzeptanz33 GitHub-StarsPrüfen
- Star-/Fork-Aktivität33 Stars und 1 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
- Aktuelle WartungHeute gepushtBestanden
- LizenzklarheitAGPL-3.0Bestanden
- README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
- Abhängigkeits-/Laufzeitrisikocredential or environment access, database surfaceInfo
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