linkedin-writer

Prüfen · 66
Im Registry indexiert

LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready

Verified installs0
Stars483
Version1.0.0
Qualität73/100 · Stark
Vertrauen66/100 · Nur Sandbox
Audit81/100 · Prüfung nötig

Asset-Profil

Recherche und Wissensarbeit

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

Bereich ansehen

Szenario

Recherche-Agents

I need my agent to research a topic, compare sources, and produce a concise report.

Agent-Fit

Claude Code + CLI + Codex

Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.

Installieren

Bereit

npx skills add flaqai/backlink_skills --skill linkedin-writer

Wartung

Aktuell

1 Tage seit dem letzten Push

Risiko

Prüfung nötig

Financial research output is not financial advice; require human review before any live investment decision

GitHub-Qualität

483

73/100 Qualität · 74/100 Vertrauen

Abdeckungs-Tags

RechercheRecherche-AgentsSicherheitagent-skill

Review-Notizen

Financial research output is not financial advice; require human review before any live investment decision · The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.

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

Stark
73

Solid option that is likely worth shortlisting for production workflows.

Vertrauen

Nur Sandbox
66

Nützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.

Audit

Prüfung nötig
81

Maschinenlesbare 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.

CodexClaude CodeCursorOpenAgentSkill CLI

Stars

483 GitHub-Stars

Repository-Aktivität

483 Stars und 175 Forks

Wartung

1 Tage seit dem letzten Push

Lizenz

MIT

Installieren

npx skills add flaqai/backlink_skills --skill linkedin-writer

Installationssicherheit

Standard-Paket- oder Laufzeit-Installationspfad

Berechtigungsfläche

Dateisystem- oder Dokumentzugriff

Agent-Ergebnisse

Noch keine Agent-Ergebnisdaten

Dokumentation

Starker README/SKILL.md-Kontext

Risikoübersicht

Vor Produktion prüfen

  • The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review

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.

JSON öffnen

Geeignete Aufgaben

  • Research-Agent-Workflows
  • Claude-Code-Teams
  • builders willing to evaluate younger projects
  • Suchquellen

Geeignete Agents

CodexClaude CodeCursorOpenAgentSkill CLICLI

Installationsentscheidung

Befehl
npx skills add flaqai/backlink_skills --skill linkedin-writer
Richtlinie
Prüfen
Menschliche Prüfung
Ja

Vertrauen und Risiko

Vertrauen
66/100
Audit
81/100
Risikoebene
Prüfung nötig

Ergebnis-Loop

Endpoint
/api/agent/outcome
Event-ID
resolve
Ergebnisse
5

Installationsbefehl

npx skills add flaqai/backlink_skills --skill linkedin-writer

Nicht verwenden, wenn

  • Teams, die ein vom Anbieter unterstütztes SLA benötigen
  • production agents without a repository review
  • The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
  • Financial research output is not financial advice; require human review before any live investment decision
  • The skill references external parent skill files (e.g., fact-check, humanization, R2 upload) that are not included in this submission; proper integration depends on those files being present.

Agent-Sicherheit v2

57/100 · Vor Installation prüfen

ExperimentellPrüfen

Sparse or mixed signals. Useful for discovery, but not for autonomous installation.

Test manually in an isolated workspace and compare against safer alternatives.

Per API auflösen

Mittel

Browser automation

Skill may drive a browser or interact with web pages.

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.

Mittel

Datenbankzugriff

Die Skill kann Schemata prüfen, Datenbanken abfragen oder mit persistenten Speichern arbeiten.

  • Financial research output is not financial advice; require human review before any live investment decision

Installationsziele

Diesen Skill im Agent-Workflow installieren

Über den öffentlichen Endpunkt erhältst du Befehl, Sicherheitscheckliste, Ziel-Prompts und kanonische Links.

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 flaqai-linkedin-writer

Agent-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.

Textplan öffnen

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 linkedin-writer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/flaqai-linkedin-writer/install
Install command: npx skills add flaqai/backlink_skills --skill linkedin-writer
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.

Installations-API öffnen

Agent-Prompt

Use linkedin-writer for this task. Review https://www.openagentskill.com/api/skills/flaqai-linkedin-writer/install, then install with: npx skills add flaqai/backlink_skills --skill linkedin-writer

Registry-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 öffnen

Agent-Fit

75/100

Recherche-Agents

Plattformen

Claude Code

Audit-Bericht

Prüfung nötig · 81/100

Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.

Audit-Bericht ansehenEval-Bericht ansehen

Agent-Entscheidungspanel

Companion skill for Research agents

Shortlist this skill and compare it with close alternatives before production adoption.

75
Bereitschaft
Shortlist
Phase

Rolle im Stack

Ergänzende Skill

Primäre Eignung

Recherche-Agents

Vertrauenslabel

Starke Shortlist

Installationspfad

Befehl bereit

Verwenden wenn

  • Research-Agent-Workflows
  • Claude-Code-Teams
  • builders willing to evaluate younger projects

Evidenz

  • recent repository activity
  • install command or GitHub repo available
  • Qualitätsprofil 73/100
  • 12 OpenAgentSkill-Interaktionen

zuerst prüfen

  • The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.

Implementierungspfad

  1. 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Recherche-Agents-Aufgabe vollständig aus.
  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.

Vertrauensprofil

Nur Sandbox

Nützlicher Kandidat mit fehlenden oder gemischten Vertrauenssignalen. Bis der Ergebniszyklus die Passung belegt, in einem isolierten Arbeitsbereich verwenden.

66
OpenAgentSkill Trust Score

GitHub-Akzeptanz

Info

483 GitHub-Stars

Star-/Fork-Aktivität

Info

483 Stars und 175 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar

Aktuelle Wartung

Bestanden

1 Tage seit dem letzten Push

Lizenzklarheit

Bestanden

MIT

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 provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
  • Financial research output is not financial advice; require human review before any live investment decision.
  • Quality score needs review
  • 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

Stark Kandidat für Agent-Workflows

Solid option that is likely worth shortlisting for production workflows.

73
GitHub-Stars
483
Aktualität
vor 1 Tagen
Installationsbereit
Ja
Lizenz
MIT
Vor Installation prüfen: The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.

Workflow-Eignung

Diese Skill in diesen Szenarien nutzen

Workflow-Eignung

Zum vollständigen Workflow hinzufügen

Alternativen-Shortlist

Vor Installation vergleichen

Similar skills that may fit this task.

Alle vergleichen

Übersicht

--- name: linkedin-writer description: LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包. ---

# LinkedIn Writer

## Goal

Turn a topic, product, argument, report, source bundle, or existing draft into a credible LinkedIn-native long-form article that helps a defined professional audience make a decision, understand a change, or improve how they work.

This skill reuses the parent `writer` workflow for fact checking, humanization, image packaging, and optional Cloudflare R2 delivery, but it does not treat LinkedIn as a generic SEO blog host or as Medium with a different publishing button.

LinkedIn-native writing prioritizes:

- a specific professional reader and work context; - a defensible point of view or useful decision framework; - current LinkedIn search and conversation signals; - expertise demonstrated through evidence, examples, and boundaries; - short, skimmable sections for busy readers; - a discussion-worthy close rather than a generic sales conclusion; - a complete native publishing pack, including LinkedIn SEO settings.

Default language follows the user's request. If the user gives no language, use the language of their source material or target audience.

## Format Routing

Use the requested format, not a blended default:

| Destination | Workflow | |---|---| | LinkedIn Article or LinkedIn newsletter edition | Use this skill in full | | LinkedIn short feed post only | Use the short-post rules and publishing pack in this skill; do not force a long article | | Google-first website article | Use `../SKILL.md` | | Medium article or third-party editorial essay | Use `../medium-writer/SKILL.md` | | Chinese WeChat Official Account article | Use `../wechat-writer/SKILL.md` |

If the user says only “LinkedIn article” or “LinkedIn long-form,” default to a native LinkedIn Article. If they already run a newsletter and provide its name or theme, package the piece as a newsletter edition. Do not claim a newsletter was created or published without direct evidence.

## Required References

Read each relevant file completely before acting:

- Topic discovery, LinkedIn search, trend expansion, and current seed topics: `references/linkedin-topic-research.md` - Google-to-LinkedIn discovery, business-depth enrichment, and final LinkedIn humanization: `references/linkedin-business-depth-and-humanization.md` - New article, rewrite, or reusable output format: `references/linkedin-article-template.md` - Article review, scoring, and revision gate: `references/linkedin-review-rubric.md` - Fact-heavy claims, comparisons, current products, or statistics: `../references/fact-check-and-style.md` - Final natural-language edit after factual and structural fixes: `../references/humanization.md` - Images, local paths, file packaging, and optional R2 delivery: `../references/output-packaging.md` - R2 upload tasks only: `../references/r2-image-upload.md` and `../references/r2-security.md`

Do not load unrelated references merely because they exist.

## Non-Negotiable Boundaries

1. Do not invent professional experience, product testing, customers, interviews, internal data, quotes, results, credentials, or events. 2. First-person events may appear only when the user supplied them for this task or they exist in an approved, attributable source package. 3. Do not turn LinkedIn search result counts, reactions, comments, or repeated phrases into search-volume claims. 4. Do not call a topic “trending,” “viral,” or “hot” without dated evidence. Use “recurring conversation,” “current topic seed,” or similarly bounded language when evidence is directional. 5. Do not mention or tag people and Pages merely to trigger notifications. Every suggested mention must have a content reason. 6. Do not convert a product announcement into disguised thought leadership. State affiliations, recommendations, and commercial relationships when they materially affect trust. 7. Do not use engagement bait such as “Agree?”, forced polls, empty controversy, or unrelated hashtags. Invite a concrete professional response. 8. Do not copy another LinkedIn creator's hook, framework, story, examples, distinctive phrases, or conclusion. Extract only topic signals and questions, then synthesize an original angle. 9. Do not claim publication, indexing, newsletter delivery, reach, or engagement from a completed local package. 10. Writing, generating images, uploading assets, and publishing externally are separate permission levels.

## LinkedIn Article Task Card

Before research or writing, create or infer a task card in 14 lines or fewer and save it as `linkedin-brief.md`:

- Publish as: personal profile / Company Page / unknown. - Format: standalone Article / newsletter edition / short feed post. - Professional audience: role, seniority, industry, and work situation. - Reader decision: what they should understand, compare, decide, or do. - Core thesis: one sentence the article must establish. - Expertise basis: supplied experience, verified sources, product knowledge, or editorial analysis. - Primary topic phrase: one natural phrase for LinkedIn and external search. - Related topic cluster: 4-8 entities, skills, problems, roles, or outcomes. - Conversation tension: trade-off, change, misconception, or unresolved question. - Business context: stakeholders, buying/approval path, economics, implementation, risk, and measurement dimensions that matter. - Evidence requirement: 3-6 claims that must be checked. - Target length: normally 900-1,800 words; adjust to the subject, not a platform myth. - CTA: discussion question, practical next step, subscription prompt, or disclosed product action.

Make conservative assumptions when details are missing. Ask only when audience, thesis, or authority to use personal experience is materially ambiguous.

## Working Modes

### Continuous mode (default)

Run `brief -> LinkedIn and Google-to-LinkedIn research -> business insight map -> evidence -> outline -> draft -> audit -> rewrite -> final humanization -> integrity recheck -> package` without pausing at every step. A request to “write an article” means deliver the reviewed local package.

### Topic-radar mode

When the user asks for hot topics, search ideas, or content planning, stop after the ranked topic map unless they also ask for an article. Do not draft ten shallow articles.

### Interactive mode

Pause at the topic shortlist or outline only when the user explicitly asks to choose first.

### Audit mode

If the user asks only for review, produce findings without overwriting the draft. If they ask to improve, preserve the original and apply fixes before re-auditing.

## End-to-End Workflow

### 1. Create an isolated article directory

Use:

```text writer/linkedin-writer/output/<article-slug>/ ```

This LinkedIn-specific output directory overrides the parent writer's default `writer/output/<article-slug>/` location. Prefer a short ASCII, hyphen-separated slug under 80 characters. Do not mix multiple campaigns in one directory.

Recommended working files:

```text linkedin-brief.md linkedin-topic-map.md linkedin-insight-map.md source-ledger.md outline.md draft.md article-linkedin.md linkedin-audit.md linkedin-publishing-pack.md image-plan.md ```

Create only the files the task needs. Keep `draft.md` separate from `article-linkedin.md` so an unreviewed draft cannot be mistaken for final copy.

### 2. Research LinkedIn search demand and conversation context

Read `references/linkedin-topic-research.md` completely.

Do not begin with a static list of broad trends. Build a query grid around:

```text core entity or skill × audience or role × work outcome × tension or decision × current change or timeframe ```

Use LinkedIn search suggestions and Posts results when available. Filter by recent date, content type, author industry/company, or source type when useful. Triangulate recurring questions with primary reports, official product or policy sources, credible industry research, customer questions, and the user's own content goals.

Then use Google to discover publicly indexed LinkedIn material with exact phrases, date operators, exclusions, and scoped queries such as:

```text site:linkedin.com/posts "<topic>" "<role or objection>" after:YYYY-MM-DD site:linkedin.com/pulse "<topic>" "<implementation, ROI, risk, or governance>" site:linkedin.com/company "<topic>" "<official case or report>" ```

Record Google results separately. Search snippets and LinkedIn creator claims are conversation signals, not automatically verified facts. Open the original page when possible, verify material claims elsewhere, and never copy a creator's hook, framework, structure, anecdote, or conclusion.

Record the exact query, date, filter, observed signal, and interpretation in `linkedin-topic-map.md`. Separate:

- **Observed:** directly visible search suggestion, repeated topic, question, format, or source. - **Inferred:** a possible reader need or angle derived from the observations. - **Verified demand:** use this label only when reliable demand data actually supports it.

Never imply that a topic is popular merely because it appears in one post or one search result.

### 3. Expand the topic before outlining

For the selected topic, create a useful professional topic cluster:

- Core concept: the named tool, skill, market shift, or decision. - Business outcome: time, quality, growth, cost, risk, hiring, retention, or customer value. - Role impact: what changes for practitioners, managers, executives, buyers, or candidates. - Implementation: workflow, prerequisites, governance, measurement, and failure modes. - Trade-off: what the popular framing misses or where the approach breaks. - Evidence: current data, official documentation, case material, or observable examples. - Adjacent conversation: 3-5 related topics that deepen the article without causing drift. - Discussion gap: a question qualified readers can answer from experience.

Reject adjacent topics that do not strengthen the thesis or reader decision. “More keywords” is not the same as more depth.

Read `references/linkedin-business-depth-and-humanization.md` and create `linkedin-insight-map.md`. Enrich the selected topic across the dimensions that materially affect the business decision:

- decision trigger and cost of waiting; - sponsors, users, approvers, blockers, buyers, and owners; - cost, budget, ROI, revenue, margin, or option value; - workflow, data, integration, adoption, and change management; - baselines, leading indicators, outcome metrics, and guardrails; - risk, strongest objection, failure mode, and reversibility; - one attributable or explicitly hypothetical scenario; - the next artifact, meeting, pilot, or decision the reader should initiate.

For a substantial business article, normally develop at least five relevant dimensions. Do not force irrelevant finance or governance sections into a career essay, but do not omit a material stakeholder, cost, or risk merely to keep the article simple.

### 4. Build the claim-source ledger

Create `source-ledger.md` for any current, factual, comparative, or decision-shaping article.

For each material claim, record:

- claim ID and exact claim; - claim type: fact / inference / editorial judgment / user-provided experience; - source title, publisher, date, and URL; - status: `verified`, `user_provided`, `needs_verification`, `softened`, `removed`, or `unsupported`; - where it will appear; - caveat or expiry risk. - rese

Technische Details

Version
1.0.0
Lizenz
MIT
Letzte Aktualisierung
21. Aug. 2026
Veröffentlicht
21. Aug. 2026

Entscheidungsübersicht

Ergänzende Skill

75
Bereit
Shortlist
Phase

recent repository activity

Audit

Installationsprüfung

Installations- und Adoptionsprüfung

81
Prüfung nötig
Sicherheit
82/100
Wartung
100/100
Installieren
92/100
Vollständiges Audit öffnenEval-Bericht ansehen

Von Agent belegte Evidenz

Von Agent belegte Evidenz

Ergebnisberichte nach Resolve, Prüfung, Installation und einem begrenzten Lauf.

0
Belegt
Needs first agent runAuto-Installation: zuerst prüfenLetzter: Unbekannt
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

X

Szenariobasierter Entwurf für linkedin-writer, bereit für einen manuellen X-Post.

Kuratorenhinweis
linkedin-writer: LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-...

483 stars

https://www.openagentskill.com/skills/flaqai-linkedin-writer?ref=x
X-Entwurf öffnen
Optionale Antwort mit Installationsbefehl
Listing + install path for linkedin-writer:
https://www.openagentskill.com/skills/flaqai-linkedin-writer?ref=x

Install: npx skills add flaqai/backlink_skills --skill linkedin-writer
Antwortentwurf öffnen

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller
flaqai
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 beanspruchen

Eigentümeranspruch

Diesen Skill-Eintrag beanspruchen

Dieser Registry-indexiert-Eintrag wird flaqai 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.

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

Autor

F

flaqai

@flaqai

Plattform-Fit

Gesundheitssignale

GitHub-Stars
483
Qualitätswert
42/100
Letzter GitHub-Push
21. Aug. 2026
Framework-Hinweise
Unbekannt
OpenAgentSkill-Aufrufe
12
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

66
  • GitHub-Akzeptanz483 GitHub-StarsInfo
  • Star-/Fork-Aktivität483 Stars und 175 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarInfo
  • Aktuelle Wartung1 Tage seit dem letzten PushBestanden
  • LizenzklarheitMITBestanden
  • README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
  • Abhängigkeits-/LaufzeitrisikoKeine wesentlichen Abhängigkeitsrisikohinweise in öffentlichen MetadatenBestanden