ai-assist-dockerize-website
Guide the user through containerizing and serving a simple website or documentation folder with Docker — inspects the project to detect what to serve (ready-to-serve static HTML, a buildable site that emits static output, or a raw markdown/docs folder that needs rendering), gener
Asset-Profil
Recherche und Wissensarbeit
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Szenario
Recherche-Agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent-Fit
Claude Code + Browser agents + CLI
Geeignet für Codex, Claude Code, Cursor, CLI oder benutzerdefinierte Agents.
Installieren
Bereit
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
Wartung
Aktuell
Heute gepusht
Risiko
Prüfung nötig
Lizenz ist unklar
GitHub-Qualität
88
61/100 Qualität · 62/100 Vertrauen
Abdeckungs-Tags
Review-Notizen
Lizenz ist unklar · Dependency or permission surface needs review
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
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Prüfung nötigMaschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
OpenAgentSkill Trust Score v5
Menschliche Prüfung vor Installation
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
88 GitHub-Stars
Repository-Aktivität
88 Stars und 12 Forks
Wartung
Heute gepusht
Lizenz
Unbekannt
Installieren
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
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
- Repository license is unknown, which creates ambiguity about the legal terms for reuse.
- Financial research output is not financial advice; require human review before any live investment decision.
- Lizenz ist unklar
- Quality score needs review
Installationsbereitschaft
Installationspfad verfügbar
- Installationspfad ist verfügbar
- Repository-Belege sind verfügbar
- Lizenz ist unklar
- 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
- Research-Agent-Workflows
- Claude-Code-Teams
- builders willing to evaluate younger projects
- Suchquellen
Geeignete Agents
Installationsentscheidung
- Befehl
- npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
- Richtlinie
- Blockieren
- Menschliche Prüfung
- Ja
Vertrauen und Risiko
- Vertrauen
- 54/100
- Audit
- 70/100
- Risikoebene
- Prüfung nötig
Ergebnis-Loop
- Endpoint
- /api/agent/outcome
- Event-ID
- resolve
- Ergebnisse
- 5
Installationsbefehl
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-websiteNicht verwenden, wenn
- Teams, die ein vom Anbieter unterstütztes SLA benötigen
- production agents without a repository review
- Repository license is unknown, which creates ambiguity about the legal terms for reuse.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
- Lizenz ist unklar
Alternative
Last30days Skill
53.5K Stars
npx skills add mvanhorn/last30days-skill -g
Alternative
Academic Research Skills
38.4K Stars
npx skills add Imbad0202/academic-research-skills
Alternative
GPT Researcher
28.0K Stars
npx skills add assafelovic/gpt-researcher
Alternative
DeepResearch
19.8K Stars
npx skills add Alibaba-NLP/DeepResearch
Agent-Sicherheit v2
26/100 · 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
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.
- Hinweise auf Hochrisiko-Berechtigungen: Shell or command execution, Secrets or environment access
- Lizenz ist unklar
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 jparkerweb-ai-assist-dockerize-websiteAgent-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%20ai-assist-dockerize-website%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve-Text
/api/agent/resolve?task=Use%20ai-assist-dockerize-website%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
Installationsübergabe
/api/skills/jparkerweb-ai-assist-dockerize-website/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 ai-assist-dockerize-website in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20ai-assist-dockerize-website%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-dockerize-website/install
Install command: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
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/jparkerweb-ai-assist-dockerize-website/install
LLM-Textformat
/api/skills/jparkerweb-ai-assist-dockerize-website/install?format=text
Alternativen finden
/api/skills/search?q=ai-assist-dockerize-website&limit=3
Agent-Prompt
Use ai-assist-dockerize-website for this task. Review https://www.openagentskill.com/api/skills/jparkerweb-ai-assist-dockerize-website/install, then install with: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-websiteRegistry-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/jparkerweb-ai-assist-dockerize-website
LLM-Text
/api/registry/manifest/jparkerweb-ai-assist-dockerize-website?format=text
Installationsalias
/api/registry/install/jparkerweb-ai-assist-dockerize-website
Empfehlen
/api/registry/recommend?task=Use%20ai-assist-dockerize-website%20in%20an%20agent%20workflow&limit=3
Agent-Fit
Recherche-Agents
Use-Case-Tags
Plattformen
Claude Code, Browser agents
Audit-Bericht
Prüfung nötig · 70/100
Maschinenlesbare Prüfung von Installationsbereitschaft, Sicherheitsmetadaten, Wartung und Akzeptanzrisiko.
Agent-Entscheidungspanel
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Rolle im Stack
Fallback-Kandidat
Primäre Eignung
Recherche-Agents
Vertrauenslabel
Zuerst prototypisieren
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 61/100
- 10 OpenAgentSkill-Interaktionen
zuerst prüfen
- Repository license is unknown, which creates ambiguity about the legal terms for reuse.
Implementierungspfad
- 1Installieren Sie es in einem Sandbox-Agent und führen Sie eine Recherche-Agents-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
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub-Akzeptanz
Prüfen88 GitHub-Stars
Star-/Fork-Aktivität
Prüfen88 Stars und 12 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbar
Aktuelle Wartung
BestandenHeute gepusht
Lizenzklarheit
PrüfenUnbekannt
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
- Repository license is unknown, which creates ambiguity about the legal terms for reuse.
- Financial research output is not financial advice; require human review before any live investment decision.
- Lizenz ist unklar
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 88 GitHub stars
- Stars/forks activity: 88 stars, 12 forks; issue activity unavailable in current metadata
- License clarity: Unknown
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Noch keine echten Agent-Ergebnisberichte
- Vor unbeaufsichtigter Installation ist menschliche Prüfung erforderlich
Empfohlene Aktion
Choose a stronger alternative or inspect the source manually before any install attempt.
Qualitätsprofil
Vielversprechend Kandidat für Agent-Workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow-Eignung
Diese Skill in diesen Szenarien nutzen
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Operate local tools
Local desktop
I need my agent to operate local files and desktop apps in a repeatable workflow.
Operate web apps
Browser automation
I need my agent to control a browser, fill forms, and verify web app workflows.
Workflow-Eignung
Zum vollständigen Workflow hinzufügen
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Operate and verify web apps
Browser QA agent
A workflow for agents that navigate products, fill forms, take screenshots, and verify real user flows across web applications.
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.
Last30days Skill
Research the last 30 days across Reddit, X, YouTube, Hacker News, Polymarket, GitHub, and the web, then synthesize a grounded brief for an AI agent.
Academic Research Skills
Academic Research Skills for Claude Code: research → write → review → revise → finalize
GPT Researcher
Run autonomous deep research over web and local sources
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Übersicht
--- name: ai-assist-dockerize-website description: "Guide the user through containerizing and serving a simple website or documentation folder with Docker — inspects the project to detect what to serve (ready-to-serve static HTML, a buildable site that emits static output, or a raw markdown/docs folder that needs rendering), generates a Dockerfile, .dockerignore, docker-compose.yml, helper run/build commands, and a 'Running with Docker' README section, then offers to build and smoke-test the container and publish the image to Docker Hub or GHCR. Uses nginx:alpine for static content and a multi-stage build when the site must be generated. Only invoke when the user explicitly types /ai-assist-dockerize-website. Never auto-trigger from general conversation about Docker, containers, websites, or Dockerfiles." argument-hint: "[path-to-site-or-docs-folder] [--port <port>]" ---
# Dockerize a Website
This skill is invoked **manually** — only when the user explicitly runs `/ai-assist-dockerize-website`. Don't auto-trigger it from general talk about Docker, containers, or websites.
Guide the user from a project folder to a working Docker container that serves their site or documentation. The skill is **interactive and guided**, not a one-shot script: inspect the project, propose a sensible plan, confirm a few details, generate the files, then offer to build/test and publish.
The spine is: **detect → confirm → generate → offer to test → offer to publish.** Lead with a good default at every step so the user is confirming, not configuring from scratch.
## When to use
Use this whenever the user wants to serve a *static* website or a *documentation* folder out of a container — loose HTML/CSS/JS, the build output of a site generator, or a folder of markdown docs. The "project with a docs folder" case is the bullseye.
## Out of scope — dynamic apps
This skill serves static content. If the project is a **dynamic app** that runs code per request — an Express/Fastify/Nest server, Next.js in SSR mode, Flask/Django/FastAPI, a Go/Rust web server, anything with a long-running `start`/`serve` process and a port it listens on — stop and say so plainly. Containerizing those means basing the image on the app's own runtime and running its start command, which is a different job. Detect this case (see below), tell the user, and don't half-build a static image that won't actually run their app.
Don't leave them stranded, though. After declining, offer a real next step: a correct container for a dynamic app is based on the app's own runtime (e.g. `node:22-alpine`), installs its dependencies, runs the start command, and exposes the port the app listens on. Offer to hand-write that separately — it's just outside this skill's static-hosting scope.
## Step 1 — Inspect and classify the project
Before asking anything, look at what's there so the proposal is concrete. If the user named a folder (argument or in their message), focus on it; otherwise scan the working directory.
Classify into one of four strategies:
| Signals found | Strategy | |---|---| | An `index.html` ready to serve — at the project root, or in `dist/`, `build/`, `out/`, `public/`, `_site/`, `site/`, `www/` | **A · Ready static** — single-stage nginx, copy the folder in | | Site-generator tooling: `package.json` with a `build` script + a static framework (Vite, Astro, Eleventy, Docusaurus, Gatsby, SvelteKit static), or a config file (`mkdocs.yml`, `docusaurus.config.*`, `astro.config.*`, `_config.yml`, `hugo.toml`/`config.toml`, `.eleventy.js`) | **B · Buildable** — multi-stage build → nginx | | A folder of `.md` docs with **no** generator configured | **C · Raw docs** — offer to render (recommended) or serve as-is | | A long-running server: `package.json` `start` runs a server (`node server.js`), source calls `.listen()`, or a web framework (Express, Next SSR, Flask, Django, FastAPI, Go/Rust server) | **D · Dynamic app** — stop, explain, don't build |
State your finding in one line — e.g. *"Found a built site in `dist/` with an `index.html`, so I'll serve it directly with nginx (strategy A)."* — and let the user correct you if the detection is off.
For **strategy B and C**, the build/render details live in `references/recipes.md`. Read that file when you land on those paths — it has the multi-stage Dockerfiles per generator (Node SSGs, MkDocs, Hugo, Jekyll), the SPA fallback config, the raw-markdown render path, caching headers, the non-root variant, and a Caddy alternative.
## Step 2 — Confirm the details
Pre-fill every answer from Step 1 so these are quick confirmations, not an interrogation:
- **What to serve** — the folder (strategy A) or the build output directory (strategy B/C). Show the path you detected. - **Port** — what host port to expose. Default `8080` (avoids clashing with anything already on `80`). Honor `--port` if given. - **Image / container name** — propose one lowercase-hyphenated name for both. Default to the **project folder** name, but prefer a more meaningful name when one is obvious — a `package.json` `name`, or a clear title in the README — e.g. favor `marketing-site` over a generic folder like `app` or `vite-app`. Always let the user override. - **Compose?** — yes by default (one-command up/down). Mention they can skip it. - For **strategy C**, confirm they want the docs *rendered* (recommended — browsers download raw `.md` instead of displaying it) versus served as raw files.
Skip questions whose answers are obvious or already given. The user invited a guided flow, not a form.
## Step 3 — Generate the artifacts
Generate all of these, tailored to the chosen strategy. Substitute the real folder, port, and names — don't leave placeholders in the files you write.
### Dockerfile (strategy A — ready static)
```dockerfile # syntax=docker/dockerfile:1 FROM nginx:1.27-alpine
# Copy the site into nginx's web root. COPY <SITE_DIR>/ /usr/share/nginx/html/
EXPOSE 80
# Fail the container's health check if nginx stops serving. # busybox wget ships in the alpine image, so no extra install is needed. HEALTHCHECK --interval=30s --timeout=3s --start-period=5s \ CMD wget -q --spider http://localhost/ || exit 1 ```
Pin the base image to a real minor tag (e.g. `nginx:1.27-alpine`) rather than the floating `nginx:alpine`, so a rebuild months from now doesn't silently pull a different nginx. Mention this so the user knows to bump it deliberately.
The build context is the project root (compose uses `build: .`), so `COPY` paths are relative to it: copy the served folder by its path from the root — `COPY public/ …` when the site lives in `public/`, or `COPY . …` when the site *is* the project root (then lean on `.dockerignore` to keep junk out).
For **strategy B/C**, use the matching multi-stage Dockerfile from `references/recipes.md` — a builder stage runs the generator, and only its static output is copied into the nginx stage, so build tooling never ships in the final image.
### .dockerignore
Keep the build context small and the image clean:
```gitignore .git .gitignore node_modules npm-debug.log* .env .env.* .DS_Store Thumbs.db Dockerfile* .dockerignore docker-compose*.yml ```
Tailor it to the project. For a multi-stage build (strategy B/C), also ignore the local build-output directory (`dist`, `build`, `_site`, `out`, `site`, …) — it's regenerated inside the image, and shipping a stale host copy into the build context only bloats it. The "don't ignore your content" rule is about *source* you serve directly (strategy A), not generated output.
### docker-compose.yml
```yaml services: web: build: . image: <IMAGE_NAME>:latest container_name: <CONTAINER_NAME> ports: - "<HOST_PORT>:80" restart: unless-stopped healthcheck: test: ["CMD", "wget", "-q", "--spider", "http://localhost/"] interval: 30s timeout: 3s retries: 3 start_period: 5s ```
No top-level `version:` key — it's obsolete in Compose v2 and prints a warning.
### Helper commands + README section
Append a "Running with Docker" section to the project's `README.md` (or create a short `DOCKER.md` if there's no README). The commands are identical in PowerShell and bash, so no per-shell variants are needed.
The block below is the template; its outer 4-backtick fence is only the boundary so the inner blocks display here. When you write the actual file, use normal **3-backtick** fences for the `bash` blocks — don't copy the 4-backtick wrapper.
````markdown ## Running with Docker
This site is served by nginx in a container.
### Quick start (Docker Compose)
```bash docker compose up -d --build # build the image and start in the background # open http://localhost:<HOST_PORT> docker compose logs -f # follow logs docker compose down # stop and remove ```
### Without Compose
```bash docker build -t <IMAGE_NAME> . docker run -d --name <CONTAINER_NAME> -p <HOST_PORT>:80 <IMAGE_NAME> docker stop <CONTAINER_NAME> && docker rm <CONTAINER_NAME> ``` ````
After writing the files, summarize what you created and the one command to run it.
## Step 4 — Offer to build and smoke-test
Don't build automatically — the user may not have Docker running, or may want to review the files first. Ask: *"Want me to build it and confirm it serves?"*
If yes:
1. Check the daemon is up first with `docker info`. If it fails, tell the user to start Docker Desktop and stop here — the files are already written and ready whenever they are. 2. Build and start: `docker compose up -d --build` (or `docker build` + `docker run` if they skipped compose). 3. Smoke-test the URL — request `http://localhost:<HOST_PORT>/` and confirm an HTTP 200 with non-empty HTML. On Windows use `curl.exe` or PowerShell's `Invoke-WebRequest`; give nginx a second to come up and retry once or twice before calling it a failure. 4. Report the result. Leave it running if they want to look at it, or tear down with `docker compose down`. If you started a throwaway container by hand, clean it up.
If the build or smoke-test fails, read the actual error (`docker compose logs`) and fix the real cause — a wrong output directory, a missing build step, a port already in use — rather than guessing.
## Step 5 — Offer to publish (optional)
Once it runs locally, offer to push the image to a registry so it can be shared or deployed. Only do this if the user wants it. Read `references/registry-publish.md` for the Docker Hub and GHCR walkthrough (login, tag, push, image naming, and the multi-arch `--platform` note for Apple-Silicon-built images headed to amd64 servers).
## Conventions and rationale
- **nginx:alpine for static** — tiny, battle-tested, zero app code to maintain. Reach for the Caddy alternative (in recipes) only when the user wants dead-simple config or automatic file serving. - **Multi-stage when building** — the final image carries only the rendered site, not Node/Python/Hugo and their caches. Smaller image, smaller attack surface. - **Pin the base image** to a minor tag so rebuilds are reproducible. - **Default to port 8080** on the host to avoid colliding with whatever already owns `80`. - **Non-root** is available via `nginxinc/nginx-unprivileged` (recipes) for stricter environments — note it as an option rather than forcing it. - **No secrets in the image** — static hosting rarely needs any; if the user mentions API keys or env config, that's a sign this is really a dynamic app (strategy D).
## Reference files
- `references/recipes.md` — multi-stage Dockerfiles per generator (Node SSGs, MkDocs, Hugo, Jekyll), the raw-markdown render path, SPA fallback, caching headers, non-root, and the Caddy alternative. Read it for strategy B or C. - `references/registry-publish.md` — pushing the image to Docker Hub or GHCR. Read it for Step 5.
Technische Details
- Version
- 1.0.0
- Lizenz
- Unknown
- Letzte Aktualisierung
- 22. Aug. 2026
- Veröffentlicht
- 21. Aug. 2026
Entscheidungsübersicht
Fallback-Kandidat
recent repository activity
Audit
Installationsprüfung
Installations- und Adoptionsprüfung
- Sicherheit
- 63/100
- 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 ai-assist-dockerize-website, bereit für einen manuellen X-Post.
ai-assist-dockerize-website: Guide the user through containerizing and serving a simple website or documentation folder wi... 88 stars https://www.openagentskill.com/skills/jparkerweb-ai-assist-dockerize-website?ref=x
Optionale Antwort mit Installationsbefehl
Listing + install path for ai-assist-dockerize-website: https://www.openagentskill.com/skills/jparkerweb-ai-assist-dockerize-website?ref=x Install: npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
Quelle des Eintrags
Registry-indexiert
Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.
- Ersteller
- jparkerweb
- 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 jparkerweb 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/jparkerweb-ai-assist-dockerize-website)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-dockerize-website)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-dockerize-website/audit)
[](https://www.openagentskill.com/skills/jparkerweb-ai-assist-dockerize-website)Autor
jparkerweb
@jparkerweb
Tags
Plattform-Fit
Gesundheitssignale
- GitHub-Stars
- 88
- Qualitätswert
- 37/100
- Letzter GitHub-Push
- 22. Aug. 2026
- Framework-Hinweise
- Unbekannt
- OpenAgentSkill-Aufrufe
- 10
- 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
Do not auto-install
- GitHub-Akzeptanz88 GitHub-StarsPrüfen
- Star-/Fork-Aktivität88 Stars und 12 Forks; Issue-Aktivität ist in den aktuellen Metadaten nicht verfügbarPrüfen
- Aktuelle WartungHeute gepushtBestanden
- LizenzklarheitUnbekanntPrüfen
- README/SKILL.md-VollständigkeitMetadaten enthalten ausreichend Nutzungs- und Workflow-KontextBestanden
- Abhängigkeits-/Laufzeitrisikocommand execution surface, credential or environment accessBeheben
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