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
Supply asset profile
Research and knowledge work
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
Scenario
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Agent fit
Claude Code + Browser agents + CLI
Codex, Claude Code, Cursor, CLI, or custom agents.
Install
Ready
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
Maintenance
fresh
Pushed today
Risk
Needs review
License is unclear
GitHub quality
88
61/100 Quality · 62/100 Trust
Coverage tags
Review notes
License is unclear · Dependency or permission surface needs review
Agent adoption scorecard
Trust, audit, and install readiness at a glance
These scores combine public repository metadata, OpenAgentSkill review signals, maintenance freshness, and install readiness. They are a shortlist signal, not a replacement for human review.
Quality
PromisingUseful candidate, but compare it with alternatives before adopting.
Trust
Do not auto-installTrust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
Audit
Needs reviewA machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
OpenAgentSkill Trust Score v5
Human review before install
Choose a stronger alternative or inspect the source manually before any install attempt.
Stars
88 GitHub stars
Repo activity
88 stars, 12 forks
Maintenance
Pushed today
License
Unknown
Install
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
Install safety
standard package or runtime install path
Permission surface
secrets or environment access, shell or command execution
Agent outcomes
No agent outcome data yet
Docs
Strong README/SKILL.md context
Risk summary
Review before production
- 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.
- License is unclear
- Quality score needs review
Install readiness
Install path available
- Install path is available
- Repository evidence is available
- License is unclear
- No Agent Proven outcome evidence yet
Agent-readable metadata
Machine-readable decision data for this skill.
Use this block or the embedded JSON to decide whether an agent should install this skill, choose an alternative, or ask for human review first.
Suited tasks
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
- Search sources
Suited agents
Install decision
- Command
- npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-website
- Policy
- block
- Human review
- yes
Trust and risk
- Trust
- 54/100
- Audit
- 70/100
- Risk level
- Needs review
Outcome loop
- Endpoint
- /api/agent/outcome
- Event ID
- resolve
- Outcomes
- 5
Install command
npx skills add jparkerweb/ai-assist-skills --skill ai-assist-dockerize-websiteDo not use when
- teams that need a vendor-supported SLA
- production agents without a repository review
- Repository license is unknown, which creates ambiguity about the legal terms for reuse.
- High-risk permission hints: Shell or command execution, Secrets or environment access
- License is unclear
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Agent safety v2
26/100 · Avoid automatic install
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.
high
Shell or command execution
Skill metadata references terminal, CLI, shell, subprocess, or command execution workflows.
medium
Browser automation
Skill may drive a browser or interact with web pages.
medium
Network access
Skill likely fetches remote pages, APIs, repositories, or external services.
medium
Filesystem access
Skill may read or write project files, documents, generated artifacts, or local workspace state.
- High-risk permission hints: Shell or command execution, Secrets or environment access
- License is unclear
Install targets
Install this skill in your agent workflow
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
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 resolve plan
Let an agent verify fit before installing.
The Resolve API returns the selected skill, alternatives, safety policy, audit notes, install target, and copy-paste prompt an agent can follow without scraping this page.
Open JSON
/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
Install handoff
/api/skills/jparkerweb-ai-assist-dockerize-website/install
Agent should check
- 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.
Copy prompt
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 handoff
Give an agent the install path, not another directory page.
Use the public install endpoint to fetch the command, safety checklist, target prompts, and canonical links for this skill.
Install handoff
/api/skills/jparkerweb-ai-assist-dockerize-website/install
LLM text format
/api/skills/jparkerweb-ai-assist-dockerize-website/install?format=text
Find alternatives
/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 metadata
Agent-readable profile for automatic skill selection.
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
Manifest
/api/registry/manifest/jparkerweb-ai-assist-dockerize-website
LLM text
/api/registry/manifest/jparkerweb-ai-assist-dockerize-website?format=text
Install alias
/api/registry/install/jparkerweb-ai-assist-dockerize-website
Recommend
/api/registry/recommend?task=Use%20ai-assist-dockerize-website%20in%20an%20agent%20workflow&limit=3
Agent fit
Research agents
Use-case tags
Platforms
Claude Code, Browser agents
Audit report
Needs review · 70/100
A machine-readable review of install readiness, security metadata, maintenance, and adoption risk.
Agent decision cockpit
Fallback candidate for Research agents
Prototype with this skill first; keep a fallback candidate ready.
Role in stack
Fallback candidate
Primary fit
Research agents
Trust label
Prototype first
Install path
Command ready
Use when
- Research agents workflows
- Claude Code teams
- builders willing to evaluate younger projects
Evidence
- recent repository activity
- install command or GitHub repo available
- 61/100 quality profile
- 10 OpenAgentSkill engagement events
review first
- Repository license is unknown, which creates ambiguity about the legal terms for reuse.
Implementation path
- 1Install it in a sandbox agent and run one Research agents task end to end.
- 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.
Trust profile
Do not auto-install
Trust Score v5 found insufficient evidence for agent installation. Treat this as discovery material, not an executable recommendation.
GitHub adoption
CHECK88 GitHub stars
Stars/forks activity
CHECK88 stars, 12 forks; issue activity unavailable in current metadata
Recent maintenance
PASSPushed today
License clarity
CHECKUnknown
Good signals
- AI review approved
- Install path is available
- Repository evidence is available
- Recently maintained repository
- Install command has no obvious high-risk pattern
- Outcome loop is ready but needs first real agent run
Review before install
- 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.
- License is unclear
- 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
- No real agent outcome reports yet
- Human review required before unattended installation
Recommended action
Choose a stronger alternative or inspect the source manually before any install attempt.
Quality profile
Promising candidate for agent workflows
Useful candidate, but compare it with alternatives before adopting.
Workflow fit
Use this skill in these scenarios
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 fit
Add it to a complete workflow
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.
Alternative shortlist
Compare before you install
Similar skills that may fit this task.
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Academic Research Skills
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GPT Researcher
Run autonomous deep research over web and local sources
DeepResearch
Tongyi Deep Research, the Leading Open-source Deep Research Agent
Overview
--- 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.
Technical details
- Version
- 1.0.0
- License
- Unknown
- Last updated
- Aug 22, 2026
- Published
- Aug 21, 2026
Decision snapshot
Fallback candidate
recent repository activity
Audit
Install review
Install and adoption review
- Security
- 63/100
- Maintenance
- 100/100
- Install
- 92/100
Agent-proven evidence
Agent-proven evidence
Outcome reports after resolve, review, install, and one narrow run.
- Success rate
- —
- Recent failure
- —
- Outcomes
- 0
- Output quality
- —
- Failed
- 0
- Not relevant
- 0
- Installs
- 0
- Risk blocked
- 0
- Setup needed
- 0
- Production
- 0
No agent outcome data yet. The first agent run can report success, setup needs, risk blocks, failure, or not-relevant through /api/agent/outcome.
Install
Add to agent workflow
Free and open source. Review the report before installing into production agents.
Growth loop
Share kit
Scenario-led draft for ai-assist-dockerize-website, ready for a manual 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
Optional reply with install command
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
Listing source
Registry indexed
This listing was indexed from public sources and is not marked official until a maintainer claim is approved.
- Creator
- jparkerweb
- Indexed by
- OpenAgentSkill community index
Attribution links to the public repository or creator profile. Creators can claim the listing to update ownership signals.
Claim this skillOwner claim
Claim this skill listing
This Registry indexed listing is attributed to jparkerweb but is not marked official yet. Claim it to add a verified owner signal and make future launch, install, and audit updates easier to trust.
Creator backlink kit
Add the evidence badges to your README
Show the canonical listing, current trust and audit signals, and real Agent-Proven evidence where developers evaluate the repository.
[](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)Author
jparkerweb
@jparkerweb
Tags
Platform fit
Health signals
- GitHub stars
- 88
- Quality score
- 37/100
- Last GitHub push
- Aug 22, 2026
- Framework hints
- Unknown
- OpenAgentSkill views
- 10
- Install copies
- 0
- Outbound clicks
- 0
Community signal
Share whether this skill looks useful for your agent workflow. Aggregated feedback improves rankings over time.
Trust & safety
Do not auto-install
- GitHub adoption88 GitHub starsCHECK
- Stars/forks activity88 stars, 12 forks; issue activity unavailable in current metadataCHECK
- Recent maintenancePushed todayPASS
- License clarityUnknownCHECK
- README/SKILL.md completenessMetadata includes enough usage and workflow contextPASS
- Dependency/runtime riskcommand execution surface, credential or environment accessFIX
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