AlphaClaw is a browser-based management layer for OpenClaw, an AI-agent gateway, that guides setup and monitors running agents. It helps users configure multiple agents, manage integrations, recover gateway failures, and observe activity from one dashboard.
Borrowing it
Nothing to install: this file belongs to chrysb/alphaclaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/chrysb/alphaclaw/main/.agents/skills/railway-volume-to-docker/SKILL.mdgit clone --depth 1 https://github.com/chrysb/alphaclawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/chrysb/alphaclaw/railway-volume-to-docker)<a href="https://agentmods.dev/skills/chrysb/alphaclaw/railway-volume-to-docker"><img src="https://agentmods.dev/badge/skills/chrysb/alphaclaw/railway-volume-to-docker/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/chrysb/alphaclaw/railway-volume-to-docker"><img src="https://agentmods.dev/badge/skills/chrysb/alphaclaw/railway-volume-to-docker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Tool Misuse · line 94 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium MCP Rug Pull · line 80 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
- low Tool Misuse · line 80 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- low Tool Misuse · line 80 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- low Tool Misuse · line 80 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- low Tool Misuse · line 80 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00045 | $0.01047 |
| Opus 5 | $0.00023 | $0.00524 |
| Sonnet 5 | $0.00009 | $0.00209 |
| Haiku 4.5 | $0.00005 | $0.00105 |
Grade C, and why
railway-volume-to-docker scanned grade C with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
docker run --rm -v "$VOLUME_NAME":/data -v "$PWD":/backup alpine sh -lc "rm -rf /data/* /data/.[!.]* /data/..?* 2>/dev/null || true; cd /data && tar -xzf /backup/railway-data.tgz" How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Railway Volume To Docker
Copy a Railway service's /data volume into local Docker (openclaw-railway-template) so local testing uses real runtime data.
When to use
- User asks to test locally with "real data"
- User asks to sync Railway deployment data into Docker
- User asks to copy production usage/session/config state to local
Preconditions
- Railway CLI is installed and logged in.
- Local repo
~/Projects/openclaw-railway-templateexists. - Service uses
/dataas persistent root (ALPHACLAW_ROOT_DIR=/data). - User accepts that secrets/tokens may be copied locally.
Safety notes
- This process can copy sensitive data (
/data/.env, sessions, logs). - Never commit snapshot files.
- Prefer local secure machine only.
- Remove snapshot artifacts after restore.
Workflow
Run these commands from ~/Projects/openclaw-railway-template.
1) Confirm Railway target
railway status
Expected output should show project/environment/service linked to the intended deployment.
2) Verify remote volume path exists
railway ssh -- sh -lc "pwd && ls -la /data"
If /data is missing, stop and confirm service/container setup.
3) Build a remote snapshot archive
Use tolerant tar flags because live files can change during read.
railway ssh -- tar --warning=no-file-changed --ignore-failed-read -czf /tmp/railway-data.tgz -C /data .
railway ssh -- ls -lh /tmp/railway-data.tgz
4) Download snapshot (text-safe transfer)
Do not stream binary tar directly; use base64 to avoid transport corruption.
rm -f railway-data.tgz railway-data.tgz.b64
railway ssh -- base64 -w0 /tmp/railway-data.tgz > railway-data.tgz.b64
base64 -D -i railway-data.tgz.b64 -o railway-data.tgz
file railway-data.tgz
tar -tzf railway-data.tgz > /dev/null && echo "archive_ok"
On Linux, use base64 --decode railway-data.tgz.b64 > railway-data.tgz instead of base64 -D.
5) Restore snapshot into local Docker volume
docker compose up -d openclaw
CONTAINER_ID=$(docker compose ps -q openclaw)
VOLUME_NAME=$(docker inspect "$CONTAINER_ID" --format '{{range .Mounts}}{{if eq .Destination "/data"}}{{.Name}}{{end}}{{end}}')
docker compose stop openclaw
docker run --rm -v "$VOLUME_NAME":/data -v "$PWD":/backup alpine sh -lc "rm -rf /data/* /data/.[!.]* /data/..?* 2>/dev/null || true; cd /data && tar -xzf /backup/railway-data.tgz"
docker compose up -d openclaw
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 121 lines · 45 tokens per session scan C 8f818f0eda71
railway-volume-to-docker is a skill published in the GitHub repository chrysb/alphaclaw (1,474 stars, last pushed today), licensed MIT. It adds 45 tokens to every session and 1,047 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
kubernetes
Kubernetes operations expert for kubectl, pods, deployments, and debugging.
agent-bom
Open security platform for agentic infrastructure — broad scanning plus MCP discovery, CVEs, blast radius, SBOMs, CIS benchmarks (AWS, Azure, GCP, Snowflake), OWASP/NIST/MITRE compliance, AISVS v1.0, MAESTRO layer tagging, and vector database security checks. Use when the user mentions vulnerability scanning, MCP…
pentest-cloud-infrastructure
Cloud security posture management and container security assessment for AWS, Azure, GCP, and Kubernetes.
Pynchy Ops
Use when managing the pynchy service on the server — deploying changes, observing logs, checking service status, restarting the service, setting up GitHub auth, rebuilding the agent container, or running commands on the live Pynchy host. Also use when interacting with the LiteLLM proxy — investigating failed requests…
xcloud-docker-deploy
Skill "xcloud-docker-deploy" from Asif2BD/xCloud-Docker-Deploy-Skill, covering xcloud docker deploy v1.4.1, start here, how xcloud works, current xcloud api + agent skills context and api token flow.
azd-deployment
Deploy containerized frontend + backend applications to Azure Container Apps with remote builds, managed identity, and idempotent infrastructure.