Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add deepgram/dglabs-deepclaw --skill healthcheckgit clone --depth 1 https://github.com/deepgram/dglabs-deepclawWrote 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/deepgram/dglabs-deepclaw/healthcheck)<a href="https://agentmods.dev/skills/deepgram/dglabs-deepclaw/healthcheck"><img src="https://agentmods.dev/badge/skills/deepgram/dglabs-deepclaw/healthcheck/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/deepgram/dglabs-deepclaw/healthcheck"><img src="https://agentmods.dev/badge/skills/deepgram/dglabs-deepclaw/healthcheck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00070 | $0.02401 |
| Opus 5 | $0.00035 | $0.01201 |
| Sonnet 5 | $0.00014 | $0.00480 |
| Haiku 4.5 | $0.00007 | $0.00240 |
Grade A, and why
healthcheck scanned grade A with 0 findings 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 9d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to healthcheck — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 246 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenClaw Host Hardening
Overview
Assess and harden the host running OpenClaw, then align it to a user-defined risk tolerance without breaking access. Use OpenClaw security tooling as a first-class signal, but treat OS hardening as a separate, explicit set of steps.
Core rules
- Recommend running this skill with a state-of-the-art model (e.g., Opus 4.5, GPT 5.2+). The agent should self-check the current model and suggest switching if below that level; do not block execution.
- Require explicit approval before any state-changing action.
- Do not modify remote access settings without confirming how the user connects.
- Prefer reversible, staged changes with a rollback plan.
- Never claim OpenClaw changes the host firewall, SSH, or OS updates; it does not.
- If role/identity is unknown, provide recommendations only.
- Formatting: every set of user choices must be numbered so the user can reply with a single digit.
- System-level backups are recommended; try to verify status.
Workflow (follow in order)
0) Model self-check (non-blocking)
Before starting, check the current model. If it is below state-of-the-art (e.g., Opus 4.5, GPT 5.2+), recommend switching. Do not block execution.
1) Establish context (read-only)
Try to infer 1–5 from the environment before asking. Prefer simple, non-technical questions if you need confirmation.
Determine (in order):
- OS and version (Linux/macOS/Windows), container vs host.
- Privilege level (root/admin vs user).
- Access path (local console, SSH, RDP, tailnet).
- Network exposure (public IP, reverse proxy, tunnel).
- OpenClaw gateway status and bind address.
- Backup system and status (e.g., Time Machine, system images, snapshots).
- Deployment context (local mac app, headless gateway host, remote gateway, container/CI).
- Disk encryption status (FileVault/LUKS/BitLocker).
- OS automatic security updates status. Note: these are not blocking items, but are highly recommended, especially if OpenClaw can access sensitive data.
- Usage mode for a personal assistant with full access (local workstation vs headless/remote vs other).
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.
- 9d ago First seen · 246 lines · 70 tokens per session scan A adba53ffae39
healthcheck is a skill published in the GitHub repository deepgram/dglabs-deepclaw (23 stars, last pushed 4mo ago), licensed MIT. It adds 70 tokens to every session and 2,401 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to healthcheck, differing in 0 lines, and is treated as a copy.
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