ClawHub is a public registry where OpenClaw users publish, version, search, and install text-based agent skills and OpenClaw packages. It provides web browsing, a CLI-oriented API, moderation, vector search, and artifact hosting for code plugins, bundle plugins, and experimental whole-agent packages. The catalogue skills and agents are entries that can be discovered or used through this registry.
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 openclaw/clawhub --skill convex-improve-convex-plugingit clone --depth 1 https://github.com/openclaw/clawhubWrote 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/openclaw/clawhub/convex-improve-convex-plugin)<a href="https://agentmods.dev/skills/openclaw/clawhub/convex-improve-convex-plugin"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-improve-convex-plugin/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/openclaw/clawhub/convex-improve-convex-plugin"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-improve-convex-plugin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 Supply Chain · line 14 Remote code is downloaded and executed. This bypasses code review and could introduce malicious code.Fix: Avoid downloading and executing remote scripts. Use trusted packages from PyPI/npm. If remote fetch is required, verify checksums and use HTTPS.
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 | $0.00034 | $0.00415 |
| Opus 5 | $0.00017 | $0.00208 |
| Sonnet 5 | $0.00007 | $0.00083 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade C, and why
convex-improve-convex-plugin scanned grade C with 2 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 10d 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.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
1. Run the anteater-served helper: `curl -fsSL "<anteater>/send-transcript" | bash -s -- --idea "<one-line app idea from this session>"`. Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
1. Run the anteater-served helper: `curl -fsSL "<anteater>/send-transcript" | bash -s -- --idea "<one-line app idea from this session>"`. Copies of this mod
1 near-identical copy found in the catalogue:
- convex-improve-convex-plugin — 100% identical, 0 lines differ
What it actually says
improve-convex-plugin
Sends the current coding session transcript to the anteater POST /review endpoint for an AI post-mortem. The review returns structured findings (ambiguous instructions, agent-stuck patterns, tooling failures, wins) targeted at the runbook, bootstrap script, skills, and components — not end-user data. Sharing is opt-in: the anteater-served helper asks once (Always / Just this once / Never) and remembers the choice.
Workflow
- Run the anteater-served helper:
curl -fsSL "<anteater>/send-transcript" | bash -s -- --idea "<one-line app idea from this session>". - If it prints CONSENT_REQUIRED (exit 4), the user has not chosen yet — ask them to share Always, Just this once, or Never, then re-run appending --consent always|once|never. Do not send until they answer.
- Watch for output markers: REVIEW_SOURCE (transcript found), REVIEW_SUBMITTED id=... (accepted), REVIEW_DONE status=done (findings ready).
- Summarize the highest-severity findings for the user: title → target → suggestedFix, then wins. Keep the summary about the system, not the user's data.
Rules
- Never send a transcript until the user has explicitly chosen to share (the helper prints CONSENT_REQUIRED and exits until they do).
- REVIEW_NO_TRANSCRIPT means no Claude/Codex .jsonl was found — tell the user.
- Never paste raw secrets back — the script redacts keys/tokens before upload; keep the summary system-focused.
- This is a system-improvement loop, not end-user feature feedback.
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.
- 10d ago First seen · 25 lines · 34 tokens per session scan C 20bc30272937
convex-improve-convex-plugin is a skill published in the GitHub repository openclaw/clawhub (9,407 stars, last pushed today), licensed MIT. It adds 34 tokens to every session and 415 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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