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-optimizegit 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-optimize)<a href="https://agentmods.dev/skills/openclaw/clawhub/convex-optimize"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-optimize/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-optimize"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00023 | $0.00452 |
| Opus 5.5 | $0.00009 | $0.00181 |
| Sonnet 5.5 | $0.00005 | $0.00090 |
| Haiku 4.5 | $0.00002 | $0.00045 |
Grade A, and why
convex-optimize 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 1mo 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- convex-optimize — 100% identical, 0 lines differ
What it actually says
Audit and optimize an existing Convex app
The remediation WORKFLOW for an existing app: open with a scored assessment, then act on it — upgrade stale components and set up observability — plan-then-confirm-then-apply. The assessment itself is delegated to launch-readiness (the findings-bus scorer); optimize's distinct value is the actions it takes on the result.
Workflow
- Detect the app: a
convex/directory, the schema, and whether it's an anonymous or cloud deployment. - ASSESS via
launch-readiness— one scored, deduped report across authz/reviewer/advisor/insights with an ordered fix plan. Do not re-run those passes by hand; optimize consumes launch-readiness's report rather than re-implementing the audit. - UPGRADE: run
check-updatesagainst the pinned@convex-dev/*components and fold stale-component (staleness-class) findings into the same plan. - OBSERVABILITY: if the readiness report flagged an observability gap (no prod error capture), offer to install
sentinel. - Present the combined prioritized plan — the launch-readiness score + the fix plan + upgrades + observability, security/data-loss first — and apply only on explicit confirmation, dispatching each fix to its fixCapability.
- After applying, re-run the launch-readiness assessment and show the score delta.
Rules
- Read-only first. Present a plan and CONFIRM before changing any file.
- Delegate the audit to launch-readiness (the findings-bus scorer); don't re-implement reviewer/advisor/insights inline — optimize's job is acting on the report (upgrades + observability), not re-scoring.
- Prioritize security and data-loss risks above style, following launch-readiness's ordering.
- Never auto-land changes on someone's existing prod app; re-assess after applying and show the score moved.
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.
- 1mo ago First seen · 27 lines · 23 tokens per session scan A 58115c49778c
convex-optimize is a skill published in the GitHub repository openclaw/clawhub (9,526 stars, last pushed today), licensed MIT. It adds 23 tokens to every session and 452 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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