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 agentmods add skills/openclaw/clawhub/convex-explain-appnpx skills add openclaw/clawhub --skill convex-explain-appgit 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-explain-app)<a href="https://agentmods.dev/skills/openclaw/clawhub/convex-explain-app"><img src="https://agentmods.dev/badge/skills/openclaw/clawhub/convex-explain-app.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.00914 |
| Opus 5 | $0.00023 | $0.00457 |
| Sonnet 5 | $0.00009 | $0.00183 |
| Haiku 4.5 | $0.00005 | $0.00091 |
Grade A, and why
convex-explain-app 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 6d 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-explain-app — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Explain this Convex app
Before you can safely change an app you have to know what it is — and reading 15 function files top-to-bottom is slow and error-prone. This capability produces the map fast and accurately by reading the two sources that can't lie: the schema (the data model) and the function surface (functionSpec / the exported queries/mutations/actions). It is deliberately DESCRIPTIVE — it explains what IS, hands judgment to the audit capabilities and changes to the fixers. It is also the natural first step of an optimize or self-heal session, and the reusable 're-explain the current architecture' that 'change what you built' depends on.
Workflow
- DETECT the app: the
convex/directory,schema.ts, and whether a deployment exists (if one does,functionSpec/tablesvia the official MCP give the authoritative live surface; if not, read the source directly). deploy-guard classifies any deployment read as read-only. - DATA MODEL: from
schema.ts, list every table with its fields and, crucially, its RELATIONSHIPS — whichv.id("other")fields point where, and which indexes exist (indexes reveal the intended access paths). Draw the foreign-key graph in words: 'tasks belong to projects (projectId) and users (ownerId); messages belong to conversations'. - FUNCTION SURFACE: enumerate every exported function, split PUBLIC (query/mutation/action — the attack/API surface) from INTERNAL (internalQuery/... — not client-reachable), and for each give a one-line 'what it does + what it touches'. The public/internal split is the single most important thing a newcomer needs and the thing source-skimming most often gets wrong.
- AUTH / OWNERSHIP MODEL: state how identity is established (auth.config.ts provider? a users table keyed by tokenIdentifier?) and how ownership is enforced (is there a requireOwner-style check? which field is the owner?). Say plainly if there is NO auth foundation — that is load-bearing context for anyone about to change the app. (Describe the model; do not audit it for holes — that's convex-authz.)
- COMPONENTS + EXTERNAL EDGES: list the
@convex-dev/*components installed (convex.config.ts) and what they provide, the HTTP routes (http.ts) and crons, and any external calls in actions (which APIs, which env vars). - FLOW: trace 1-2 representative end-to-end paths ('client calls createTask → validates → inserts into tasks scoped to the caller → listMyTasks reads it back by the by_owner index') so the reader sees the moving parts connected, not just catalogued.
- PRESENT as a scannable map (data model → public/internal functions → auth model → components/edges → a flow or two), accurate to the source. End by pointing at the next verbs: convex-reviewer/convex-authz to audit it, launch-readiness to score it, design/convex-expert to extend it. Never invent behavior the source doesn't show; if something is ambiguous, say so rather than guessing.
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.
- 6d ago First seen · 30 lines · 47 tokens per session scan A 908d349556d4
convex-explain-app is a skill published in the GitHub repository openclaw/clawhub (9,393 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 914 once invoked, about $0.0002 per session on Opus 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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