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-optimizenpx 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.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 | $0.00023 | $0.00452 |
| Opus 5 | $0.00012 | $0.00226 |
| Sonnet 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 4d 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.
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.
- 4d ago First seen · 27 lines · 23 tokens per session scan A 58115c49778c
convex-optimize is a skill published in the GitHub repository openclaw/clawhub (9,391 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. 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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