Vercel Optimize Audit
vercel-labs/agent-skills
Runs a metrics-first audit of a deployed Vercel project, gating investigations on real signals to produce ranked, citation-backed cost and performance recommendations.
Audit and optimize an existing Convex app: security, scale, upgrades, observability.
Install Convex Optimize
$ npx skills add openclaw/clawhub --skill convex-optimize -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install openclaw/clawhub convex-optimize --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/convex-optimize .claude/skills/convex-optimize && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "convex-optimize" agent skill from https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize into .claude/skills/convex-optimize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "convex-optimize", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimizeType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add openclaw/clawhub --skill convex-optimize -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install openclaw/clawhub convex-optimize --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.agents/skills/convex-optimize .agents/skills/convex-optimize && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "convex-optimize" agent skill from https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize into .agents/skills/convex-optimize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "convex-optimize", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add openclaw/clawhub --skill convex-optimize -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install openclaw/clawhub convex-optimize --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.agents/skills/convex-optimize .cursor/skills/convex-optimize && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "convex-optimize" agent skill from https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize into .cursor/skills/convex-optimize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "convex-optimize", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/openclaw/clawhub.git --path .agents/skills/convex-optimize--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add openclaw/clawhub --skill convex-optimize -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install openclaw/clawhub convex-optimize --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.agents/skills/convex-optimize .gemini/skills/convex-optimize && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "convex-optimize" agent skill from https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize into .gemini/skills/convex-optimize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "convex-optimize", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install openclaw/clawhub convex-optimizeInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add openclaw/clawhub --skill convex-optimize -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .github/skills && cp -r skills-src/.agents/skills/convex-optimize .github/skills/convex-optimize && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "convex-optimize" agent skill from https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize into .github/skills/convex-optimize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "convex-optimize", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add openclaw/clawhub --skill convex-optimize -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install openclaw/clawhub convex-optimize --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/openclaw/clawhub.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.agents/skills/convex-optimize .opencode/skills/convex-optimize && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "convex-optimize" agent skill from https://github.com/openclaw/clawhub/tree/main/.agents/skills/convex-optimize into .opencode/skills/convex-optimize/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "convex-optimize", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
convex-optimize- GitHub stars
- 9.5k
- Used in
- 1 other repo
- Token cost
- ~509 tokens
- SKILL.md length
- 238 words
- Files
- 1
- Skills in repo
- 57
- Repo updated
- First seen
- Licence
- MIT
At a glance
Audit and optimize an existing Convex app: security, scale, upgrades, observability.
- Works in 6 steps: Detect the app: a convex/ directory, the… → ASSESS via launch-readiness — one… → UPGRADE: run check-updates against the… → …
- Tasks that involve Observability
- SKILL.md covers Workflow and Rules
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Convex Optimize is an agent skill from openclaw/clawhub. Audit and optimize an existing Convex app: security, scale, upgrades, observability.
Its SKILL.md is about 510 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in DevOps & Cloud, covering Observability. The repository describes itself as: Skill + Plugin Registry for OpenClaw. The licence is MIT.
When your agent uses it
- Tasks that involve Observability
Example prompts
- “/convex-optimize”
Workflow steps
6 steps, taken from the first numbered list in SKILL.md.
- 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…
- UPGRADE: run check-updates against the pinned @convex-dev/* components and fold stale-component (staleness-class) findings into the same…
- 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…
- After applying, re-run the launch-readiness assessment and show the score delta.
What it can do on your machine
Read from SKILL.md and the folder at commit a18bc74. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Convex Optimize loads about 509 tokens when it runs. Until then it costs about 25 tokens; SKILL.md has 238 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from openclaw/clawhub at commit a18bc74, republished under its MIT licence (© openclaw). 238 words, ~509 tokens.
Download SKILL.mdSave it as
.claude/skills/convex-optimize/SKILL.md (or your agent's skills folder).- name
- convex-optimize
- description
- Audit and optimize an existing Convex app: security, scale, upgrades, observability.
<!-- GENERATED from convex-agents content/capabilities/optimize.json — do not edit by hand. -->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.
© openclaw, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
Just SKILL.md in .agents/skills/convex-optimize of openclaw/clawhub.
Open the folder on GitHubat commit a18bc74
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in openclaw/clawhub, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Convex Optimize next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Convex Optimize this skillopenclaw/clawhub | 9.5k | 1 repos | ~509 | Automated safety check: Pass | MIT | |
| Vercel Optimize Auditvercel-labs/agent-skills | 32k | 8 repos | ~4.3k | Automated safety check: Pass | None | |
| Kubeshark Installerkubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Notes | Apache-2.0 | |
| Kubeshark KFL2 Filter Referencekubeshark/kubeshark | 12k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| KubeSphere ServiceMesh Managerkubesphere/kubesphere | 17k | — | ~2.4k | Automated safety check: Pass | Custom licence | |
| Kubernetes Network Root Cause Analysiskubeshark/kubeshark | 12k | — | ~5.3k | Automated safety check: Pass | Apache-2.0 |
Similar skills
- Official
Kubeshark Installer
kubeshark/kubeshark
Installs and configures Kubeshark on a Kubernetes cluster, choosing between the quick CLI path and a Helm install with custom values.
Kubeshark KFL2 Filter Reference
kubeshark/kubeshark
Syntax reference for KFL2, the CEL-based display filter language used to search Kubernetes network traffic captured by Kubeshark, loaded before any filter is written.
KubeSphere ServiceMesh Manager
kubesphere/kubesphere
Installs, checks and troubleshoots the KubeSphere ServiceMesh extension (Istio, Kiali, Jaeger), including grayscale release, sidecar injection, topology and tracing issues.
Kubernetes Network Root Cause Analysis
kubeshark/kubeshark
Investigates past Kubernetes incidents from Kubeshark traffic snapshots: takes captures, dissects API calls, extracts PCAPs and compares traffic over time.
Caveman Gateway Setup
JuliusBrussee/caveman
Routes every LLM call in a repository through the Caveman Cloud gateway in record mode, so requests and costs are measured without changing behavior.
More from openclaw/clawhub
All 57 skills in this repoAxiom Alerting Management
openclaw/clawhub
Creates and manages Axiom monitors and notifiers end to end through the v2 API, with scripts for each CRUD operation and a recommended create-validate-tune workflow.
Axiom Dashboard Builder
openclaw/clawhub
Designs and deploys Axiom dashboards through the API, choosing chart types and writing APL or metrics queries, with templates and migration notes for Splunk and Grafana.
Axiom Cost Control
openclaw/clawhub
Finds unused data in Axiom by analyzing query patterns, then deploys a cost dashboard and ingest monitors to keep spend under the contract limit.
Axiom Metrics Query
openclaw/clawhub
Explores and queries OpenTelemetry metrics in Axiom MetricsDB, listing datasets, metrics and tags first and picking the right aggregation for each metric's type.
Axiom SRE Investigator
openclaw/clawhub
Investigates incidents and production problems with hypothesis-driven debugging, queries Axiom observability data when available, and keeps secrets out of commands and output.
Axiom Eval Writer
openclaw/clawhub
Scaffolds evaluation suites for the Axiom AI SDK: eval files, scorers, flag schemas and axiom.config.ts, generated from plain descriptions of an AI capability.
Related
Categories
Questions about Convex Optimize
What does Convex Optimize do?
Audit and optimize an existing Convex app: security, scale, upgrades, observability. Convex Optimize is an agent skill from openclaw/clawhub. Audit and optimize an existing Convex app: security, scale, upgrades, observability.
When should I use Convex Optimize?
Convex Optimize fits situations like: tasks that involve Observability.
How do I install Convex Optimize in Claude Code?
Run `npx skills add openclaw/clawhub --skill convex-optimize -a claude-code`. Or copy the skill folder (.agents/skills/convex-optimize in openclaw/clawhub) into .claude/skills/convex-optimize in your project. Claude Code loads it when a task matches its description.
How do I install Convex Optimize in Codex?
Run `npx skills add openclaw/clawhub --skill convex-optimize -a codex`. Or copy the skill folder (.agents/skills/convex-optimize in openclaw/clawhub) into .agents/skills/convex-optimize in your project. Codex loads it when a task matches its description.
Can I use Convex Optimize in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openclaw/clawhub --skill convex-optimize -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/convex-optimize, .gemini/skills/convex-optimize, .github/skills/convex-optimize and .opencode/skills/convex-optimize in your project.
What does Convex Optimize need to run?
SKILL.md names no scripts, command-line tools or credentials: Convex Optimize is instructions for the agent only.
Does Convex Optimize access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Convex Optimize safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Convex Optimize use?
Convex Optimize is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Convex Optimize use?
About 509 tokens (SKILL.md is roughly 2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
What are the alternatives to Convex Optimize?
Skills that share tags, products or a category with Convex Optimize: Vercel Optimize Audit (vercel-labs/agent-skills, 32k stars), Kubeshark Installer (kubeshark/kubeshark, 12k stars), Kubeshark KFL2 Filter Reference (kubeshark/kubeshark, 12k stars) and KubeSphere ServiceMesh Manager (kubesphere/kubesphere, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Convex Optimize?
openclaw (a GitHub organization) maintains it in openclaw/clawhub, which has 9,489 GitHub stars. The repository holds 57 skills in this directory. The repository was last updated on October 6, 2026.
Source: openclaw/clawhub on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.