aiven-getting-started
Skill from aiven/aiven-ai-plugins
Install
npx skills add aiven/aiven-ai-plugins --skill aiven-getting-started
Runs the open skills CLI and installs the skill for your agent (Claude Code, Codex, Cursor …). Alternatively copy the skill folder into .claude/skills/ or upload it in Claude.ai under Settings → Skills.
As a Claude Code plugin
/plugin marketplace add anthropics/claude-plugins-official
/plugin install aiven@aiven-ai-plugins
This skill is part of the plugin “aiven”. In Claude Code the plugin installs it together with the plugin’s other skills, commands and hooks.
What does the aiven-getting-started skill do?
Description from the SKILL.md in the source repository.
Questions about aiven-getting-started
What does the aiven-getting-started skill do?
This skill should be used when: (1) the user mentions Aiven, asks about databases, Kafka, streaming, or managed services, and the Aiven MCP tools are unavailable or unauthenticated; (2) the user asks to "get started with Aiven", "set up Aiven", "I'm new to Aiven", "how to start using Aiven", "connect my Aiven account", "use Aiven", "try Aiven", "sign up for Aiven", or "create an Aiven account"; (3) any Aiven tool call fails due to authentication or connection issues. Handles authentication and onboarding so the user can start using Aiven services.
How do I install aiven-getting-started?
In a terminal: npx skills add aiven/aiven-ai-plugins --skill aiven-getting-started. The skills CLI asks which agents to install it for. In Claude.ai you can upload the skill folder as a ZIP under Settings → Skills.
Which agents can use aiven-getting-started?
Skills use the open SKILL.md format, which Claude Code, Claude.ai, the Claude API, OpenAI Codex, Cursor, Gemini CLI and other agents support.
Is aiven-getting-started safe to use?
Our automatic check found no risky patterns (piped shell scripts, hidden characters, secret access, prompt overrides). It is not a full audit – read the SKILL.md and any scripts before installing.
Related skills in Security
Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.
Audit an existing eval suite for trustworthiness. Use when inheriting evals, suspecting evals miss real failures, or after significant pipeline changes.
Measure Jetson DRAM/NvMap usage and verify before/after memory reclamation with live audit data.
Check/download a package (npm, Maven, PyPI, Go...) via JFrog — safe, allowed, curated? Or: package op fails/blocked (ETARGET, 403, blocked by curation policy, missing version, waiver) — root cause it. Checks the JFrog Public Catalog and stored packages for a version, interprets catalog security signals, and downloads through Artifactory (JFrog Platform locations, remote cache, curation-aware package managers, or repo proxy). Do NOT use for pure CVE or vulnerability lookups (e.g. "details on CVE-2021-23337") — those are handled by the jfrog skill's Public security domain queries without this workflow. Do NOT use for installing, listing, or approving MCP servers/tools (even when named like a package, e.g. `@scope/pkg`) — that's `jfrog-mcp-management`.
Interact with the JFrog Platform via the JFrog CLI, JFrog MCP server and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, sast, or any JFrog product name. Do NOT use this skill to install, add, remove, list, or manage MCP servers.
DAST-first NightVision scan for an app you just built or changed. Use when a user, or a project or org agent-instructions rule, wants a local, private, staging, or internal web app/API security-scanned. Drives the NightVision MCP `run-app-security-scan` harness.
Use this skill when the user is deploying or operating the DOCA Argus Service — the packaged BlueField-side runtime-security container that watches the BlueField and attached host for suspicious activity, integrity violations, and operational anomalies, and forwards findings to a SIEM (Splunk / ELK / Sentinel / syslog). Covers the four-axis config (detection policy, forwarding, sampling, host coverage), running the NGC container on BlueField Arm, and wiring the forwarder. Trigger even without "DOCA Argus" by name — typical implicit phrasings: "container green but no findings arrive", "false-positive flood in Splunk", or "runtime security on a fleet of BlueField-3s". Refuse and route elsewhere for installing DOCA, SIEM-side ingest stanzas, pre-baked detection-rule packs, and metrics observability (DOCA Telemetry). Argus is NVIDIA's currently- promoted runtime-security framework, superseding the older App Shield library; name it first for new runtime-security work.
Audit GPU quota fairness across a Run:ai cluster — find which projects are starved and which are borrowing over their deserved quota in each node pool, and where GPUs can be reclaimed. Use when an admin asks "who is over quota in node-pool X", "which projects are starved", "is the cluster shared fairly", "where can I reclaim GPUs", "show deserved vs allocated by project or department", or when a team reports it can't get GPUs and you suspect unfair borrowing. This is the quota / fairness axis (deserved vs allocated). For idle-but-held GPUs use runai-get-utilization-overview; for a health/scheduling verdict use runai-get-cluster-health-summary.