computer-health-check

Set up, connect, validate, and repair Claude Desktop and MCP servers using Desktop Commander — inspecting and fixing mcp.json / claude_desktop_config.json, getting MCP servers to connect, and diagnosing local AI tooling. Use when a user wants to install, configure, connect, validate, or fix Claude Desktop, an MCP server, an MCP config file, or a local AI agent/gateway. Trigger on the symptoms that actually bring people in: "Claude can't see my tools", "my MCP server isn't showing up", "tools aren't loading in Claude", "add an MCP server", "my MCP isn't connecting", "fix my Claude Desktop config", "the agent won't respond", "wire up Ollama / LM Studio", "restart Claude Desktop". Also covers local agent gateways like OpenClaw and Hermes.

Use for Desktop Commander MCP capabilities — persistent shells and REPLs, long-running processes, filesystem beyond the workspace, structured files (.xlsx, .docx, .pdf, images) and large local data files such as CSVs, ripgrep search at scale, SSH, or cross-turn state.

Create and maintain a Markdown knowledge base that any AI agent can read, search, and update. Use when the user wants to start a knowledge base, add or update notes, organize docs/notes for an agent or LLM to consume, build an index of notes, or run a cleanup/maintenance pass on an existing MD knowledge base. Triggers include "knowledge base", "KB", "notes for the agent", "index of notes", "second brain", "docs for AI context", "add a note", "update the KB".

Organize Obsidian vaults with MOCs, wikilinks, frontmatter/properties, dashboards, orphan-note checks, and cleanup workflows. Use ONLY when the user explicitly refers to Obsidian — by naming "Obsidian", an Obsidian "vault", or an Obsidian-specific feature such as wikilinks ("[[ ]]"), the Dataview or Bases plugins, or Obsidian Properties. Within that Obsidian context it covers creating Maps of Content, adding/fixing wikilinks, finding orphan notes, normalizing YAML frontmatter, generating Dataview/Bases dashboards, organizing folders, deduplicating or renaming notes, and preparing the vault for AI use. Do NOT use this skill for generic note-taking, Markdown, or knowledge-base requests that don't mention Obsidian.

Manage and search AIDP audit logs — enable/disable auditing, set retention, and query audit-log entries for a DataLake. Use when the user asks about audit logging, who did what, compliance/retention of AIDP activity, or wants to search audit events. Self-contained — official `aidp audit` CLI preferred, `oci raw-request` fallback.

Read Snowflake from an AIDP notebook through the AIDP `aidataplatform` Spark format handler. Use when the user mentions Snowflake, Snowflake warehouse, Basic authentication, KeyPair authentication, private.key.file, or private.key.content. Snowflake writes are not supported in AIDP 4.0.

First-run setup for the Snowflake to AIDP migrator. Finds or creates the one migration config file (snowmig-config.yaml), reads every field back with secrets masked, verifies Snowflake authentication by password, key-pair, programmatic access token or SSO, verifies the AIDP end, then smoke-tests the connection and reports the account, region, role and warehouse. Use the first time the migrator runs on a machine, or when any other stage fails with an authentication, connection or "no config found" error.

Read-only PREVIEW of a Snowflake environment from the operator's machine - inventory of tables and views with row counts, byte sizes and column types, plus the census of objects that are not tables or views, table-maintenance state and security posture. Use ONLY when the user wants to look at an account without migrating it - answering what is in there, how big the tables are, what the security posture looks like. This is NOT the discovery step of a migration: a migration discovers inside AIDP as a workflow (runbook S6), because a laptop-side read leaves no log and no evidence on the platform. If the user asked to migrate, route to snowflake-migrator-overview and follow S1 through S12.

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.

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.

Audit a website for WCAG 2.1 AA accessibility violations using automated scanning and Noibu traffic data, and optionally draft the fixes. Use when you want to know what's inaccessible across the store, check WCAG or ADA compliance, find contrast or heading errors, audit for screen reader support, generate an accessibility report, or fix accessibility violations with pull requests or tickets.

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.