Kagi monitoring

Skill Microck/kagi-cli/skills/kagi-monitoring

Automate repeated Kagi queries with batch search, watches, notifications, history, caching, and MCP. Use when the user needs bulk processing, scheduled discovery, change detection, or a stable integration surface.From its SKILL.md

Install

npx -y skills add Microck/kagi-cli --skill kagi-monitoring

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

SKILL.md

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Kagi Monitoring and Automation

Build repeatable workflows from structured Kagi output. Keep stdout machine-readable and send progress or diagnostics to stderr.

Choose the workflow

Need	Command
Run independent queries together	`kagi batch`
Detect changes in results	`kagi watch`
Deliver a result or alert	`kagi notify`
Inspect prior local activity	`kagi history`
Expose Kagi tools to an agent	`kagi mcp`

Run kagi auth status before automating authenticated commands.

Batch search

kagi batch "rust" "zig" "go" --format toon --limit 3
printf 'rust\nzig\ngo\n' | kagi batch --format compact

Use argument queries for short fixed sets and stdin for generated lists. Keep the query list as the recoverable input so a failed run can be repeated.

Watch search results

kagi watch "site:example.com release notes" --interval 300

Define what counts as a meaningful change before starting a long-running watch. Use a descriptive query and an interval that matches how often the source updates.

Notifications

Use kagi notify --help to select the configured delivery target, then connect it to a watch or batch result. Never put secrets in notification text or command history.

History and caching

Use kagi history to inspect local command records before duplicating work. Use --local-cache only for calls where stale data is acceptable. Never cache research that must reflect a current price, release, outage, or policy.

MCP

Use the stdio server when another agent host needs Kagi tools:

kagi mcp

Keep MCP stdout reserved for protocol messages. Treat a log written to stdout as a protocol-breaking bug.

Automation rules

Prefer json, compact, or toon over pretty.
Preserve nonzero exit codes instead of turning failure into empty output.
Keep credentials in Kagi auth storage or environment variables, never scripts.
Limit concurrency to what the endpoint and account can sustain.
Record enough input to reproduce a failed item.
Use a process supervisor or scheduler for long-running watches.

Completion criteria

Automation is complete when:

inputs and outputs have stable machine-readable shapes;
failures remain observable and retryable;
credential values never appear in scripts or logs;
cache and polling choices match the freshness requirement; and
long-running processes have a clear owner and stop condition.