Cloud, CI/CD, monitoring, and deployment (6.8K skills)
Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.
Monitor a swarm, a group of agents, and receive notifications when its status or agent count changes. A watcher script checks status periodically and streams changes through Monitor; separate tools provide one-time status and health checks.
Run a ruflo agent on your ruOS cloud desktop through an authenticated connection. Follow its output as it runs, record execution status in ruflo, and release the task claim when the run finishes.
Register a Cognitum Seed device at a supplied endpoint or its default USB Ethernet address. The workflow establishes an agent connection, retrieves device identity, assigns initial trust, optionally pairs the device, and records registration.
Organize Cognitum Seed devices into fleets, or device groups. Create and list fleets, add or remove devices, and delete fleets through dedicated commands, with explicit confirmation required before deletion.
Manage firmware updates across IoT device fleets through staged rollouts. Check a small initial group for anomalies before expanding deployment, inspect rollout status, and roll back updates with explicit user approval for device changes.
Check federation health, the connectivity between agents in separate installations. Summarize connected peers, trust levels and scores, active sessions, message exchanges, personal information redactions, and threat detections, with a search of stored federation history.
Prepare a node to participate in federation, a setup connecting multiple nodes. Generate an ed25519 keypair and configuration, optionally set a compliance mode, and display the public key and endpoint to share with peers.
Detect and classify unusual readings from Cognitum Seed devices, including spikes, flatlines, and drift. Review anomaly scores, recommend quarantine for scores above 0.9, and save detected patterns for future reference.
Check a Ruflo installation for common environment and configuration problems, then run automatic repairs. The checks cover software versions, background services, the memory database, API keys, MCP servers, disk space, and TypeScript.
Compare recorded agent spending with a project budget and receive alerts at four usage thresholds. Review remaining funds and recommended actions, or use the check to block agent launches when the budget is exhausted.
Export recorded AI usage costs and budgets as Prometheus metrics or JSON. Feed external dashboards with totals, model tier breakdowns, session costs, and budget utilization, or send the same data to a webhook endpoint.
Summarize spending events for each connected peer over the past hour, day, and week, and flag peers above a configured suspension threshold. This consumer-side script depends on upstream event publishing that the source describes as not yet implemented.
Compare two saved cost-summary JSON snapshots to identify changes in total spending, sessions, and model costs. Set limits on spending increases or token growth to flag cost regressions during pull request checks.
Connect ruflo agents running on different machines to share status, tasks, and results through signed messages. Coordinate work ownership across nodes, verify messages against registered public keys, and synchronize shared rooms over HTTP.
Run four cost checks in parallel to assess budget limits, accelerating daily spending, unusual sessions, and projected budget exhaustion. Combine their results into one health status and exit code for automated CI workflows.
Check Ruflo's health and summarize system diagnostics, MCP server state, and active agents. Routine checks leave the installation unchanged; an explicit repair request runs the repair command and reports the resulting changes and status.
Track production session spending over time by grouping costs into configurable time windows. Compare the latest window with earlier spending averages, display the increase, and optionally return a failure exit code when it exceeds a chosen percentage.
Inspect local AGNTCY integration settings and summarize endpoint configuration, the optional SLIM package, transport status, and session counters in a short table. Distinguish available components from unimplemented features without making network calls or changing settings.
Manage Flow Nexus accounts, run code in sandboxes, deploy app templates, and track credits. The skill guides platform operations through connected tools, including file storage, execution monitoring, and coding challenge submissions.
Run and manage an agent in Anthropic's cloud for long or asynchronous tasks. Create a session with packages and tools, send work, inspect status and activity, continue with saved files and conversation, then terminate the session.
Coordinate software releases on GitHub, from version updates and release notes to testing, builds, deployment, and rollback. It describes workflows using multiple AI agents, including coordinated package releases and gradual deployments with monitoring.
Analyze performance in Claude Flow swarms, groups of agents working together. Identify delays in communication, processing, memory, and networking, then generate reports and prioritized recommendations. Compare previous runs or apply available automatic optimizations.
Coordinate groups of AI agents in the cloud with Flow Nexus. Assign research or development tasks, automate workflows triggered by events or schedules, and monitor execution, queues, and agent capacity through the platform's tools.