# Ai Operations > >- - **Type:** Skill - **Install:** `agentstack add skill-harness-harness-skills-ai-operations` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [harness](https://agentstack.voostack.com/s/harness) - **Installs:** 0 - **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills) - **Latest version:** 0.1.0 - **License:** Apache-2.0 - **Upstream author:** [harness](https://github.com/harness) - **Source:** https://github.com/harness/harness-skills/tree/main/skills/ai-operations - **Website:** https://developer.harness.io/docs/platform/harness-ai/harness-skills/ ## Install ```sh agentstack add skill-harness-harness-skills-ai-operations ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # AI Operations Configure AI-powered predictive failure analysis and intelligent alert correlation using Harness AIDA. ## Instructions ### Step 1: Establish Scope Confirm the user's org, project, service, and observability stack. ``` Call MCP tool: harness_list Parameters: resource_type: "project" org_id: "" ``` ### Step 2: Identify the AI Operations Task Determine which workflow the user needs: 1. **Predictive Failure Analysis** -- ML-based detection of impending failures before SLO breach 2. **Alert Correlation and Noise Reduction** -- Group related alerts and suppress duplicates ### Step 3: Configure Predictive Failure Analysis Gather from the user: - Service name and data sources (Datadog, Prometheus, CloudWatch) - Prediction horizon (30 minutes, 1 hour, 4 hours, 24 hours ahead) - Training data period (30 days, 90 days, 6 months) - Model type preference (anomaly detection, time series forecasting, ensemble) Configure failure prediction scenarios: 1. **Memory leak detection** -- Flag services where memory grows above threshold per window 2. **Disk exhaustion** -- Predict time-to-full and alert N hours in advance 3. **Connection pool saturation** -- Alert when pool usage exceeds threshold for sustained duration 4. **Latency degradation** -- Detect progressive slowdown before SLO breach 5. **Deployment-induced regression** -- Correlate metric changes with recent deployments Configure alerting: - Set prediction confidence threshold (suppress below threshold to reduce noise) - Route alerts to PagerDuty, Slack, or other channels - Enable auto-generated runbook suggestions using AIDA - Set up false positive feedback loop for model improvement Configure data sources: - Metrics source (Prometheus, Datadog, CloudWatch) - Log source (Elasticsearch, Splunk, CloudWatch Logs) - Trace source (Jaeger, Datadog APM, AWS X-Ray) - Model retraining frequency (daily, weekly, monthly, on data drift) ### Step 4: Configure Alert Correlation and Noise Reduction Gather from the user: - Current alert volume (alerts/day) and target reduction percentage - Alerting tools in use (PagerDuty, OpsGenie, Grafana, Datadog) - Correlation preferences Configure alert correlation: - Correlation window: group alerts fired within N minutes - Correlation method: topology-based, time-based, ML-based, or hybrid - Service dependency mapping for topology-based correlation Configure noise reduction: - Deduplication: merge identical alerts across sources - Suppression: suppress known-noisy alerts during maintenance windows - Aggregation: combine N similar alerts into a single incident - Priority scoring: ML-based severity assignment using historical resolution data Configure intelligent routing: - Route to the team that owns the affected service - Escalation: auto-escalate if not acknowledged within SLA - Context enrichment: attach recent deployments, related logs, and runbook links to alerts ## Examples - "Set up predictive failure analysis for our payment service" -- Configure ML models to detect memory leaks, disk exhaustion, and latency degradation - "Reduce our alert noise by 50%" -- Configure alert correlation and deduplication to reduce daily alert volume - "Alert us 4 hours before disk runs out" -- Configure disk exhaustion prediction with advance warning - "Correlate alerts across our microservices" -- Set up topology-based alert correlation using service dependency map - "Auto-generate runbook suggestions for alerts" -- Enable AIDA-powered runbook recommendations ## Performance Notes - ML models need 2-4 weeks of baseline data before predictions become reliable -- expect higher false positive rates initially. - Topology-based correlation requires an accurate service dependency map -- stale maps cause missed correlations. - Alert correlation windows should balance grouping (longer = fewer alerts) with response time (shorter = faster notification). - Retraining frequency should match how fast the system changes -- fast-moving services need weekly retraining. ## Troubleshooting ### High False Positive Rate - Increase the confidence threshold to suppress low-confidence predictions - Provide false positive feedback to improve the model - Check that training data period includes representative traffic patterns (weekday, weekend, peak) ### Predictions Not Triggering - Verify data sources are connected and sending metrics - Check that the prediction horizon is appropriate for the failure mode - Ensure the model has completed initial training (2-4 weeks minimum) ### Alert Correlation Missing Related Alerts - Increase the correlation window to capture cascading failures - Update the service dependency map if topology-based correlation is in use - Check that all alert sources are integrated (missing sources cause orphaned alerts) ## Source & license This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code. - **Author:** [harness](https://github.com/harness) - **Source:** [harness/harness-skills](https://github.com/harness/harness-skills) - **License:** Apache-2.0 - **Homepage:** https://developer.harness.io/docs/platform/harness-ai/harness-skills/ Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-harness-harness-skills-ai-operations - Seller: https://agentstack.voostack.com/s/harness - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.