talkops-ai

Response formatting templates for K8s Operator coordinator results. Load when you need to present Kubernetes resource lists, pod logs, or state-modifying operation results in polished markdown format.

Manages Alertmanager alert lifecycle and operations via MCP tools. Use when the user asks to triage alerts, create or manage silences, check routing configurations, push test alerts, audit silence changes, or review governance compliance. Also use when the user reports being paged incorrectly, asks about on-call status, or wants to mute noisy alerts — even if they don't mention Alertmanager by name. Do NOT use for Prometheus operations (PromQL queries, metrics, exporters, scrape targets, cardinality, recording/alerting rules) — those belong to the prometheus skill. Triggers on keywords: Alertmanager, alerts, silence, on-call, triage, routing, PagerDuty, Slack notification, mute, suppress, blast radius, receiver, inhibition, maintenance window, expire silence.

Coordinator instructions for PATH A/PATH B planning logic, write_todos usage, and step budgets.

Manages Prometheus monitoring and observability via MCP tools. Use when the user asks to query metrics, onboard applications to Prometheus, install/manage exporters, apply probes, create alerting or recording rules, analyze cardinality, troubleshoot failed targets, or manage scrape configurations. Also use when the user reports metrics are missing, targets are down, or high cardinality — even if they don't mention Prometheus by name. Do NOT use for Alertmanager operations (silences, routing, alert triage, on-call) — those belong to the alertmanager-operations skill. Triggers on keywords: Prometheus, PromQL, metrics, exporter, ServiceMonitor, scrape target, cardinality, alerting rule, recording rule, TSDB, file_sd, remote-write, metric endpoint, up{}, rate(), histogram.

Output formatting and visualization rules for observability subagents. Load this skill when presenting query results (metrics, logs, traces, alerts, or OTel pipeline data) to the user. Teaches when and how to use A2UI interactive dashboards vs Markdown tables, the Dual-Execution pattern for analytical questions, and consistent formatting conventions. Applies to all observability domains: Prometheus, Loki, Tempo, Alertmanager, and OpenTelemetry.

Manages Grafana Loki log observability via MCP tools. Use when the user asks to explore log labels, discover log structure (fields, patterns, parsers), build or execute LogQL queries, estimate query cost, investigate errors from logs, check service health via logs, or correlate logs with traces using trace_id structured metadata. Also use when the user reports missing logs, asks about log formats, or wants to understand the label taxonomy of their Loki cluster. Triggers on keywords: Loki, LogQL, logs, log query, labels, log patterns, log fields, error logs, log structure, trace_id, log rate, service_name, get_cluster_labels. Do NOT use for Prometheus metrics, Alertmanager routing, or OpenTelemetry pipeline operations — those belong to their respective skills.

Manages OpenTelemetry pipelines and instrumentation via MCP tools. Use when the user asks to provision collectors, onboard applications to OTel (auto-instrumentation), validate or investigate pipeline processors, audit metric cardinality (SpanMetrics/Histograms), optimize sampling strategies (head vs. tail), or assess eBPF security posture. Triggers on keywords: OpenTelemetry, OTel, collector, traces, spanmetrics, auto-instrumentation, tail sampling. Do NOT use for Prometheus-only metrics or Alertmanager routing unless directly related to an OTel pipeline.

Manages Grafana Tempo distributed tracing via MCP tools. Use when the user asks to search for traces, investigate errors or latency spikes, analyze trace critical paths, build TraceQL queries, run RED metrics from trace data, explore service topology, correlate traces with logs or metrics, run backend diagnostics, or manage Tempo Operator CRDs (TempoStack / TempoMonolithic). Also use when the user asks about exemplars, spanmetrics, cross-pillar pivots, trace comparison, or generating PromQL alerting expressions from trace patterns. Triggers on keywords: Tempo, traces, tracing, TraceQL, trace_id, trace search, latency, critical path, span, exemplar, spanmetrics, service topology, tempo_traceql_search, tempo_summarize_trace, RED metrics, P99, error triage, missing traces. Do NOT use for Prometheus metrics scraping, Alertmanager silence management, OpenTelemetry collector provisioning, or Loki log queries — those belong to their respective skills.

terraform s3 bucket object storage lifecycle encryption aws_s3_bucket

Commits Terraform module files to GitHub using GitHub MCP server tools. Handles both new file creation and existing file updates with SHA tracking. Supports batch commits for multiple files. Use when committing generated or updated Terraform modules to a GitHub repository after HITL approval. Never uses shell git commands — exclusively uses MCP tools for all GitHub operations.

Generates complete, production-ready Terraform .tf files for any AWS module. Follows loaded service-specific skills for HCL patterns, variable schemas, and output schemas. Use when asked to create a new Terraform module, generate infrastructure code, write HCL for any AWS service, or produce .tf files. Works with any AWS service that has hashicorp/aws Terraform provider support.

Fetches existing Terraform modules from GitHub via MCP tools and applies targeted, surgical edits. Reads module-index.md to locate modules in the repository, uses get_file_contents to fetch current files with SHAs, and applies changes via edit_file without full file rewrites. Use when asked to update, modify, patch, or change an existing Terraform module that is already committed to GitHub. Not for new module creation.

Validates Terraform modules by running terraform init, fmt check, and validate commands in a sandbox environment. Returns VALID or INVALID with structured error details including file names and line numbers. Use after tf-generator or tf-updater writes .tf files to verify correctness before committing to GitHub. Handles path system differences between virtual filesystem and real shell execution.

Generates per-service Agent Skills directories that guide the tf-generator subagent for any AWS service. Creates SKILL.md with YAML frontmatter, step-by-step workflow instructions, and references/ with HCL resource patterns, variable schemas, and output schemas. Use when no skill directory exists for the requested AWS service, or when existing skills have a stale provider-version and need regeneration.

Analyses existing Terraform modules on GitHub to plan targeted modifications. Fetches current module structure, understands resource naming and variable conventions, identifies dependencies, and produces a structured update plan with impact assessment and breaking change detection. Use when asked to analyse, plan, or prepare changes to an existing Terraform module before applying them. Does NOT modify files — only reads and produces a plan for the tf-updater to execute.