Utility Matrix

Use when a user asks about alert noise, alert fatigue, or on-call burden from non-actionable alerts. Given weekly alert volume, actionable %, engineer count, and avg resolution time, returns noise rate, wasted hours, and fatigue tier.

Use this when a user asks about their Apdex score or wants to measure user satisfaction with response times. Takes satisfied, tolerating, and total request counts; satisfied plus tolerating may not exceed total. Returns the Apdex score (0–1) and its rating on the published five-band scale: excellent, good, fair, poor or unacceptable.

Use this when a user asks what a specific availability percentage (like 99.9% or 99.99%) means in downtime minutes per month or year. Converts availability SLA percentage to exact downtime budgets: annual, monthly, weekly, and daily.

Audit a page URL against its canonical tag. Detects missing canonical, self-referencing canonical, and relative URL issues.

Assess certificate expiry risk given a domain and days remaining. Returns risk level (OK, WARNING at <30 days, CRITICAL at <7 days, EXPIRED at <0).

Use when a user asks about change failure rate or DORA CFR. Given total and failed deployments, returns failure rate percentage and DORA tier.

Use when a user asks about cloud waste, idle resources, or unattached storage. Given monthly cloud bill and idle/oversized percentages, returns waste estimate and tier.

Use this when a user asks for Kubernetes cluster capacity. Uses the same node vCPU, node memory, node count, system reserve, and target utilization model as the browser calculator.

Use this when a user asks how many database connections to configure. Uses the same max_connections, app server count, database cores, average query duration, and peak RPS model as the browser calculator.

Use when a user asks about deployment frequency or DORA deploy cadence.

Scan text (config, code, env files) for leaked API keys, tokens, passwords, and private keys, plus static code-risk findings such as dynamic code execution, wildcard CORS, unsafe HTML sinks, process spawning and removed security guards. Returns findings with redacted matches and severity classification.

Lint a Dockerfile against a rule-based subset of Hadolint checks: untagged or :latest base images, interactive or unpinned apt-get installs, apt list cleanup, apk cache, ADD for local files, shell-form CMD/ENTRYPOINT, running as root and a missing HEALTHCHECK. Lines joined with a trailing backslash count as one instruction, and each issue reports its line in the original file.

Use this when a user wants to benchmark an engineering team against DORA metrics. Takes deploys per month, lead time in hours, change failure rate in percent and, optionally, time to restore in hours. Classifies each metric as elite, high, medium or low against fixed benchmark bands; an omitted restore time is reported as unknown and the overall profile is marked partial rather than assumed.

Use when a user asks about EC2 cost. Given instance type and hours, returns monthly cost and DO-equivalent savings estimate.

Use this when a user asks about monthly internet egress cost from daily request volume, average response size, provider, and CDN offload. Uses the same model as the browser calculator.

Use this when a user asks about their SLO error budget, remaining error budget, or burn rate. Given SLO percentage and window, returns budget minutes remaining, burn rate multiplier, and status (Under budget / Elevated / ALERTING).

Use this when a user asks what an outage or incident cost, or wants to calculate the financial impact of downtime. Takes hourly revenue, downtime duration, and affected percentage. Returns revenue impact, engineering cost, and total incident cost in dollars.

Historical curated package-name advisory reference only. Matches older CVE examples for common packages; does not query live feeds or evaluate installed version ranges, so it never proves a package is currently vulnerable or safe.

Lint a Kubernetes manifest (YAML) for common issues: missing resource limits, missing health checks, :latest tag usage, privileged containers. Returns issue list and severity.

Use when a user asks about MTTR or DORA recovery time. Takes incident timestamps (string lines "start,detected,resolved" or array of {start,detected,resolved}).

Use when a user asks about on-call load or burnout risk. Uses team size, rotation length, and hourly rate exactly as the browser calculator does.

Use this when a user asks how much their GitHub Actions CI/CD pipeline costs per month. Takes average job duration in minutes, jobs per run, runs per month and the runner label (ubuntu-latest, windows-latest, macos-latest or self-hosted; matched case-insensitively). Uses GitHub list prices for standard hosted runners effective 1 Jan 2026 and GitHub Free's 2,000 included minutes for private repositories, drawn at list price. Self-hosted minutes carry no GitHub charge.

Use this when a user asks about their ML model precision, recall, F1 score, or F-beta score. Takes precision and recall as fractions from 0 to 1 and an optional beta weight. Returns F1, F-beta, and interpretation of the precision-recall tradeoff.

Estimate PostgreSQL sequential-scan and index-scan cost from table rows, table pages, selectivity, and storage type. Uses the same four-input model as the browser calculator.

Calculate API rate limit utilization given current request count and limit. Returns utilization percentage, buffer, safety status, and risk level (OK at <80%, WARNING at 80-95%, CRITICAL at >95%).

Use this when a user asks whether to buy Reserved Instances or stay on On-Demand, or how long an RI takes to pay for itself. Takes On-Demand and RI monthly costs plus optional upfront fees (0 when omitted). Judges the 1-year and 3-year terms separately and returns each break-even month and net saving. It recommends a term only when that term saves money over its own length, and prefers 3 years only when it beats three 1-year terms over 36 months at current prices.

Use when a user asks about instance rightsizing or overprovisioned compute.

Use this when a user asks how fast they are burning their error budget, or whether their current error rate will exhaust their SLO window. Takes error count, total requests, SLO target, and observation window. Returns burn rate and budget consumption per hour.

Use when a user asks about risk of a software purchase or SaaS commitment.

Use when a user asks to estimate monthly cost of a Terraform plan.

Use when a user asks about TCO or multi-year SaaS cost.