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Found 845 Skills
Expert product specification and documentation writer. Use when creating PRDs, user stories, acceptance criteria, technical specifications, API documentation, edge case analysis, design handoff docs, feature flag plans, or success metrics. Covers the full spectrum from high-level requirements to implementation-ready specifications.
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
Create a new SigNoz alert rule from a natural-language intent — threshold, anomaly, log-volume, error-rate, latency, or absent-data alerts across metrics, logs, traces, and exceptions. Make sure to use this skill whenever the user says "alert me when…", "notify me if…", "set up monitoring for…", "page me on…", "create an alert for…", or asks for a new alert/notification rule, even if they don't say the word "alert" explicitly. Also use it when someone asks to be notified about error rates, latency spikes, log volume, CPU/memory pressure, or anomalous behavior on a service or host.
Weekly engineering retrospective. Analyzes commit history, work patterns, and code quality metrics with persistent history and trend tracking. Team-aware with per-person contributions, praise, and growth areas. Use when asked for weekly retro, what shipped this week, or engineering retrospective.
FastAPI OpenTelemetry style: native FastAPIInstrumentor, centralized observability init, Python decorators, OTLP logs, and LLM cost metrics.
Explains business financial terms and frameworks for engineering managers — produces term definitions (ARR, COGS, CAC, LTV, gross margin, burn rate, EBITDA, AARRR), translation formulas for making engineering work visible in business language, and a three-layer framework for building business credibility. Use when the user says "business terms," "EBITDA," "burn rate," "CAC," "LTV," "gross margin," "ARR," "how do I speak to business people," "I don't understand finance," "make the case for engineering work," "connect engineering to business outcomes," "talk to the P&L owner," or "business impact." Do NOT use when the user wants to connect engineering metrics (DORA, velocity) to business metrics — use developer-productivity instead.
Configures the analytics side of a PostHog experiment — exposure criteria (default `$feature_flag_called` vs custom exposure events), primary and secondary metrics, the supported metric types (count, sum, ratio with `math` and `math_property`, retention with `retention_window_start` and `start_handling`), multivariate user handling ("Exclude" vs "First seen variant"), and how to read results once the experiment is live. Use when the user adds or edits a primary or secondary metric (e.g. "add a secondary metric tracking 'downloaded_file' per user"), sets up a ratio metric (e.g. "revenue from purchase_completed / pageviews"), sets up a retention metric (e.g. "$pageview → uploaded_file, 7-day window"), configures custom exposure (e.g. "only count users who hit /checkout"), changes multivariate handling, or asks "who is in the analysis?", "how do I measure impact?", "is this winning?", "what's the confidence level?", or "should I ship?".
Build and deploy a Coralogix dashboard for a given service from its logs, spans, metrics, and service specs. Discovers telemetry via cx CLI commands, emits importable Coralogix JSON, verifies every PromQL and DataPrime query live through the `cx` CLI, and creates or updates dashboards via `cx dashboards create` and `cx dashboards replace`. Use whenever the user asks to create, build, generate, deploy, update, replace, or modify a Coralogix dashboard, monitoring dashboard, or observability dashboard for a service, app, or pipeline.
Analyze alternative investments including hedge funds, private equity, and venture capital. Use when the user asks about hedge fund strategies (long/short, macro, event-driven), PE or VC performance metrics (IRR, TVPI, DPI), fee structures ('2-and-20', carry, hurdle rates), the J-curve effect, illiquidity premiums, lock-up periods, or hedge fund replication. Also trigger when users mention 'managed futures', 'CTA', 'fund of funds', 'vintage year', 'capital calls', 'distributions', 'carried interest', or ask how to evaluate an alternative investment manager.
Guides actuarial work for insurance and reinsurance—pricing and rate adequacy, reserving and IBNR, loss development and triangles, mortality/morbidity and lapse assumptions, experience studies and credibility, capital and risk metrics at overview level, product design tradeoffs (life, health, P&C, annuity), and regulatory reporting concepts (NAIC, IFRS 17, Solvency II overview—not legal advice). Use when the user mentions actuary, actuarial, IBNR, loss development, reserve analysis, mortality table, pricing insurance, experience study, IFRS 17, loss ratio, combined ratio, credibility, or asks for assumption documentation and model governance for insurance products—not generic FP&A (financial-analyst), investment banking valuation (comps-analysis, dcf-model), legal policy interpretation (commercial-counsel), clinical trials, software-only implementation (senior-software-engineer), or broad GRC without actuarial models (compliance-engineer).
Analyzes Kubernetes resource usage metrics and historical data to suggest optimal CPU and Memory requests and limits. Use to reduce cloud costs, prevent OOMKills, and improve overall cluster reliability by right-sizing your deployments.
Detect AI-generated code patterns ("slop") in PHP/Laravel and TypeScript/React source — comment narration, generic naming, premature interfaces, defensive overdose, mock-everything tests, and the absence of human "scars". Use when reviewing AI-assisted PRs, auditing code for taste/quality (not metrics — that's technical-debt), or hardening a code-review checklist. Triggers on "review for AI slop", "find AI patterns", "check code feels human", "audit code-quality taste".