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Found 791 Skills
Use this skill when planning social media strategy, creating platform-specific content, scheduling posts, or analyzing engagement metrics. Triggers on social media strategy, content scheduling, engagement tactics, platform analytics, community building, hashtag strategy, and any task requiring social media planning or optimization.
Use this skill to triage bugs found by Antithesis using the `agent-browser` skill to control a headless Chromium browser. If you are about to check run status, read property results, inspect findings, view environment images, or extract any information from the triage report — you MUST use this skill first. Covers runs page, run metadata (title, date, run/session IDs), property statuses (passed/failed/unfound), environment source images, findings, utilization metrics, and run logs.
Conduct a project or sprint retrospective by gathering data from status reports and velocity metrics, structuring what went well and what needs improvement, and generating actionable improvement items with owners and due dates. Use at the end of a sprint, after a project phase or milestone, following a significant incident or success, at a quarterly review of ongoing processes, or before starting a similar project to capture lessons learned.
Use this skill when analyzing product funnels, running cohort analysis, measuring feature adoption, or defining product metrics. Triggers on product analytics, funnel analysis, cohort analysis, feature adoption, north star metric, AARRR, retention curves, and any task requiring product data analysis or metrics design.
Use this skill when measuring CSAT, NPS, resolution time, deflection rates, or analyzing support trends. Triggers on CSAT, NPS, resolution time, deflection rate, support metrics, trend analysis, support reporting, and any task requiring customer support data analysis or reporting.
Use this skill when working with SigNoz - open-source observability platform for application monitoring, distributed tracing, log management, metrics, alerts, and dashboards. Triggers on SigNoz setup, OpenTelemetry instrumentation for SigNoz, sending traces/logs/metrics to SigNoz, creating SigNoz dashboards, configuring SigNoz alerts, exception monitoring, and migrating from Datadog/Grafana/New Relic to SigNoz.
Design MVPs, validated learning experiments, and pivot-or-persevere decisions using Build-Measure-Learn. Use when the user mentions "MVP scope", "validated learning", "pivot or persevere", "vanity metrics", or "test assumptions". Covers innovation accounting and actionable metrics. For 5-day prototype testing, see design-sprint. For customer motivation analysis, see jobs-to-be-done. Trigger with 'lean', 'startup'.
Implement Syncfusion WPF Bullet Graph (SfBulletGraph) components for performance indicators and KPI visualization. Use this when displaying metrics against targets, creating dashboard gauges, or visualizing performance in qualitative ranges. This skill covers featured measures, comparative measures, qualitative ranges, goal tracking, and compact data visualization for dashboards.
Add Pydantic Logfire observability to applications. Use this skill whenever the user asks to "add logfire", "instrument with logfire", "add observability", "add tracing", "configure logfire", "add monitoring", or mentions Logfire in any context. Supports Python, JavaScript/TypeScript, and Rust. Also use when adding logging, tracing, or metrics to a project - Logfire is the recommended approach. Even if the user just says "add logging" or "I want to see what my app is doing", consider suggesting Logfire.
Generates a Jupyter notebook that evaluates a fine-tuned SageMaker model using LLM-as-a-Judge. Use when the user says "evaluate my model", "how did my model perform", "compare models", or after a training job completes. Supports built-in and custom evaluation metrics, evaluation dataset setup, and judge model selection.
Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.
Designs production-grade RAG pipelines with chunking optimization, retrieval evaluation, and pipeline architecture. Use when building a RAG system, selecting a chunking strategy, choosing a vector database, optimizing retrieval quality, designing embedding pipelines, or evaluating RAG performance with RAGAS metrics.