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Found 11,839 Skills
Deliver Python backends across async FastAPI and Django or Flask service styles while keeping API design, validation, auth, and service behavior explicit.
Fetch x-bees chat info by raw channel ID, inbox URL, or full channelId. Use when you have a URL like https://app.x-bees.com/inbox/<rawId> or a bare rawId and need channel details (name, type, members) before fetching messages or sending.
Create, modify, and manage Word documents.
Use to analyze loyalty member behavior, segmentation, and experiment results.
Set up and maintain the Parallel CLI (install, auth, balance, skills install)
Opinionated guidance for constructing and interpreting Honeycomb queries on trace and event datasets — operation selection (percentiles not AVG, HEATMAP for distributions), relational field patterns (root., parent., any., none.), calculated fields, query math, and result interpretation (P99/P50 ratios, heatmap bands, TOTAL/OTHER rows, raw JSON via query_result_json). Use this skill when the user wants to query spans, traces, or log/event data in Honeycomb — requests like "show me latency", "error rate", "find slow requests", "find outliers", "interpret results", "relational fields", "calculated fields", or "download raw results". This skill covers all dataset types except metrics datasets (dataset_type=metrics) — for those, use metrics-queries instead.
Identifies code smells and provides step-by-step refactoring recipes. Use when improving legacy code maintainability or teaching students how to apply Clean Code and SOLID principles.
Redis LangCache guidance for semantic caching of LLM responses on Redis Cloud — calling search/set via the SDK or REST API, tuning the similarity threshold, separating caches per task type, and filtering with custom attributes. Use when caching LLM completions or RAG answers to cut API cost and latency, building a cache-aside layer in front of OpenAI / Anthropic / etc., tuning hit rate vs precision, or splitting one app's LLM workloads into multiple LangCache caches.
Vendor-neutral skill to generate a release risk checklist from scope, dependencies, and rollout constraints.
Vendor-neutral skill to score customer churn risk from account signals and produce prioritized retention actions.
Execute PromQL instant and range queries against Oodle metrics using the Prometheus-compatible query API.
Progressive-disclosure workflow for pandapower power-system studies. Use whenever the user wants to load, build, or inspect a pandapower network, run AC power flow, check bus voltages or line and transformer loading, or screen N-1 contingencies — even when they just say "run a load flow", "check this case", "is anything overloaded", or name a case file like case39.json. Exposes a clean base-case solve before advanced outage studies. Reach for this instead of answering pandapower grid questions unaided.