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Found 3,325 Skills
Conversionomics integration. Manage data, records, and automate workflows. Use when the user wants to interact with Conversionomics data.
Example of a properly self-contained skill following all best practices
Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Debug and harden production LLM prompts — handle prompt injection, output format drift, instruction forgetting in long contexts, and cross-model portability issues. Use this skill when the user ships an LLM-powered feature to production and needs to diagnose why outputs are inconsistent, unsafe, or regressed after model updates — NOT for basic 'write a better prompt' questions.
Rust no_std skill for embedded and bare-metal development. Use when writing
Coverage-guided fuzzing workflow for C/C++, Rust, and Go targets. Runs audit-context-building to find suspicious code, writes a targeted harness, builds with sanitizers, runs the fuzzer, and reports crashes.
A/B test design and experiment planning for paid advertising. Structured hypothesis framework, statistical significance calculator, test duration estimator, sample size calculator, and platform-specific experiment setup guides (Meta Experiments, Google Experiments, LinkedIn A/B). Use when user says A/B test, split test, experiment design, test hypothesis, statistical significance, sample size, or test duration.
This skill should be used when a developer is ready to implement a GitHub Task issue and needs to read the full spec hierarchy (Task + Feature + Epic), explore the codebase, produce a concrete Technical Approach with real file paths, and drive TDD implementation against Gherkin scenarios. Triggers on phrases like "implement task
Use when building command-line interface tools; when choosing argument parsing libraries; when handling stdin/stdout/stderr patterns; when implementing subcommands; when tests for CLI apps fail or are missing
Find branch coverage gaps in changed code and fix them by writing missing tests. Two analysis layers: Source ↔ Test (logic branches vs test cases) and Spec ↔ Test (requirement scenarios vs test cases, when a spec file is provided). Use when verifying test completeness after implementing a feature or fixing a bug, when auditing whether tests match a spec, or when suspecting untested branches.
Build and test iOS apps on simulator using XcodeBuildMCP. Use after making iOS code changes, before creating a PR, or when verifying app behavior and checking for crashes on simulator.