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Found 1,678 Skills
Apply Hierarchical Linear Modeling (HLM) to analyze nested data structures with random intercepts and slopes, accounting for intra-class correlation and cross-level interactions. Use this skill when the user has students nested in schools, employees in firms, or repeated measures in individuals, needs to partition variance across levels, or when they ask 'how do I handle nested data', 'what is ICC', or 'do group-level factors moderate individual-level relationships'.
Analyze data privacy compliance requirements under GDPR, Taiwan's Personal Data Protection Act (PDPA), and related regulations. Use this skill when the user needs to assess data privacy obligations, design compliant data handling processes, evaluate cross-border data transfer risks, or understand data subject rights — even if they say 'do we comply with GDPR', 'can we collect this data', 'what are our privacy obligations', or 'how do we handle user data in Taiwan'.
Drop-in pandas replacement with ClickHouse performance. Use `import chdb.datastore as pd` (or `from datastore import DataStore`) and write standard pandas code — same API, 10-100x faster on large datasets. Supports 16+ data sources (MySQL, PostgreSQL, S3, MongoDB, ClickHouse, Iceberg, Delta Lake, etc.) and 10+ file formats (Parquet, CSV, JSON, Arrow, ORC, etc.) with cross-source joins. Use this skill when the user wants to analyze data with pandas-style syntax, speed up slow pandas code, query remote databases or cloud storage as DataFrames, or join data across different sources — even if they don't explicitly mention chdb or DataStore. Do NOT use for raw SQL queries, ClickHouse server administration, or non-Python languages.
Audit experiment integrity before claiming results. Uses cross-model review (GPT-5.4) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims.
Product bundling strategy — virtual bundles, multi-pack pricing, cross-sell bundles, bundle listing optimization
Turn work into realistic delivery plans and status tracking. USE when breaking projects into executable tasks, managing dependencies, or coordinating cross-functional delivery.
Central router for the marketing skill ecosystem. Use when unsure which marketing skill to use, when orchestrating a multi-skill campaign, or when coordinating across content, SEO, CRO, channels, and analytics. Also use when the user mentions 'marketing help,' 'campaign plan,' 'what should I do next,' 'marketing priorities,' or 'coordinate marketing.'
Expert product launch strategist for SaaS and technology companies. Use when planning product launches, coordinating cross-functional launch teams, managing beta programs, creating launch communication plans, planning launch day execution, setting up post-launch monitoring, running launch retrospectives, or defining launch metrics. Covers launch tiering, internal enablement, rollback planning, and contingency strategies.
This skill should be used when the user wants to check whether an agent skill is portable across providers. Common triggers include "is this skill cross-provider safe", "will my skill work in cursor", "audit skill compatibility", "check if this loads in codex", and "which providers support this skill". Spawns one agent per provider in parallel using bundled provider-doc snapshots (refreshed on cadence — never fetched at runtime) and produces a compatibility matrix plus a COMPAT.md report. Skip when authoring a new skill (use skill-creator) or rerunning baselines (use skill-eval).
/cs:cross-eval <memo> — Multi-model consensus on a board memo or strategy brief. Claude + Codex + Gemini cross-review with graceful degradation.
Enterprise AI marketing automation toolkit with 18 agents, 93 commands, and 28 skills for campaign planning, content creation, SEO, CRO, and growth workflows
Propose and execute rubric or bucket upgrades. Two modes: **Full rubric bump** (highest-risk action, mandatory 5-step process + cross-model audit) and **--bucket-only lightweight recalibration** (only update bucket boundaries, no changes to rubric formulas). **Phase 2 mandates using cheat-score-blind sub-agent to re-score the calibration pool** — self-scored fallback is not accepted. Trigger phrases: "upgrade rubric"/"bump rubric"/"update formula"/"I want to add a dimension"/"adjust weights"/"recalibrate bucket"/"recalibrate bucket".