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符合当前检索条件的共有 914
Data AI 3 浏览 · 4 下载

clarity-gate

by Antigravity · v1.0.0

Pre-ingestion verification for epistemic quality in RAG systems. Ensures documents are properly qualified before entering knowledge bases. Produces CGD (Clarity-Gated Documents) and validates SOT (Source of Truth) files.

Data AI 3 浏览 · 3 下载

posthog-automation

by Antigravity · v1.0.0

Automate PostHog tasks via Rube MCP (Composio): events, feature flags, projects, user profiles, annotations. Always search tools first for current schemas.

Data AI 3 浏览 · 3 下载

seo-forensic-incident-response

by Antigravity · v1.0.0

Investigate sudden drops in organic traffic or rankings and run a structured forensic SEO incident response with triage, root-cause analysis and recovery plan.

Data AI 3 浏览 · 4 下载

quant-analyst

by Antigravity · v1.0.0

Build financial models, backtest trading strategies, and analyze market data. Implements risk metrics, portfolio optimization, and statistical arbitrage.

Data AI 3 浏览 · 4 下载

context-compression

by Antigravity · v1.0.0

This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions...

Data AI 3 浏览 · 4 下载

context-guardian

by Antigravity · v1.0.0

Guardiao de contexto que preserva dados criticos antes da compactacao automatica. Snapshots, verificacao de integridade e zero perda de informacao.

Data AI 3 浏览 · 4 下载

apify-ecommerce

by Antigravity · v1.0.0

Scrape e-commerce data for pricing intelligence, customer reviews, and seller discovery across Amazon, Walmart, eBay, IKEA, and 50+ marketplaces. Use when user asks to monitor prices, track competi...

Data AI 3 浏览 · 4 下载

memory-systems

by Antigravity · v1.0.0

Design short-term, long-term, and graph-based memory architectures

Data AI 3 浏览 · 4 下载

polars

by Antigravity · v1.0.0

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.