14 skills found
microprediction / preciseOnline (incremental) covariance, correlation, and precision estimation in Python — the streaming complement to sklearn.covariance
majiayu000 / issue-size-estimationIssue粒度判定とコード行数見積もりの基準、サイズラベル、分割判定ロジックを定義
deedy5 / ddgsA metasearch library that aggregates results from diverse web search services
kwon416 / real-estimate-AIPython RAG LangChain + 부동산 통계 정보 open API 챗봇 프로젝트
uiuc-kang-lab / hyperparamsGuide for hyperparameter selection — learning rate formulas, LoRA rank, batch size, group size, schedules, and model-specific tuning
JungHoonGhae / tossinvest-cli토스증권을 AI 에이전트와 터미널에서 다루는 도구. CLI 와 MCP 서버로 계좌·시세·주문은 물론 웹앱 전용 기능(수급·AI 시그널·스크리너·배당)까지, JSON·CSV 구조화 출력으로 AI 도구·자동화에 바로 연동.
DangQuangSE / Inversion ExerciseFlip core assumptions to reveal hidden constraints and alternative approaches - "what if the opposite were true?"
simonlin1212 / a-stock-dataA股全栈数据工具包 · 10层架构 · 43端点(含3官方备胎) · 15数据源 · 行情/研报/资金面/筹码/公告/打板/ETF期权/舆情互动全覆盖+备用源降级 | China A-Share full-stack data toolkit (43 endpoints)
cbtw-apac / qdrant-loaderEnterprise-ready vector database toolkit for building searchable knowledge bases from multiple data sources. Supports multi-project management, automatic ingestion from Confluence/JIRA/Git, intelligent file conversion (PDF/Office/images), and semantic search.
Nikolai-Iakubovskii / app-paywall-pilotA framework for designing App Store-compliant subscription paywalls. 4 layers: AI skill (Claude/GPT/Cursor) + knowledge base (79 sourced benchmarks) + Python LTV tool + docs. 20-concept academic foundation (Kahneman + Layer 2).
rwi001 / polarsFast in-memory DataFrame library for datasets that fit in RAM
huang-sh / pytorch-fsdpAn open-source, model-agnostic AI workbench for scientific discovery.
xbtlin / ai-berkshireAI 时代的伯克希尔:基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code / Codex. 4 masters' methodologies + multi-agent adversarial analysis.
raiyanyahya / ensembleMulti-model consensus debate via the filesystem. LLMs propose, peer-review, rebut, vote and synthesize a group-confirmed answer. CLI + MCP.