Compare pasted decision records and meeting notes to identify conflicting choices, missing context, and unresolved questions while retaining a traceable source ledger.
Turn video scripts into structured retention audits — map hook failures, desertion windows, and re-engagement fixes using attention psychology.
LLMエージェントがセッションをまたいで記憶を保持するための永続メモリ層。抽象基底クラス MemoryStore ＋ LocalJSONStore 実装で、保存・取得・検索を再利用可能に。Karpathy の「LLM OS（記憶＝ディスク層）」着想。バックエンド差し替え可・Python標準ライブラリのみ・LLM呼び出しゼロ・ユニットテスト同梱。
You write one great idea — then lose hours reformatting it for blog, email, X, YouTube, and Instagram. Content Repurposer fixes that: paste one memo, outline, or transcript and get five platform-ready outputs in one run — blog with 10 headlines, 10 X posts, newsletter with subject lines, YouTube script, and 10-slide Instagram carousel. Works with Claude Code, Codex, Cursor, and other AI agents. No API keys or integrations. Anti-hallucination rules keep every output faithful to your source.
I've spent years building production software, designing enterprise architectures, reviewing thousands of code changes, and optimizing AI-assisted development workflows. This Skill packages proven engineering, planning, documentation, testing, and code-review practices into a lightweight workflow that helps AI understand any project before making changes, reducing mistakes, hallucinations, unnecessary token usage, and repeated context loading.
Your agent or chatbot keeps getting document content wrong, citing passages that don't exist, or saying "I can't find it" about things plainly in the docs. This reframes it as a retrieval failure, not a prompt bug: corpus profile, chunking, embedding and vector-DB index choice, retrieval strategy (top-k, hybrid BM25 + dense, rerank, query rewrite), context assembly with citations, and a golden Q&A set scored on recall@k / MRR / faithfulness.
Research Context Management helps agents run long, evidence-heavy research without losing sources, claims, contradictions, or next steps. It builds a durable workspace with a source ledger, batch notes, claim matrix, contradiction register, working brief, and handoff checkpoint, so deep research, audits, market scans, and reports can be reviewed, resumed, and turned into reliable outputs with clearer evidence trails.
Created from 10+ years of manufacturing and industrial automation experience across food packaging, planter-part manufacturing, and Tier 1 automotive environments, with Ignition used in production-facing work over that span. The skill packages practical SCADA judgment around maintainable expressions, operator-safe behavior, troubleshooting habits, and realistic Designer/Gateway validation workflows.
Notion 塞爆的人都知道，問題不是沒地方放，是放進去要填的欄位太多，所以最後全丟在收件匣爛掉。 這支讓 AI 判斷該進知識庫還是行動庫、抓連結內容寫摘要、填好所有欄位——然後逐筆 GET 回查驗證真的寫進去了才說成功。API 回 200 不等於資料正確落地，而多數工具在這裡就宣稱完成了。 合法選項直接讀 Notion 真實 schema，永遠不會過期。