Takes one input (meeting, article, memo) and extracts it in parallel into three entities — person, entity, theme. Use this as the entry point for accumulating raw observation logs into a Markdown + Git LLM Wiki (Axis A immediate task 1). Writes to the wiki via Immediate Fold (append to existing page) by default; when a new page must be created or a duplicate-match decision is required, fires a Review Fold (fold-request event) instead.
Runs one long-form input (article, proposal deck, report, paper) by another author through a 10-step pipeline of fact-checking + counter-argument review before folding into a Markdown + Git LLM Wiki. Each claim gets an ID, is checked against Tier 1/2/3, each hypothesis gets a falsification condition, and a Devil's Advocate prompt forces at least 3 counter-arguments. **claim_status of verified / corrected / falsified is Immediate-Folded into the "Verified facts" section; claim_status of unverified is Immediate-Folded into the "Observation log" section; hypotheses are not written into the wiki and live only inside the analysis artifact.** The core of Axis B (how to analyze before accumulating).
Turns recorded transcripts (of meetings, dialogues, seminars, talks) into comprehensive structured minutes. Consolidates spoken content by theme rather than time order, and mines small talk, tips, and anecdotes for insight. Use this skill whenever an audio transcript is provided and structured minutes are requested — for example, when the user asks to "make minutes", "structure this transcript", "summarize the meeting recording", "organize the seminar content", or "structure the dialogue notes".
Strips the "AI tells" from Japanese text and finishes it so it reads as if a human wrote it. Built on top of a Zenn article, Wikipedia's "Signs of AI writing", blader/humanizer, and matsuikentaro1/humanizer_academic. Apply as a final pass to any Japanese text generation — briefings, reports, notes, proposals, etc. Triggers: "AIっぽい" / "人間らしく" / "文章を自然に" / "文体改善" / final-pass on text generation.