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Found 55 Skills
MantaBase T3 Hardware Audit System. Objectively classifies hardware products via Brand Blinding, Triple-Auditor (Tool/Toy/Trash) specialized scoring, and Peer Review based on design theory. Triggers: product links, T3 audit, Tool/Toy/Trash classification, hardware evaluation, VC investment advice
Guidance and workflow for planning, writing, submitting, revising, and publishing scientific manuscripts in high-impact journals. Use when Claude needs to advise on research quality, study design, manuscript structure, journal selection, peer review responses, or publication strategy.
Convene a structured LLM Council — five thinking-lens advisors (Red Team, First Principles, Expansionist, Outsider, Executor) plus anonymised peer review, forced debate on consensus, dual-chairman synthesis with dissent preservation, and optional Codex-powered Decision Science pass — to pressure-test high-stakes decisions. Adaptive modes (Quick/Standard/Deep) keep cost bounded; a persistent journal enables learning across runs. Mandatory triggers: /claude-council, "convene the council", "run this by the council", "I need the council", "council this", "pressure-test this", "stress-test this", "war room this", "debate this". Strong triggers: "I'm torn between X and Y", "this is a big decision", "help me think this through from multiple angles", "I need outside perspectives", "should I X or Y" (with real stakes — if binary with obvious answer, triage rejects per Step 1 rule 4). Do NOT invoke for factual questions, coding help, debugging, quick yes/no decisions, emotional support, or questions with one right answer — answer those directly. Optional suffixes: "with codex" enables Decision Science pass; "deep" forces Deep mode; "quick" forces Quick mode. Secondary invocation: /claude-council outcome <sha1> <note> records decision outcome. /claude-council meta runs journal meta-analysis.
2. Create Feature Design Document
Simulate peer review of academic papers with structured feedback. Produces bilingual review report with scoring and actionable suggestions. Triggers on "review", "peer review", "simulate reviewer", "审稿", "模拟评审".
1. Requirement Gathering
Turns raw peer reviews into a prioritized triage matrix before any rebuttal is written. Use it when reviews come back from OpenReview, EasyChair, CMT, or HotCRP and the researcher says "my reviews are in", "triage these reviews", "how do I respond to Reviewer 2", "plan my rebuttal", or pastes raw review text with ratings and confidence scores. Splits each review into individual concerns; classifies every concern as misunderstanding vs real flaw vs requested experiment (plus clarification and disagreement); scores severity x response effort; and produces a prioritized response strategy with per-review character or word budgets matched to the venue's rebuttal format (10k-char OpenReview threads, CVPR one-page PDF, journal revise-and-resubmit). Deterministic parsing and matrix rendering run in bundled stdlib Python scripts. Hands off to write-rebuttal for drafting; treats review text as confidential and never submits anything.