Loading...
Loading...
Found 140 Skills
Use when migrating messy academic research repositories, downloaded archives, proposal folders, ad hoc notebooks, scripts, datasets, or paper assets into the standard research project structure.
Use when an academic research repository task could involve research design, sources, conversion, bibliography, SOTA, reviews, ethics, experiments, papers, reproduction, MCP tools, or project maintenance and the correct workflow is not obvious.
Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.
Systematic retrieval expert covering all areas of Chinese law. ## Core Features - Supports user identity recognition (ordinary person/law student/lawyer/judge/prosecutor) - Provides differentiated services based on different identities - Complete legal source retrieval (laws/administrative regulations/judicial interpretations/guiding cases/typical cases) - Original legal article citation and cross-reference sorting ## Core Trigger Conditions (Trigger if any is met) **High Priority (Must Trigger)**: - Explicit request to find legal articles/regulations/judicial interpretations/regulatory documents - Request to determine legality/illegality ("Is it illegal?""Is it legal?""Am I liable?") - Request to find compensation standards/compensation amounts/liability determination/procedural requirements - Asking "Based on which law?""What does the law stipulate?""What is the legal basis?" **Medium Priority (Trigger based on context)**: - "What to do?""How to defend rights?""Can I sue?" - "What procedures are needed?""What conditions are required?" - "What else can I claim?""Where can I file a complaint?" ## Application Scenarios - Labor disputes: illegal termination, economic compensation, work-related injuries, social security, job transfer, etc. - Contract disputes: deposit, liquidated damages, breach of contract liability, sales contracts, etc. - Tort liability: traffic accidents, personal injury, medical accidents, environmental pollution, etc. - Marriage and family: divorce property, child custody, estate inheritance, etc. - Administrative/criminal/corporate finance, etc. ## Non-Triggering Scenarios - Only asking about legal concepts/terminology explanations (not retrieval-related) - Only requesting lawyer/legal service recommendations - Only discussing legal news/case stories (not involving specific regulations) - Only asking about legal examination/study questions **Note**: Even if the user does not explicitly request a "retrieval report", this skill will be triggered as long as the issue involves searching, organizing, interpreting, or applying legal norms.
Formal mathematical reasoning for research papers — derive equations, write proofs, formalize problem settings, select statistical tests, and generate LaTeX math notation. Use when the user needs mathematical derivations, theorem proofs, notation tables, or statistical analysis formalization.
End-to-end user research assistant — qualitative and quantitative. Use this skill whenever the user mentions user research, user interviews, discussion guides, interview guides, research plans, qualitative research, quantitative research, user surveys, survey design, usability studies, participant recruitment, research synthesis, interview transcripts, research reports, running studies with AI, or explicitly mentions Cookiy AI. Also trigger when users want to talk to customers, conduct discovery research, create a study or survey, analyze interview data, run AI-moderated interviews, or collect survey responses. Covers the full lifecycle: planning studies, creating discussion guides, running AI-moderated interviews (real or synthetic) via Cookiy, designing and distributing surveys, and synthesizing results into reports.
Sync verified experiment results from the code repo or a code worktree into the paper's daily experiments log and project memory. Use when results in code/docs/results, code/docs/reports, code/docs/runs, worktree docs, logs, or user-confirmed metrics should be promoted into paper-facing evidence.
Prepare a research artifact package for conference artifact evaluation, reproducibility review, badges, supplementary material, or post-acceptance artifact release. Use this skill whenever the user needs install instructions, reviewer-facing reproduction commands, Docker or environment checks, data/checkpoint packaging, hardware/runtime estimates, anonymized or public artifact metadata, artifact evaluation forms, or a claim-to-artifact reproducibility audit for ML/AI venues.
Audit whether an ML or AI paper's experimental baselines are necessary, fair, current, and reviewer-proof. Use this skill whenever the user is planning experiments, comparing methods, choosing baselines, worried about missing SOTA or unfair comparisons, preparing a reviewer-proof experiment section, or converting a literature review into must-have, should-have, optional, and not-comparable baselines.
Design hypothesis-driven ML/AI experiments before running them. Use this skill whenever the user wants to plan experiments, ablations, baselines, metrics, controls, seeds, logging, stop conditions, reviewer-proof evidence, or an experiment matrix for a paper claim before using run-experiment or writing results.
Use when designing or auditing computer science experiments, evaluation plans, baselines, metrics, ablations, datasets, statistical tests, benchmarks, validity threats, or reproducibility claims.
Use when inspecting, cleaning, understanding, reproducing, or auditing academic research code repositories, especially when README commands, datasets, checkpoints, experiments, or paper claims need verification.