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Found 183 Skills
Use when planning, running, comparing, or recording computational experiments, benchmarks, ablations, autonomous research loops, overnight runs, training runs, or exploratory variants.
Use when verifying citations, bibliography, manuscript claims, source support, factual accuracy, numerical results, citation drift, or evidence provenance in academic work.
Use when building research dashboards, annotation tools, data browsers, paper-supporting demos, SOTA explorers, experiment viewers, or frontend interfaces for academic projects.
Split Markdown documents into paragraph blocks with stable IDs and hashes, only replace blocks approved by the user, and output the retention ratio, modification reasons, and issue tracking report. Use when the user asks for "only modify these paragraphs", "partial modification according to review comments", "keep other content unchanged", "generate reviewable modification patch", or requests the rw-revision-patch workflow.
USE FOR RAG/LLM grounding. Returns pre-extracted web content (text, tables, code) optimized for LLMs. GET + POST. Adjust max_tokens/count based on complexity. Supports Goggles, local/POI. For AI answers use answers. Recommended for anyone building AI/agentic applications.
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.
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.
Use this skill for "review this paper", "review this manuscript", "peer review", "review my paper", "critique this manuscript", "review this submission", "give me feedback on my paper", "check my methods", "review my statistics", "review as a peer reviewer", "evaluate this manuscript", "review this PDF", or mentions manuscript review, peer review, paper critique, or methodological review.
Compiles any research input — PDF papers, GitHub repositories, experiment logs, code directories, or raw notes — into a complete Agent-Native Research Artifact (ARA) with cognitive layer (claims, concepts, heuristics), physical layer (configs, code stubs), exploration graph, and grounded evidence. Use when ingesting a paper or codebase into a structured, machine-executable knowledge package, building an ARA from scratch, or converting research outputs into a falsifiable, agent-traversable form.
Guide a focused CS or AI literature review sprint that turns a topic, idea, claim, or project direction into a ranked paper map, closest-work risk assessment, method taxonomy, novelty implications, baseline implications, and next actions. Use this skill whenever the user needs to survey a topic, check novelty, map related work, prepare a project, find canonical or recent papers, decide read/skim/ignore priority, or turn papers into a research direction.
Create a new Git branch or code worktree for experiments, features, baselines, rebuttal fixes, or method revisions. Use when starting an isolated code direction, creating a branch, creating a project-aware code worktree under a project control root, or setting up a worktree with UV sync, IDE config copying, linked assets, and worktree memory.
Pre-submission checklist for LaTeX academic papers. Use when the user wants to submit a paper, check submission readiness, prepare camera-ready, switch to final mode, or verify a paper is ready for a conference deadline.