Results for “art”

Benchmarks medical AI agents on synthetic EHR tasks, measuring success rates for data retrieval, temporal aggregation, and threshold-based conditional logic with exact-match scoring.

Use this skill for crypto market research, tokens, narratives, liquidity, risks, source verification. Trigger when the task involves crypto work related to Crypto Market Research, production implementation, audits, debugging, strategy, or validation.

Use this skill before architecture, dependency, integration, parser, bot, UI, deployment, or non-trivial bugfix tasks. It forces Codex to search GitHub, compare open-source solutions, check licenses, and extract reusable patterns without copying unsafe code.

Compile deep repository evidence into artifact-bound correctness doctrine, including authority/law/proof maps, owned invariants, failure archaeology, and knowledge routing, with an optional minimal repository-specific skill portfolio.

Manage biological datasets and models with LaminDB, an open-source lineage-native lakehouse. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation, collections, branches, storage, and workflow integrations.

Search and analyze news articles with media bias context, get balanced perspectives, access live stock/ETF/crypto data with AI analysis, and find memes by semantic meaning.

Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.

Use quando o usuário solicitar a ativação do Gemini CLI para revisão de código, revisão de plano ou processamento de contexto amplo (>200k). Ideal para análise abrangente exigindo janelas de contexto grandes. Usa Gemini 3 Pro por padrão para raciocínio e codificação de última geração.

Use when a research design spec exists and the user is ready to translate it into a concrete implementation plan with phased tasks, artifacts, and verification criteria. Produces a research execution plan organized in canonical research phases — collection, preparation, analysis, robustness, writing, submission.

Evaluates the trade-off between file size reduction and image fidelity when encoding radio astronomy data using JPEG2000, benchmarking both lossless and lossy compression modes to determine the compression ratio at which visual artifacts first appear.

Use when auditing an ESEC/FSE research-track submission for HotCRP readiness, covering the mandatory paper-registration step, the ACM Primary Article Template page budget, double-anonymous "heavy" anonymity, the open-science Data Availability statement, PACMSE publication, and desk-reject triage before the AoE cutoff.

Run STORM phase 2 — outline generation. This skill should be used when the user asks to "generate an outline for a storm article", "draft a wikipedia-style outline", or invokes /storm:outline. Produces a draft outline from parametric knowledge then refines it using the research information table.

Use when auditing a KDD submission for readiness in either cycle, covering Research vs ADS track declaration, OpenReview profile completeness for all authors, the ACM sigconf 8-content-page budget, double-blind checks, in-paper artifact references, resubmission declarations, and desk-reject triggers specific to ACM SIGKDD.

Use when planning an IPSN-lineage project timeline from track/venue selection through the CPS-IoT Week deadline, double-blind submission, deployment and hardware logistics, rebuttal, the Best Research Artifact Award, and the dual ACM/IEEE camera-ready — with honest handling of the fact that IPSN merged into SenSys.

Summarizes, condenses, or extracts text, transcripts, and key points from URLs, articles, web pages, PDFs, podcasts, YouTube videos, and local files. Acts as a fallback transcription tool when the user asks to transcribe, digest, recap, or get the gist of a link, video, or document.

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.

Run STORM phase 1 — multi-perspective research. This skill should be used when the user asks to "research a topic for an article", "find sources on X from multiple perspectives", "do storm research on X", or invokes /storm:research. Discovers personas, runs simulated Q&A per persona in parallel, and produces an information table + deduplicated sources.

Especialista em CrewAI - o principal framework multi-agente baseado em papéis utilizado por 60% das empresas da Fortune 500. Abrange design de agentes com papéis e objetivos, definição de tarefas, orquestração de crew, tipos de processo (sequencial, hierárquico, paralelo), sistemas de memória e flows para workflows complexos. Essencial para construir equipes colaborativas de agentes IA. Use quando: crewai, equipe multi-agente, papéis de agentes, crew de agentes, agentes baseados em papéis.

Evidence-based investigation of issues, bugs, API calls, integrations, and other aspects of software development that need a deep dive to find the root cause and solutions. Use when you need to debug, troubleshoot, diagnose, or figure out why something is broken. Does not review code for quality or style — use code-review for auditing changes or post-code-review-to-pr for posting review feedback to GitHub. Does not assess architectural health or structural risk — use architectural-analysis for architectural concerns. Does not research open-ended options, prior art, or how something works when nothing is broken — use research for that. Does not capture feedback on Han's own skills — use han-feedback for that.

Use to CREATE or REVIEW a Job to be Done (JTBD) in Alan Klement's demand-side, progress-based sense — a job is the progress a customer is trying to make, never a task or feature. Trigger whenever someone wants to write or draft a JTBD, job statement, or job story; figure out the real motivation, struggle, or progress behind a feature, request, or product; understand why customers switch, adopt, or churn; turn interview notes or research into jobs; or pressure-test, sharpen, or rewrite existing JTBDs into proper jobs. Also trigger on "grill my JTBD", "is this a real job or just a task/feature", "what's the actual job here", "why would they switch". In an A-Team target repo, jobs are durable artifacts — this skill writes docs/product/jtbd/NN-<slug>.md per CONTRACT.md and never deletes or silently rewrites a job. Do NOT use for writing PRDs, roadmaps, tickets, or acceptance criteria (those consume a finished JTBD), or for implementation user stories.