Results for “pr”

Agente final do time Code New Project Agents do Reversa. Decompõe um PRD em componentes lógicos e gera specs SDD (Spec-Driven Development) por componente, com score automático de qualidade. Use quando o usuário digitar "/reversa-spec-sdd", "reversa-spec-sdd" ou quando invocado pelo orquestrador `/reversa-new`. Lê `_reversa_sdd/prd.md` e produz `_reversa_sdd/sdd/<componente>.md`. Ao concluir, faz handoff para `/reversa-forward`. Também pode ser usado avulso para avaliar uma spec existente (modo de avaliação) ou para gerar spec única a partir de qualquer entrada que o usuário forneça. Entrega: arquivos `.md` no formato SDD com score de qualidade (0 a 100) e análise de gaps.

Orchestrator + builder for distinctive, production-grade frontends that don't look AI-generated. Derives stack and design context from product-soul/PRD/specs, then runs the anti-slop chain — explore distinct directions, lock a DESIGN.md system, build from golden examples with mandatory polish + every interactive/empty/loading/error state, then review. Load when the user asks to build a UI, design a frontend, build a landing page or dashboard or web app, beautify or redesign a page, make a UI look premium/playful/editorial, says "build me a frontend", "make this not look AI-generated", "design this interface", "give this real polish", or "frontend design". Routes to design-direction, design-system, design-review.

Use this skill when a user needs KOL or influencer research, creator email lookup, similar-creator discovery, outreach-list building, influencer prospecting, or a contact table from TikTok, Instagram, or YouTube profile URLs. It uses AIsa's WaveInflu APIs to find verified creator emails, match similar YouTube or TikTok creators, enrich each recommended profile with contact emails, and return an outreach-ready Markdown table without inventing missing data. Use when: the user needs YouTube search, trend discovery, channel research, or SERP analysis.

Models cascading what-if scenarios across all business functions to prepare for compound adversity, using a structured six-step framework that maps variables, domain impacts, cascade effects, severity levels, early warning signals, and hedging strategies.

Plans a sprint with capacity estimation, story selection, dependency mapping, and risk identification. Use when preparing for sprint planning, estimating team capacity, or balancing scope against velocity. Triggers on: sprint planning, sprint plan, capacity planning, sprint scope, story points, sprint goal, backlog grooming, sprint prep, what do we build this sprint.

Use only for video game projects; route research, validation, roadmap, metrics, and launch work through the game pack

Turn an approved PRD or implementation goal into SRS/FRS technical requirements (How), architecture, contracts, and verification decisions.

Turn account data into a QBR that earns the renewal. It builds the value-delivered story, adoption against the goals they signed up for, the open risks stated plainly, the forward roadmap, and the expansion ask, then hands you a deck outline. Built for B2B customer success teams, customizable to your CRM and product analytics. Trigger on "build a QBR", "prep the business review", "quarterly review for this account", "what do I show the customer", "value story for the renewal", or any account-review prep.

Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)

Plan a feature from BRD-lite brief or clear intent into PRD (What), decisions, implementation plan, and task slices.

Validate PRD, UX, Architecture and Epics specs are complete. Use when the user says "check implementation readiness".

Battle-tested PyTorch training recipes for all domains — LLMs, vision, diffusion, medical imaging, protein/drug discovery, spatial omics, genomics. Covers training loops, optimizer selection (AdamW, Muon), LR scheduling, mixed precision, debugging, and systematic experimentation. Use when training or fine-tuning neural networks, debugging loss spikes or OOM, choosing architectures, or optimizing GPU throughput.

Use when building, refactoring, or documenting Graft apps and proxies, including when asked to create a tool server, API server, dual-protocol server, or MCP-HTTP bridge. Graft's core thesis: define tools once and serve them as both HTTP REST endpoints and MCP tools from the same server, with discovery, docs, and OpenAPI generated automatically. Covers concrete actions such as defining tools and handlers, configuring authentication middleware, setting up HTTP and stdio transports, generating OpenAPI documentation, wrapping existing APIs via proxy mode, and wiring up the full CLI workflow.

Architecture discovery for Sage/Acorn projects — map existing codebase, discover post types, routes, ACF field groups, Livewire components, Service Providers, identify architectural gaps, architecture decision records, ADR, component boundary map, data flow diagram, risk register, implementation sequencing, discovery session, understanding unknown codebases, use before architecting or building new features, output contract, discover-abilities

Editor-chefe do Time Reversa Docs. Gera index.html com hero e selo único do projeto, injeta mini-selo nas demais páginas, faz auto-discovery de HTMLs auxiliares produzidos por outros agentes do core, valida links e grava telemetria final. Ative com /reversa-docs-publisher, reversa-docs-publisher, regenerar index, refazer selo, atualizar índice.

Expert guidance for integrating and building applications with shadcn/ui components, including component discovery, installation, customization, and best practices.

Use when asked to fix, build, refactor, review, verify, or release software and the work is non-trivial — including delivering a change request (issue, ticket, or request) from intake through planning, gates, implementation, review, verified PR, and authorized post-merge release. neckbeard routes the change through framing, discovery, design, implementation, review, verification, delivery, and learning — choosing the smallest *safe* intervention, proving it at the real delivery boundary, and leaving an inspectable evidence ledger. For change-request / issue-to-PR work, conditionally loads a 9-phase journey with gates, delivery packet, and lifecycle integration. Composes specialist catalog skills rather than replacing them. Not a persona, not a '10x developer' prompt, not a LOC-minimizer. The journey is not loaded for plain fixes, refactors, or reviews that lack an issue/ticket trajectory.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Use to set concurrency and work-in-progress limits for support teams — chat concurrency, personal WIP on async work, and limits as quality protection rather than arbitrary caps. Trigger for "how many chats per agent", "concurrency settings", "WIP limits", "agents juggling too many tickets", async backlog per person, or quality dropping as concurrent load rises.

Applies the BIZBOK enterprise architecture framework to structure analysis, problem solving, and decision documentation.

Runs throwaway experiments to validate feasibility, compare approaches, and surface unknowns before committing to a real build.

Identifies common creative thinking anti-patterns and provides detection methods and alternatives.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Reviews a product document (PRD, spec, design brief) BEFORE implementation to surface holes — undefined edge cases, missing states, policy gaps — by attacking what the document is SILENT about (things unwritten, and things written only for the happy path). Acts as a strict "sign-off manager," ruling Approve / Conditional / Reject and producing a polite, forwardable question list. Works for planners/PMs (self-review before sharing), engineers (blocking questions before coding), and designers (screen states with no mockup). Use whenever the user wants a spec/PRD/plan/brief checked for readiness or gaps, or says "review this spec", "find holes in this PRD", "poke holes in this", "is this plan good to build?", "can I start implementing this?", "what states/edge cases am I missing?", "what should I ask the PM before coding?", "run devil", or pastes/links a planning document and asks whether it's ready to act on. Do NOT use it to: write or draft a new spec, summarize or translate a document, estimate/break down tic

Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

Access and analyze drug information from the DrugBank database — drug properties, interactions, targets, pathways, chemical structures, and pharmacology data. Use when working with pharmaceutical data, drug discovery research, drug-drug interaction analysis, target identification, chemical similarity searches, ADMET predictions, or any task needing detailed drug and drug-target records from DrugBank. Part of the AlterLab Academic Skills suite.

Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Extracts and reconciles medical provider, wage-loss, and insurance/lien data from personal injury plaintiff fact sheets and initial disclosures against builder draft responses. Use when the user mentions PFS analysis, medical provider reconciliation, wage loss audit, insurance lien tracking, PI discovery reconciliation, builder response validation, MDL plaintiff data extraction, FRCP 26(a)(1) disclosures, treatment chronologies, or specials spreadsheets.

Facilitate a structured sprint retrospective to surface insights and produce actionable improvements with owners and deadlines.

Erstellt Entwürfe für Agio und Kapitalerhöhungsstrukturen in der Corporate-Praxis, inklusive Anträgen, Begründung und Anlagenlogik.

Analyzes an app from video, screenshots, or description, then generates implementation stories in dependency order.

Guides a structured brainstorming session that surfaces user ideas, narrows options, and produces a spec file for later planning.