Results for “pr”

Turn a customer's stated goals into a success plan that predicts the renewal. It sets time-to-value milestones, names an owner on both sides for each, and picks the leading indicators that tell you a year early whether this account renews. Built for B2B customer success teams, customizable to your CRM and product analytics. Trigger on "build a success plan", "onboarding plan for this account", "what are the milestones", "how do I know they'll renew", "time-to-value plan", or any post-sale planning.

Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.

NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance/Innovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget/scope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with acade

Set up and verify a connection to an NI USRP radio (USRP E320, N300, N310, N320, N321, X300, X310, or X410) using Wireless Testbench. Use when connecting a USRP for the first time, configuring radio hardware, troubleshooting connection failures, or verifying a radio setup. Covers host inspection (OS, NIC type/speed/MTU), device discovery (findsdru, probesdru), UHD version checking, programmatic radio configuration, and basebandTransceiver verification. Also use when the user mentions USRP setup, radio not found, connection errors, dropped samples, or network configuration for SDR hardware.

This skill should be used when a SKILL.md file needs compression, deduplication, or token reduction. It provides an embedding-based compression pipeline that detects and removes redundant chunks within SKILL.md files using local embeddings (all-MiniLM-L6-v2). Two-pass approach: (1) free intra-skill deduplication via cosine similarity clustering, (2) optional LLM-judged graded eval to detect pretraining overlap. Typical result: 25-46% token reduction with zero quality loss. This skill is not intended for editing skill content, creating new skills, routing optimization, or cross-skill deduplication.

The living embodiment of WinDAGs V3 — the AI orchestration platform where agents accumulate genuine expertise. Knows the full constitution, convention process, tradition positions, behavioral contracts, and derivative documents. Advises on architecture with ADR provenance, explains decisions with tradition attribution, grows source material as we build. Activate on "windags", "WinDAGs", "constitution", "convention", "why did we decide", "what does WinDAGs say", "how should we build", "architecture decision", "tradition", "behavioral contract". NOT for building specific DAGs (use windags-architect), creating individual skills (use skill-architect), or managing skill libraries (use windags-librarian).

Run a Codex prompt on a cron schedule or at a specific future time. Codex app Automations is the official, always-on surface for durable recurring schedules — recommend it FIRST; this local skill is a terminal fallback that only fires while its daemon runs. Triggers include "/schedule", "cron", "every Monday 9am", "run this at 3pm", "schedule a codex prompt", "recurring codex task", and one-time future runs ("run once at 3pm", "remind me to check X tomorrow"). For fixed-interval repeats ("every N minutes"), use the separate `loop` skill instead.

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

Guides the creation of high-quality MCP servers that enable LLMs to interact with external services through well-designed tools, covering planning, implementation, testing, and evaluation.

Provides a framework for rolling out organizational changes using the ADKAR model adapted for startups, with communication templates, resistance patterns, and change fatigue management.

Evaluates prospective partnerships by classifying partner tier, designing a 90-day joint GTM plan, modeling revshare economics, and surfacing kill criteria for under-performing partnerships.

Plans, executes, and validates Google Kubernetes Engine (GKE) cluster upgrades and maintenance operations for both Standard and Autopilot clusters, producing upgrade plans, checklists, and runbooks with gcloud commands.

Guides authentication and authorization to Google Cloud services, covering human users, service identities, Application Default Credentials (ADC), and best practices for secure access.

Evaluates Google Cloud workloads against the Reliability pillar of the Well-Architected Framework, providing actionable recommendations for building, deploying, and managing reliable systems.

Manage AWS S3 buckets and objects: create buckets, upload/download files, configure lifecycle policies, set up static websites, manage permissions, and implement cross-region replication.

Ask visual questions about video clips using a VSS agent's video_understanding tool, requiring a fresh look at frames rather than prior metadata or search results.

Trains, evaluates, exports, quantizes, and runs inference for a Grounding DINO model that detects objects described by text prompts without a fixed class vocabulary.

Assesses NumPy code for cuPyNumeric migration readiness by analyzing source code against an API support manifest and GPU-scaling idioms, producing a structured verdict with per-finding reasoning.

Score a clinical ASR manifest against a chosen NIM, produce a five-section KER leaderboard, and route the user via a post-eval decision tree.

Create an end-to-end customer journey map with stages, touchpoints, emotions, pain points, and opportunities to identify friction points and improve user experience.

Forces AI agents to investigate before editing or running destructive commands, improving code quality by requiring concrete facts like importers, data schemas, and user instructions.

Classifies ECC components (skills, commands, rules, hooks) into DAILY and LIBRARY buckets based on evidence from the actual codebase, producing an install plan and verification report.

Write long-form content like blog posts, guides, tutorials, and newsletters with a distinctive, non-generic voice derived from supplied examples or brand guidance.

Provides HIPAA-specific decision gates and guardrails for healthcare software, directing users to implementation and review skills for PHI handling, audit logging, and compliance.

Provides architecture and implementation patterns for building modules with the tinystruct Java framework, covering dual-mode CLI/HTTP actions, JSON serialization, database persistence, MCP tools, and more.

Provides a fast in-memory DataFrame library for datasets that fit in RAM, with lazy evaluation, parallel execution, and an Apache Arrow backend for ETL pipelines and analytics.

Generates a step-by-step construction plan from a one-line objective, where each step is self-contained and executable by a fresh agent in a new session.

Designs and reviews feedback loops for AI agents to ensure goals are machine-decidable, loops are damped, and human judgment is preserved.

Translates Figma designs into production-ready application code with pixel-perfect accuracy, using the Figma MCP server to fetch design context, screenshots, and assets.

Audits .NET test mock usage by tracing each mock setup through the production code's execution path to find dead, unreachable, redundant, or replaceable mocks.

Manage design systems in Stitch using MCP tools, including retrieval of assets, creating or updating design systems, and applying them to screens.

Detects and triages explicitly-unbounded AEM queries (p.limit=-1 or setLimit(-1)) that cause OOMs, safely capping only where provably safe and escalating others for human pagination.

Enforces output quality, intellectual honesty, and continuous improvement across security analysis agents with anti-rationalization guards, self-critique loops, retry protocols, and quality gates.

Guides selection of Qdrant deployment options: local mode, Docker, self-hosted, Qdrant Cloud, Hybrid Cloud, or Qdrant EDGE based on latency, control, and production needs.

Build, scaffold, and deploy Power Automate cloud flows programmatically via the FlowStudio MCP server, including constructing flow definitions, wiring connections, deploying, and testing.