Results for “ramp”

Produce a ready-to-run onboarding plan — pre-boarding checklist, day-1 agenda, first-week plan, and 30/60/90 ramp milestones with owners.

Entry-point for MoonPay CLI, PayBox agent vault, and Cheshire mcp-server MoonPay/PayBox tools. Use for fiat on-ramp, wallets, swaps, x402, PayBox MCP, or moonpay-* skill discovery.

Generates and runs load tests for API endpoints using k6, Locust, or Artillery. Detects the API framework, creates realistic user behavior flows, executes ramp-up, sustained, spike, and stress profiles, and produces a bottleneck analysis with optimization recommendations.

Use to define what actually predicts support performance in your organisation and build a hiring profile around validated predictors — structured work samples, not vibes. Trigger for "hiring profile", "what should we look for in support hires", "interview scorecard for agents", "reduce bad hires", "work sample test", screening criteria review, or checking whether your interview process predicts ramp and QA.

Use to plan and run peak-season support — what to freeze, pre-stage and measure daily so volume spikes do not become quality and attrition crises. Trigger for "peak season readiness", "holiday staffing plan", "Black Friday prep", freeze policy or KB changes before peak, pre-hire and ramp for surge, daily war room metrics, peak post-mortem, or when last year's peak broke SLA and attrition.

Applies the FedRAMP framework to identify, assess, and mitigate security risks in systems, processes, and data handling.

The on-ramp for any Built GTM analyst skill when your tools are not connected yet. Tells you exactly what to paste or upload to run a given analysis today, in plain language, then hands the data to the right skill. Trigger on "I haven't connected my tools", "how do I run this without connecting", "what do I paste", "run this on my data", "I have a CSV", "bring my own data", or any moment a connector-dependent skill needs data and nothing is connected.

Capture frames or clips from RTSP/ONVIF cameras.

Queue processing with fresh context per phase. Processes N tasks from the queue, spawning isolated subagents to prevent context contamination. Supports serial, parallel, batch filter, and dry run modes. Triggers on "/ralph", "/ralph N", "process queue", "run pipeline tasks".

Solve vehicle routing problems (TSP, VRP, PDP) using NVIDIA cuOpt's Python API with cost matrices, time windows, capacity constraints, and pickup-delivery pairs.

Evaluates RAG pipelines using a filesystem-based benchmark with corpus/ and train.json, running evaluate_rag.py to tune retrieval and generation flags and interpret RAGAS metrics.

Official GSAP skill for gsap.utils — clamp, mapRange, normalize, interpolate, random, snap, toArray, wrap, pipe. Use when the user asks about gsap.utils, clamp, mapRange, random, snap, toArray, wrap, or helper utilities in GSAP.

Builds a Streamlit and FastAPI RAG application that lets users upload documents and query them with natural language through LM Studio.

Capture frames or clips from RTSP/ONVIF cameras.

Capture frames or clips from RTSP/ONVIF cameras.

Capture snapshots, clips, or motion events from RTSP/ONVIF cameras using the camsnap CLI.

Capture frames or clips from RTSP/ONVIF cameras.

Deploy Runtime Application Self-Protection (RASP) agents to detect and block attacks from within application runtime, covering OpenRASP integration, attack pattern detection, and security policy configuration for Java and Python web applications.

Capture frames or clips from RTSP/ONVIF cameras.

Capture frames or clips from RTSP/ONVIF cameras.

Turn raw revenue data into a weekly or monthly wrap-up that drives action, not another dashboard screenshot. Position vs plan, what moved, the risks worth naming, and the specific next moves. Built for B2B revenue teams, customizable to your data and your cadence. Trigger on "write the revenue report", "how did we do this month", "revenue wrap-up", "are we on plan", "monthly number", or any revenue-recap request.

Import HD Map or OpenDRIVE files into a RoadRunner scene using MATLAB. Use when loading driving scenes in RoadRunner or RoadRunner Scene Builder, importing RRHD, OpenDRIVE, or other RoadRunner-supported formats for simulation, or verifying Lanelet2-to-RRHD conversion results visually. Requires rrApp handle from roadrunner-core.

Conduct systematic reviews of privileged accounts to validate access rights, identify excessive permissions, and enforce least privilege across PAM infrastructure.

Translate an approved experiment spec into a launch runbook — platform binding (PostHog primary), feature flag setup, assignment unit, exposure event definition, instrumentation QA, dashboard wiring, ramp plan, monitoring, and rollback procedure. Platform-agnostic core with one strong PostHog adapter shipped; GrowthBook, Statsig, LaunchDarkly, Optimizely, and Eppo documented as a single mapping table the user adapts. Load when a spec is approved and ready to launch, or when the user says "set up the experiment", "wire this up in PostHog", "implement the test", "create the runbook", "launch checklist for this test", or when the experimentation orchestrator routes here.

When the user wants help with revenue operations, lead lifecycle management, or marketing-to-sales handoff processes. Also use when the user mentions 'RevOps,' 'revenue operations,' 'lead scoring,' 'lead routing,' 'MQL,' 'SQL,' 'pipeline stages,' 'deal desk,' 'CRM automation,' 'marketing-to-sales handoff,' 'data hygiene,' 'leads aren't getting to sales,' 'pipeline management,' 'lead qualification,' or 'when should marketing hand off to sales.' Use this for anything involving the systems and processes that connect marketing to revenue. For cold outreach emails, see cold-email. For email drip campaigns, see email-sequence. For pricing decisions, see pricing-strategy.

Guides building Expo Router navigation with file-based routing, layouts, tabs, protected routes, and deep linking.

Generates award-level UI with GSAP motion, bento grids, and Python-driven randomization for layout variety.

Consensus planning entrypoint that auto-gates vague ralph/autopilot/team requests before execution

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

Designing and evaluating AI/ML systems: prompt engineering, RAG design, LLM application patterns, AI safety, evaluation frameworks, MLOps, cost optimization. Use for AI pipelines or eval harnesses.