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Found 1,669 Skills
Server-side tracking pipeline audit covering server-side Google Tag Manager (sGTM), Meta CAPI Gateway, Conversions API health, event deduplication via event_id, server-side hit ratio targets, pixel debugging, and PII hashing discipline. Use when user says server-side tracking, sGTM, server-side GTM, server-side tagging, CAPI, Conversions API, CAPI Gateway, Meta Conversions API, event deduplication, event_id, pixel debug, pixel health, Pixel/CAPI audit, first-party tracking, iOS 14.5 recovery, or server-side hit ratio.
End-to-end data engineering pipeline for Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
Manage Harness Software Supply Chain Assurance (SSCA) via MCP. Configure automated SBOM generation with CycloneDX or SPDX formats, set up artifact signing and attestation with Cosign, define supply chain security policies using OPA, and track SLSA provenance levels. Use when asked to generate SBOMs, sign artifacts, enforce supply chain policies, track software provenance, or manage SLSA compliance. Do NOT use for OPA pipeline governance policies (use create-policy instead) or vulnerability scanning (use security-report instead). Trigger phrases: SBOM, software bill of materials, supply chain security, SLSA, artifact signing, cosign, provenance, attestation, CycloneDX, SPDX, supply chain policy.
Create OPA governance policies for Harness via MCP. Define policies that enforce compliance rules on pipelines, services, environments, feature flags, artifacts, code repositories, templates, SBOM, security tests, Terraform, GitOps, connectors, secrets, and more. Use when asked to create, write, fix, or explain an OPA policy, Rego rule, deny rule, governance policy, compliance rule, or policy-as-code for any Harness entity. Trigger phrases: create policy, OPA policy, governance policy, compliance rule, rego policy, deny rule, enforce policy, security policy, supply chain governance.
Especialista em infraestrutura e entrega contínua no SynkOS. Use esta skill quando o usuário pedir para configurar um pipeline de CI/CD, dockerizar um serviço, preparar ou executar um deploy, configurar monitoramento e alertas, auditar infraestrutura, gerenciar secrets e variáveis de ambiente, ou fazer perguntas como "configure o CI para o projeto X", "crie o Dockerfile para Y", "o que verificar antes do deploy?", "como configurar logs e alertas?", "audite a infraestrutura", "valide o ambiente de produção". Ative também para criar documentação de rollback, validar saúde pós-deploy, e garantir que toda mudança de ambiente está versionada como código.
Submit a code review to GitHub via the GitHub API. Use this as the final step in a code review pipeline to post review findings to a PR.
Build ETL pipelines and analytics dashboards for Harvard Art Museums API data with MySQL storage and Streamlit visualization
Generic framework for converting external events (SMS, meetings, social mentions) into brain-ingestible signals. Define a transform function, register a webhook URL, and incoming events get processed through the brain pipeline.
Generate, edit, upscale, variate, and style-transfer images using the AgentOS multi-provider image pipeline with automatic fallback and character consistency.
10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.
Build messaging agents and apps with Spectrum — Photon's unified messaging SDK. Write your handler logic once and ship it across iMessage, WhatsApp Business, the terminal, or a custom platform. Spectrum is multi-platform by design and is becoming multi-language; the current SDK is `spectrum-ts` (TypeScript), with additional language SDKs planned. Use this skill for any Spectrum question — quickstart, multi-platform setup, receiving messages, content builders, spaces and users, reactions and replies, platform narrowing, the built-in providers (iMessage cloud/local/dedicated with message effects, Terminal TUI test harness, WhatsApp Business 1:1), custom event streams, graceful shutdown, building your own provider with `definePlatform`, and the production architecture patterns Photon uses internally to ship agents that live natively inside IM apps (five-stage inbound pipeline with debounce → batch flush → mark as read → generate → send, in-flight cancellation with abort signals, drain-in-handler, carry-forward, idempotent retries via stable client GUIDs and a startIndex resume cursor, per-resource memory scope `resourceId` vs `threadId`, durable job-failure audit log). This is the entry point for the skill; consult the topic files in this directory for full reference. Keywords: spectrum, spectrum-ts, photon, unified messaging, multi-platform, multi-language, im agent, messaging agent, imessage, whatsapp, whatsapp business, terminal, tuichat, definePlatform, custom platform, platform provider, platform narrowing, app.messages, Spectrum(), space, send, reply, react, tapback, typing indicator, responding, startTyping, stopTyping, content builder, text, attachment, voice, contact, richlink, poll, group, custom content, message effects, bubble effect, screen effect, line model, dedicated line, shared pool, custom events, app.stop, lifecycle, SIGINT, graceful shutdown, message queue, debounce, batch, in-flight, cancellation, abort controller, carry forward, idempotent retry, client guid, dedup, deduplication, startIndex, resume cursor, working memory, resourceId, threadId, per-resource memory, job failure, audit log, race condition, worker crash, retry, pg-boss, queue worker, conversational agent, chat agent, native messaging, agent architecture, production agent, spectrum patterns, best practices.
GPU-accelerate Python code using CuPy, Numba CUDA, Warp, cuDF, cuML, cuGraph, KvikIO, cuCIM, cuxfilter, cuVS, cuSpatial, and RAFT. Use whenever the user mentions GPU/CUDA/NVIDIA acceleration, or wants to speed up NumPy, pandas, scikit-learn, scikit-image, NetworkX, GeoPandas, or Faiss workloads. Covers physics simulation, differentiable rendering, mesh ray casting, particle systems (DEM/SPH/fluids), vector/similarity search, GPUDirect Storage file IO, interactive dashboards, geospatial analysis, medical imaging, and sparse eigensolvers. Also use when you see CPU-bound Python code (loops, large arrays, ML pipelines, graph analytics, image processing) that would benefit from GPU acceleration, even if not explicitly requested.