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Found 205 Skills
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.
ISO 27001 ISMS implementation and cybersecurity governance for HealthTech and MedTech companies. Use for ISMS design, security risk assessment, control implementation, ISO 27001 certification, security audits, incident response, and compliance verification. Covers ISO 27001, ISO 27002, healthcare security, and medical device cybersecurity.
Push Packer build metadata to HCP Packer registry for tracking and managing image lifecycle. Use when integrating Packer builds with HCP Packer for version control and governance.
AI governance and compliance guidance covering EU AI Act risk classification, NIST AI RMF, responsible AI principles, AI ethics review, and regulatory compliance for AI systems.
Vercel account, team, and billing management including plans and spend controls. Use when managing teams, accounts, or cost governance on Vercel.
Unity Catalog governance patterns, permissions models, security best practices, and policy enforcement for enterprise data governance.
Cluster and attribute related wallets — funding chains, shared signers, CEX deposit patterns. Use when tracing wallet ownership, comparing two wallets, finding wallet relationships, governance voters, or related address clusters.
Answer a question about Sky governance using the local knowledge base
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Generate Planning & Management documentation for SDLC projects. Covers Project Vision & Scope, SDP, SCMP, QA Plan, Risk Plan, SRS, and Feasibility Study. Use when starting a new project, conducting project governance, or establishing the planning...
Implements knowledge graphs for AI-enhanced relational knowledge. Covers ontology design, graph database selection (Neo4j, Neptune, ArangoDB, TigerGraph), entity extraction, hybrid graph-vector architecture, query patterns, and AI integration. Use when implementing knowledge graphs, designing ontologies, extracting entities and relationships, selecting a graph database, or building hybrid graph-vector search. Use for knowledge graph, ontology design, entity resolution, graph RAG, hallucination detection. For architecture selection and governance, use the knowledge-base-manager skill. For document retrieval pipelines, use the rag-implementer skill.
Guides edge and tactical autonomous systems—perception-planning-control under latency and safety constraints; behavior trees/state machines vs learned policies; human-on-the-loop; geofencing, no-strike rules, mission abort; sim and field testing; ROS2/middleware patterns; sensor fusion; degraded modes; autonomy audit logging. Use for UAS/autonomous stacks, safety rules, HITL, sim-to-field validation, fail-safe—not LLM products (ai-engineer), LLM red team (ai-redteam), safeguard serving (ml-infrastructure-engineer-safeguards), governance only (ai-risk-governance), MCU firmware without autonomy (embedded-real-time-software-engineer), plant PLC/DCS (control-software-developer), HIL security bench (hardware-in-the-loop-security-tester).