Results for “robot-policy”

Evaluates NVIDIA Cosmos Policy on LIBERO and RoboCasa simulation environments. Use when setting up cosmos-policy for robot manipulation evaluation, running headless GPU evaluations with EGL rendering, or profiling inference latency on cluster or local GPU machines.

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

Go-to-market strategy for AI products, covering positioning, pricing, and enterprise objections.

Provides a local-first agent framework for observable execution, composable tool use, and multi-agent orchestration with policy-guarded execution.

Go-to-market strategy for AI products. Use when positioning AI products, handling "who is responsible when it breaks" objections, pricing variable-cost AI, choosing between copilot/agent/teammate framing, or selling autonomous tools into enterprises.

Design or revise a company's commercial policy governing discounts, approver thresholds, exception flows, and deal frameworks using data-backed matrices and linting tools.

Track legislation, regulatory actions, and legal developments affecting prediction markets, crypto, and AI agents, with stage, impact, affected protocols, and operator actions.

Prüft internationale Exportfälle für Robotik: Frist, Form, Zuständigkeit, Rechtsweg und Sofortmaßnahmen; liefert eine Fristen- und Risikoampel mit Sofortschritten.

Fine-tunes and evaluates OpenVLA-OFT and OpenVLA-OFT+ policies for robot action generation with continuous action heads, LoRA adaptation, and FiLM conditioning on LIBERO simulation and ALOHA real-world setups.

`analysis-agent`/`task-agent`/`review-agent`: primary-Skill-selected for races, locks, optimistic conflicts, or worker overlap; never task owner; skip without concurrency impact.

Enforce organizational security policies across Kubernetes clusters and CI/CD pipelines using Open Policy Agent (OPA) and Gatekeeper, including writing Rego policies, deploying admission controllers, and testing policies locally.

Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.

AutoAugment: Learning Augmentation Strategies from Data

Expert guidance for OPA (Open Policy Agent), the CNCF policy engine for unified authorization across the stack. Helps developers write Rego policies for Kubernetes admission control, API authorization, infrastructure-as-code validation, and data filtering — enforcing security policies as code.

Defines a conceptual standard for autonomous agents, covering cognitive architecture, sensory integration, and tool-use protocols.

Generates HR policies for remote work, PTO, harassment prevention, and workplace safety compliance

Create, apply, and manage AgentRC policies to customize readiness scoring, disable checks, override impact levels, set pass-rate thresholds, and enforce CI gating.

Use with analysis-agent or task-agent for task-local transaction, isolation, and conflict decisions. Do not use without a transaction decision or as task owner.

Route a task to the most fitting skill or agent. Parses user intent, checks installed-skill inventory, and picks the best match (single skill or chain).

Expert en robotique avancée (ROS, SLAM, manipulation, autonomous navigation, DZ context)

Goal-Oriented Action Planning skill for world state and action costs.

Build and run the owner's self-branding system in a structured way across LinkedIn, Substack, dev.to, X, profile rewrites, recurring series, and comment strategy. Use when the owner asks for positioning, a branding plan, profile updates, content drafts, a publishing sequence, comment ideas, or help maintaining a consistent public voice. Optimize for operator credibility: specific experience, defensible numbers, concrete artifacts, and category ownership.

Design pricing, packaging, and monetization strategy for a software or AI product. Use when the team needs plan structure, free-tier logic, usage vs seat pricing tradeoffs, paywall timing, or monetization guardrails tied to product value and costs.

Expert strategy advisor for products built on LLMs or agents — not general product strategy (see `product-strategy` for that). Use this — proactively and without waiting to be asked — whenever choosing where to apply AI in a product, deciding between RAG and fine-tuning, designing how much autonomy an AI feature should have, evaluating whether an AI feature is actually defensible, or deciding whether to add AI to a feature at all. Also triggers for: "should this be an agent or a simple LLM call", "how much autonomy should this feature have", "RAG vs fine-tuning", "is this AI feature defensible", "our AI feature keeps hallucinating and users don't trust it", "should we build this with AI or just ship it deterministic", "AI product wedge", "what happens to this feature when the models get better", "human-in-the-loop design for AI features". Produces a decision-focused brief: the wedge, the architecture choice, the autonomy level, and the defensibility bet — each with an explicit trade-off.

[OMX] Strict autonomous loop: $deep-interview -> $ralplan -> $ultragoal (+ $team if needed) -> $code-review -> $ultraqa

Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.

Drafts a Bring Your Own Device (BYOD) policy for U.S. employers governing personal device access to company systems. Covers MDM enrollment, encryption, remote wipe authority, privacy expectations, data classification, and regulatory overlays (HIPAA, GLBA, SOX, GDPR). Use when creating or updating BYOD policies, mobile device security policies, or personal device programs.

Use this when the team needs to define permissions, roles, membership rules, resource ownership, or action-level authorization separate from authentication choice. Trigger on requests about access models, RBAC, ABAC, tenancy, or protected operations.

Classifies AI systems under the EU AI Act's risk tiers and maps compliance obligations for conformity, transparency, and human oversight.

Agent profile for PPC and paid-growth risk review before changing ads, budgets, bidding, tracking, or landing pages.

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

Token budget analytics and ROI scoring for AI agent actions. Track token cost per fix, detect wasteful loops, auto-route to fast tier when burn rate is too high. Integrates with token-budget-guard.sh hook. Triggered by /cost-report, "how much did that cost", "token usage", "am I wasting tokens".

Drive data strategy with governance frameworks, analytics platforms, AI/ML initiatives, and privacy compliance.

Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues.