10.7K skills · sorted by relevance
Manage the AI models offered through OpenWork, including model additions, removals, aliases, and pricing discounts. Validate configuration consistency and trigger the GitHub workflow that refreshes models from models.dev, then report its merge or failure result.
Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
Keep chat-ui model settings aligned with the HuggingFace router. Add descriptions for newly available models, check reasoning support, enable artifact panels based on model size, and remove settings for models no longer served.
Manage machine learning models and their versions in the Agent Platform Model Registry. List registered models, inspect details, upload new versions, update names or descriptions, and delete models through workflows that require confirmation for changes.
Find AI models for a specific task using official Hugging Face benchmark leaderboards. Compare scores, model sizes, and licenses, with memory-based estimates of device suitability and flags for models requiring quantization or API access.
Add AI models to Kiln or find available models missing from its list. The skill checks provider model identifiers and capabilities, updates model settings, runs tests against real APIs, and drafts a Discord announcement.
Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.
Browse and search OmniRoute's AI model catalog from the command line. Filter by provider or capability, check model aliases, validate compatible models before adding them, and maintain manually registered model details.
Browse and search OmniRoute's AI model catalog from the command line. Filter by provider or capability, check model aliases, validate compatible models before adding them, and maintain manually registered model details.
Maintain an application's supported language models by checking provider availability, adding models, and keeping documentation, live tests, and model-specific request settings aligned. It also checks provider model identifiers and candidates for the recommended list.
Run Unity tests in EditMode or PlayMode, selecting specific assemblies, namespaces, classes or methods. Review individual outcomes and durations, filter results, and include failure messages, logs or stack traces to investigate problems.
Add support for new model architectures in NeMo AutoModel, from inspecting configuration files to implementing layers, converting saved weights, registering models, and testing numerical agreement with Hugging Face implementations.
Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Plan how to serve a model on Amazon SageMaker AI. The skill uses model type, traffic, response time, and cost constraints to recommend a deployment route, check instance quotas, and coordinate specialized setup and deployment skills.
Compare models available through the HuggingFace router with chat-ui configuration, research missing models, and write short English descriptions of their architecture and capabilities. Add entries to both production and development configuration files and commit the changes.
Plan which model tier handles which work BEFORE execution begins — a high-cognition model deeply understands the problem, lays the foundations, then emits a modular plan assigning each module the cheapest tier that can safely execute it, with escalation tripwires and one-way-door protection. Advisory only: it announces "next module → tier X / model Y" at each boundary and the HUMAN switches models — harnesses like Cursor cannot switch mid-run. Load when the user asks which model to use, wants a model plan, model tiers, model-tier routing, assign models to tasks or modules, says "cheap model got stuck", "which model for this task", "cost-efficient model choice", or when implementation-plan / problem-to-plan need a model: tier column. NOT dynamic-routing (plan-path selection after failure) — this skill assigns cognition tiers to work.
The English summary is being prepared. Showing the original description.
Update context-mode from GitHub, rebuild and reinstall it, and configure hooks that connect it to the agent environment. Review the actual version and upgrade results in a checklist, then restart the session.
Combine specialized language models without retraining using mergekit. The skill explains how to choose merging methods, configure each model’s contribution, compare resulting variants, and prepare a merged model for evaluation and deployment.
Pin the current Codex session to a chosen model through Weave Router. This short alias accepts a model ID, runs the included script, and reports the router’s response while allowing the current turn to continue.
Switch automatic model selection to lower-cost options for agent tasks, either for one session or through a saved setting. Explicit model choices remain in effect, while architecture and security review tasks use a separate economy selection.
Helps configure training across multiple GPUs or machines in NeMo AutoModel. Guides strategy selection, YAML settings, and parallelism choices that split model computation or data, with constraints for large models, long sequences, and expert models.
Customize Jetson and Tegra power modes in BSP files, the device's system software configuration. Adds, removes, or edits nvpmodel profiles, adjusts frequency limits, changes the boot default, and guides deployment of the updated configuration.
Locate CCSPlayerPawn::SetModelFromClass in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerPawn::SetModelFromClass" (symbol CCSPlayerPawn_SetModelFromClass). Shortlist via callers of CBaseModelEntity::SetModel inside pawn model-selection code: this variant takes the pawn's class model. The body resolves a cached model precache handle from the player class and calls SetModel. Distinguish from SetModelFromLoadout by the ABSENCE of loadout/inventory item lookups in the call chain. Trigger: CCSPlayerPawn_SetModelFromClass, CCSPlayerPawn::SetModelFromClass
The English summary is being prepared. Showing the original description.
Find available AI models across configured providers and browse a catalog that includes provider-specific variants. Inspect individual models and create or update aliases, alternative names used to refer to models.