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Found 5,511 Skills
Set up 3D physics in Unity 6: Rigidbody movement and forces, colliders, triggers vs collisions, layer-based collision, raycasts, and joints. Use when adding a Rigidbody, handling OnCollisionEnter/OnTriggerEnter, tuning collision layers, casting rays, or when the user mentions Unity physics, AddForce, isKinematic, or linearVelocity.
Build a survival-crafting game: resource gathering, inventory, crafting and a tech tree, needs (hunger/thirst/temperature), and base building. Use for a survival or crafting/base-building game.
Structure a Bevy app around its Entity Component System: build the App with plugins, define Component/Resource types, write systems with Query/Res/Commands, filter and order systems, and use the Time resource for frame-rate-independent motion. Use when building or debugging a Bevy game in Rust — when the user mentions Bevy, ECS, App::new, add_systems, Query, Commands, components/systems, or a Cargo.toml depending on bevy.
Independently reviews findings and filters out false positives. Use when consolidated findings need validation against the actual source code. Don't use for reproducing crashes or patching code.
Configures PromQL-based Service Level Objective (SLO) alerting policies for Google Cloud resources registered in App Hub or individually specified. Generates Terraform output. Use when the user asks to configure an SLO or Service Level Objective. Don't use for standard alerting policies.
Guided authoring of an architectural invariant catalog entry through a 6-step interview (elicit, locate, weight, enforce, number, commit). Drives the invariants_scaffold and invariants_add orchestrate verbs — the agent supplies judgment and natural-language elicitation; the verbs own schema validation, file writing, and event emission. Defaults to mode: audit; mode: check is an advanced opt-in. Triggers: 'add an invariant', 'author an invariant', 'enforce an architectural rule', or invariants. Do NOT use for: editing workflow state, running a review, or hand-writing YAML (the verbs write it — never emit catalog YAML yourself).
Verification-ladder R5 review dimension: the mutation-adequacy adequacy backstop. Runs the diff-scoped mutation gate, reads the carrier, and turns surviving / NoCoverage mutants into concrete 'write a test that kills <file>:<line>' follow-ups. Triggers: 'mutation adequacy', 'check mutation score', or /review on a HIGH-tier feature. The mutation SCORE is advisory by default (a config override is needed to block on it). The NoCoverage axis has its own real, functioning block-mode enforcement path under review.mutationEnforcement: block (default budget: 0 uncovered changed lines) — opt-in, same as the score's severity. The diff-scoped CI wiring runs in observe mode unconditionally pending a clean runner verdict; it is not yet a blocking CI backstop. Do NOT run full-tree mutation here (scope:'full' is deferred to R10/v2.12).
Plan, generate, source, normalize, and validate cohesive visual game assets. Use for art direction, style bibles, sprites, tilesets, backgrounds, UI art, icons, textures, concept art, or 3D asset briefs.
Huawei Cloud CCI (Cloud Container Instance) full lifecycle management using hcloud CLI. Covers Namespace, Network, Deployment, StatefulSet, Pod creation/update/delete/status, EIPPool for public IP, logs and metrics. Two-step confirmation for all destructive operations (delete namespace cascades all resources under it). Use this skill when the user wants to operate CCI serverless containers via command line. Triggers: CCI, 云容器实例, serverless container, 容器实例, namespace, deployment, statefulset, pod, EIPPool, CCI负载, 无服务器容器, 创建容器实例, 删除容器实例, 容器状态, 容器日志
Reads a user's Link financial data — transactions, balances, and wallet sources — so agents can answer questions about spending and available source capabilities. Use when the user says "check my balance", "how much did I spend", "show my transactions", "what accounts are connected", "summarize my spending", "recent purchases", or asks about their financial activity, account balances, or linked sources.
Compress large language models using knowledge distillation from teacher to student models. Use when deploying smaller models with retained performance, transferring GPT-4 capabilities to open-source models, or reducing inference costs. Covers temperature scaling, soft targets, reverse KLD, logit distillation, and MiniLLM training strategies.
Provides guidance for interpreting and manipulating neural network internals using nnsight with optional NDIF remote execution. Use when needing to run interpretability experiments on massive models (70B+) without local GPU resources, or when working with any PyTorch architecture.