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Found 1,867 Skills
Shared Base for Aike Smart Parking Open Platform (openydt) CLI: First-time use of openydt, profile configuration, signature (v2/v3) and multi-environment (test/dev/prod) setup, response envelope and status code/business code handling, exit codes, rate limiting and retry, write operation security rules. Triggered when users first use openydt, configure/switch profiles, handle signature or environment issues, interpret status/resultCode, encounter rate limiting or authentication failures, or before performing any parking lot write operations. All openydt domain skills should read this base first.
Owns the smoke test contract for an ML experiment: a small, diagnostic-by-construction pytest that fits the experiment's learner on a portion of the real `data/` source and predicts on a *disjoint* portion that deliberately carries **no pre-history buffer**. The assertion is structural — the number of predictions must equal the number of rows in the predict grid. A pipeline that loads-then-features-then-splits will silently drop the cold-start rows of the predict slice and the test will fail with a row-count mismatch; a pipeline that marks X early and references upstream history nodes from feature steps will pass trivially. The smoke test is the executable proof of the X-marker placement rule from `build-ml-pipeline`. TRIGGER when: `test-ml-pipeline` has dispatched here to write the smoke test for an approved experiment; `pytest tests/smoke/` is failing on row count; the user asks "why is the smoke test failing?"; a pipeline edit in `build-ml-pipeline` needs an executable proof; an experiment script changes the pipeline shape and the matching smoke test needs revisiting. SKIP when: the design note does not exist or is not yet approved (route to `iterate-ml-experiment`); the user is asking about a regression test or schema invariant (route to `regression-test-ml-pipeline` / `distribution-test-ml-pipeline` once those exist); the question is the *interpretation* of CV metrics, not predict-time correctness (route to `evaluate-ml-pipeline`). HOW TO USE: read the matching experiment's `journal/NN_*.md` and `experiments/NN_*.py` first to understand the pipeline's source binding (what env-dict keys does `build_learner` expect?). Then construct two env-dicts from the **real `data/` source** — a train env and a predict env — such that the predict env carries *only the rows we want predictions for* and *no pre-history buffer*. The hard assertion is that the prediction count matches the predict-env row count exactly. The soft assertion is that the smoke set's MAE is within `3 × CV_mean` (or the task-appropriate analogue). **Do not write the design note or run CV — that's other skills' job.**
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
Expert Kubernetes architect specializing in cloud-native infrastructure, advanced GitOps workflows (ArgoCD/Flux), and enterprise container orchestration. Masters EKS/AKS/GKE, service mesh (Istio/Linkerd), progressive delivery, multi-tenancy, and platform engineering. Handles security, observability, cost optimization, and developer experience. Use PROACTIVELY for K8s architecture, GitOps implementation, or cloud-native platform design.
Tactical negotiation framework based on Chris Voss's "Never Split the Difference." Use when preparing for negotiations, during live negotiation scenarios, analyzing counterpart behavior, crafting responses to difficult conversations, handling objections, salary/contract negotiations, or when asked about negotiation techniques like mirroring, labeling, calibrated questions, or the Ackerman method.
Generate code using nx generators. USE WHEN scaffolding code or transforming existing code - for example creating libraries or applications, or anything else that is boilerplate code or automates repetitive tasks. ALWAYS use this first when generating code with Nx instead of calling MCP tools or running nx generate immediately.
Expert legal research agent for finding and scraping expungement data state by state. Knows authoritative sources, URL patterns, Firecrawl configuration, and 2026 legal landscape. Activate on "find expungement data", "scrape state laws", "legal research", "court URLs", "statute sources", "Clean Slate laws", "automatic expungement research". NOT for interpreting laws (use national-expungement-expert), building UI, or legal advice.
Target reconnaissance and enumeration for CTF challenges. Use when you need to scan ports, discover services, enumerate web directories, or fingerprint technology stacks.
Analyze China's macroeconomic data, PBOC monetary policy, fiscal policy, and their impact on A-share and Hong Kong stock markets. Track GDP, CPI, PPI, PMI, social financing, and interpret policy signals. Apply this when users inquire about China's macroeconomy, PBOC policies, the impact of economic data on markets, or need to understand policy implications for investment.
Use to set cadences, decision logs, and escalation paths for enterprise pursuits.
Ziwei Dou Shu Fortune-telling Master, proficient in chart plotting, chart interpretation, annual fortune analysis and life planning.
Central authority for Claude Code configuration and settings. Covers settings.json files (user, project, enterprise), available settings, permission settings, sandbox settings, settings precedence, plugin configuration, environment variables, and tools available to Claude. Assists with configuring Claude Code behavior, managing permissions, setting up enterprise policies, and troubleshooting configuration issues. Delegates 100% to docs-management skill for official documentation.