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Found 1,945 Skills
Analyzes the variety and depth of assertions across .NET test suites. Use when the user asks to evaluate assertion quality, find shallow testing, identify assertion-free tests (no assertions or only trivial ones like Assert.IsNotNull), flag self-referential or tautological assertions (output equals input on identity/round-trip operations), measure assertion coverage diversity, or audit whether tests verify different facets of correctness. Produces metrics and actionable recommendations. Works with MSTest, xUnit, NUnit, TUnit. DO NOT USE FOR: writing new tests (use writing-mstest-tests), other anti-patterns like flakiness or duplication (use test-anti-patterns), or fixing assertions.
Guides senior system and solution architecture—cross-service boundaries, integration patterns, non-functional requirements (scale, reliability, security, cost), ADRs, C4-style modeling, architecture review, build-vs-buy, and phased migration (strangler, dual-write). Use when designing multi-service systems, evaluating platform or vendor choices, writing or reviewing architecture decision records, defining standards and principles, or assessing technical risk across domains—not for single-service RFCs and module design (senior-software-engineer), data platform or mesh decisions (data-architect), cloud landing zone, Well-Architected, and migration architecture (cloud-architect), cloud/IaC implementation (infrastructure-engineer, cloud-engineer), internal developer platform product (platform-engineer), or program tracking (technical-program-manager). For business strategy and cases, use business-consultant; for applied AI (RAG, agents, copilots), use applied-ai-architect-commercial-enterprise.
Screen core candidate stocks with high capital returns, stable moats, long-term compound interest potential, and strong earnings quality, and output priorities, valuation disciplines, and key points for continuous tracking. Applicable to scenarios such as long-term core position stock selection, compound interest asset pool construction, and high-quality company comparison.
Build and organize a universe of potential acquirers for sell-side M&A processes. Identifies strategic and financial buyers, assesses fit, and prioritizes outreach. Use when preparing for a sell-side mandate, building a buyer universe, or evaluating potential partners. Triggers on "buyer list", "buyer universe", "potential acquirers", "who would buy this", "strategic buyers", or "financial sponsors".
This skill should be used when the user says phrases like "record a topic", "new topic", "how is this topic", "I have an idea", "help me note this down", or shares a rough content idea that needs evaluation and recording. Even if the user casually mentions a content idea, actively trigger the evaluation process without waiting for the user to explicitly say "record". First, use the five-dimensional logic of dbs-content to diagnose whether the topic is worth doing. Only record it if it is worth doing, and recommend 3-5 similar topics by drawing inferences. Do not trigger: Deepen the topic to generate an outline (use li-topic), directly write scripts (use li-writer), or AI polish or beautify the topic title. Use when the user wants to "record a topic", "log a topic idea", "is this topic worth doing", or shares a rough contentclaise Du.弹应用样子Pr广解除禁 \ 诊断和记录.
How to analyze prose style and produce style reference files. Use when creating, updating, or evaluating style files: the reference documents that capture a project's voice patterns for writer and critic agents.
Evaluate the source, strength, sustainability and weakening risks of a company's competitive advantages, and determine whether the moat truly exists and can be converted into returns. Suitable for scenarios such as long-term stock initial screening, high-quality company research, and competitive barrier judgment.
Produce first drafts that match a writer's authentic voice using their Voice DNA Document. Consumes DNA documents from writing-dna-discovery skill. Generates 2 meaningfully different drafts with headlines, confidence assessment, decision notes, and DNA refinement suggestions. Collaborative partner that evaluates, pushes back, and advocates for quality. Handles blog posts, essays, newsletters, and more.
Use when an agent needs to operate the user's real Chrome session — listing tabs, reading the page, clicking, filling, typing into rich editors, pressing keys, evaluating JS, capturing screenshots, and reading console/network buffers. All actions go through CDP and run on backgrounded tabs without stealing focus.
Generate a Well-Architected-aligned Architecture Decision Record (ADR) that documents a design decision with context, options evaluated, trade-offs, and WA pillar impact.
Create and manage Architecture Decision Records (ADRs) using MADR 4.0.0 format. Use this skill when evaluating technology choices, documenting design decisions, or reviewing past architectural choices.
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.**