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Found 296 Skills
Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
Estimate intrinsic value of stocks and companies using DCF, dividend discount models, comparable multiples, and residual income. Use when the user asks about discounted cash flow, DCF models, WACC, terminal value, dividend discount models, comparable multiples, or sum-of-the-parts valuation. Also trigger when users mention 'what is this stock worth', 'fair value estimate', 'Gordon growth model', 'free cash flow valuation', 'cost of equity', 'sensitivity analysis', 'exit multiple', or ask whether a stock is overvalued or undervalued.
Design predictive maintenance strategies using sensor data, ML models for remaining useful life (RUL), and the P-F curve framework. Use this skill when the user needs to reduce unplanned downtime, transition from reactive to predictive maintenance, evaluate sensor/IoT investments, or estimate equipment failure probability — even if they say 'machines keep breaking down', 'when will this equipment fail', 'should we invest in IoT sensors', or 'reduce unplanned downtime'.
Apply auction theory to compare the four canonical auction formats and assess revenue equivalence. Use this skill when the user needs to choose an auction format, evaluate bidding strategies, or determine when revenue equivalence breaks down due to risk aversion, asymmetry, or correlated values.
Draft a structured decision memo for Ane. Use when the user asks for a "decision memo", "decision doc", "options paper", "recommendation brief", or equivalent. Produces a scannable document with context, options, recommendation, risks, and reversibility. Applies Ane's CLAUDE.md writing style automatically.
This skill should be used when the user wants to review code, audit a diff, get a second opinion on changes, or run an adversarial review of files in the current working tree. Common triggers include "review this code", "audit this diff", "find issues in", "second opinion on this", "harsh review of", "adversarial review", and "security review of". Picks one or more reviewer personas (adversarial, security, architecture, performance). Reviews local files, `git diff`, or `git diff --staged` only — does not fetch external content. Runs in one of four modes: single-agent (one persona in the current agent), cross-model handoff (independent second opinion via another local AI CLI, with secret-shield preflight + prompt-shield wrap), multi-bg-agent (one persona per parallel background subagent), or agent-team (Claude Code Teams or equivalent on supporting agents). Skip when the user wants formatting fixes (use a linter) or refactoring patterns (use ts-best-practices or ts-best-practices-functional).
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.
Analyze equity securities, factor models, and equity portfolio construction. Use when the user asks about stocks, equity valuation ratios, index construction methods, or style analysis. Also trigger when users mention 'P/E ratio', 'growth vs value', 'market cap weighting', 'sector allocation', 'GICS classification', 'earnings per share', 'Fama-French factors', 'CAPM', 'dividend yield', 'PEG ratio', 'EV/EBITDA', or ask which factors explain equity returns.
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.**
Crear un proyecto nuevo fusionando una plantilla del equipo según el stack tecnológico (Angular, React, Symfony, etc.). Usar siempre que el usuario pida crear un proyecto nuevo, inicializar desde plantilla, o use `/project-create`. Si el stack no está claro, preguntar antes de cualquier acción.
Review the accessibility, version, identifiers, restrictions, and statements of research data, code, and materials, and do not equate public availability with reusability. Use when the user asks for "check data availability", "write data availability statement", "check whether data, code, and materials are reusable", or requests the rw-research-data workflow. Runs without a private local workspace or preset research-lab; use user-provided material and bundled public-source methods.
EXPERIMENTAL, UNTESTED recipe for posting messages to a Slack workspace from a Caffeine canister via the `slack-client` mops package (Slack Web API). Use it when the user wants their app to send a message to a Slack channel — "post to Slack", "notify a channel", "send a Slack message", or equivalent. The client is a pre-release 0.0.3 drop (bot `xoxb-` or user `xoxp-` token): its request path is verified against the live Slack API (a real message posts), but the success-response decode is not yet runtime-confirmed, so treat it as a starting point and do NOT present Slack as a fully supported platform feature yet. Hand-rolling `ic.http_request` calls to `slack.com/api` is still the wrong move — prefer the generated client so bearer auth, percent-encoding, and JSON parsing come for free.