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Found 151 Skills
Use when ANY iOS build fails, test crashes, Xcode misbehaves, or environment issue occurs before debugging code. Covers build failures, compilation errors, dependency conflicts, simulator problems, environment-first diagnostics.
macOS menu bar app that identifies USB-C cable capabilities and charging diagnostics using IOKit
Run a Bayesian A/B test on conversion data using PyMC. Use when the user wants to compare two variants (landing pages, emails, pricing, UI changes) and decide which to ship using posterior probabilities and expected loss instead of p-values. Covers Beta-Binomial model, ROPE, expected loss, sample-size guidance, and ArviZ diagnostics.
Compare React Doctor diagnostics for a GitHub pull request (PR) with Daytona. Use when asked to run parity, check a PR for diagnostic regressions, compare a PR with its base, or report added and removed diagnostics.
Guides architects on when and how to use goal-seeking agents as a design pattern. This skill helps evaluate whether autonomous agents are appropriate for a given problem, how to structure their objectives, integrate with goal_agent_generator, and reference real amplihack examples like AKS SRE automation, CI diagnostics, pre-commit workflows, and fix-agent pattern matching.
Guides the agent through the Capgo CLI command surface and routes requests to more specific Capgo skills. Use when the user asks generally about the Capgo CLI, app setup, diagnostics, OTA operations, native builds, or organization commands. Do not use when a more specific Capgo skill already clearly matches the request.
Quantitative statistics framework for time-series analysis using Longbridge price data — ADF unit root test (stationarity), cointegration (Engle-Granger / Johansen), GARCH volatility modelling (conditional heteroskedasticity), regression diagnostics (Durbin-Watson / Breusch-Pagan), bootstrap confidence intervals, hypothesis tests (t-test / F-test). Requires statsmodels and scipy. Triggers: "量化统计", "ADF检验", "单位根", "协整检验", "GARCH", "自相关", "异方差", "Bootstrap", "假设检验", "量化統計", "ADF檢驗", "單位根", "協整檢驗", "異方差", "假設檢驗", "quantitative statistics", "ADF test", "unit root", "cointegration", "GARCH", "autocorrelation", "heteroskedasticity", "bootstrap", "hypothesis test", "statsmodels".
Look up the public API of any JVM dependency (Scala 3, Scala 2, Java) from the terminal — type signatures, members, docs, and source as Markdown, no JAR unpacking needed. Use this skill whenever you need to call an unfamiliar library method, explore a package's types, or check a dependency's API. Prefer cellar over Metals MCP only for looking up external dependency APIs (`cellar get-external` vs Metals `inspect`/`get-docs`) — cellar needs no project import and queries any published Maven artifact. For everything else (references, rename, goto definition, diagnostics, compile), use Metals.
Connect to Postgres databases, run SQL and diagnostics, inspect schemas and migrations, review query performance, and use common PostGIS or pgvector patterns.
This skill should be used when the user asks to "fix my skill" or "audit this skill". Make sure to use this skill whenever the user mentions skill quality, structural issues, broken skills, or skill diagnostics — even if they don't explicitly say "repair-skill". Not for adding features or improving effectiveness — use improve-skill. Not for agents — use repair-agent.
Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies. Covers single-stock diagnostics and batch screening for both Graham cigar-butt and Buffett quality-compounder criteria. Runs cross-statement reconciliation before scoring. Data from Longbridge CLI first, MCP fallback, WebSearch only for genuine gaps. Triggers: "格雷厄姆", "巴菲特", "捡烟蒂", "烟蒂股", "NCAV", "净流动资产", "护城河", "价值投资", "安全边际", "深度价值", "撿煙蒂", "煙蒂股", "淨流動資產", "護城河", "安全邊際", "Graham", "Buffett", "cigar butt", "net-net", "NCAV screen", "moat", "value investing", "margin of safety", "deep value", "quality compounder", "價值投資", "深度價值", "防御型投资者", "防禦型投資者"
AI SDLC installation diagnostics and safe upgrade planning. Use when an AI assistant needs to inspect harness prerequisites, repository layout, module and skill registration, detect actionable installation problems, compare versioned file inventories, preview additions/modifications/removals/schema migrations, or produce backup and rollback plans without applying an upgrade. Supports `--quick-flow` and `--full-flow`.