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Found 2,056 Skills
Library-agnostic Flutter/Dart code review checklist covering widget best practices, state management patterns (BLoC, Riverpod, Provider, GetX, MobX, Signals), Dart idioms, performance, accessibility, security, and clean architecture.
Run Lighthouse CLI audits for websites and web applications from environment setup through result interpretation. Use when the user wants to audit performance, accessibility, SEO, best practices, PWA readiness, Core Web Vitals, Lighthouse CI, batch URL scans, localhost pages, or production pages. Trigger this skill for Lighthouse setup and troubleshooting in Linux or WSL, browser launcher failures such as "Cannot find Chrome" or "ECONNREFUSED 127.0.0.1", Chrome or Chromium detection issues, PageSpeed-style analysis requests, or any request to generate Lighthouse HTML and JSON reports with actionable recommendations.
When the user wants to monitor, triage, or reduce their app's crash rate — including setting up Crashlytics, prioritizing which crashes to fix first, interpreting crash data, and understanding how crashes affect App Store ranking. Use when the user mentions "crash", "crashlytics", "crash rate", "ANR", "app not responding", "crash-free sessions", "crash-free users", "symbolication", "stability", "firebase crashes", "app crashing", or "crash report". For overall analytics setup, see app-analytics.
Given a domain, identify the few independent forces that truly underpin it. Reduce dozens of phenomena to the minimal set of generators—only when you can regenerate all phenomena from these generators does it count. Use this when the user says 'rank reduction', 'find rank', 'what is rank', 'what supports this domain', 'what lies behind it', or wants to decompose any domain into its irreducible generators.
Statistical rule discovery through measurement of Go codebases: Count patterns, derive confidence-scored rules, produce Style Vector fingerprint. Use when analyzing codebase conventions, extracting implicit coding rules, profiling a repo before onboarding or PR automation. Use for "analyze codebase", "find coding patterns", "what conventions does this repo use", "extract rules", or "codebase DNA". Do NOT use for code review, bug fixes, refactoring, or performance optimization.
Deterministic 3-phase GitHub PR review comment extraction: Authenticate, Mine, Validate. Use when mining tribal knowledge from PR reviews, extracting coding standards from review history, or building datasets for the Code Archaeologist agent. Use for "mine PRs", "extract review comments", "tribal knowledge", or "PR review history". Do NOT use for analyzing patterns, generating rules, or interpreting comments — that is the Code Archaeologist agent's responsibility.
Implement a complete double-entry accounting system inside any SaaS app. Users enter transactions naturally (sales, expenses, inventory) while the system auto-posts journal entries under the hood. Produces both user-friendly reports and technical financial statements (Trial Balance, Balance Sheet, Income Statement, Cash Flow). Enforces 10000% accuracy with balanced entries and seamless void/reversal mechanics. Use when building any financial, ERP, POS, or inventory system that needs proper accounting.
Solana vault management via GLAM Protocol. Triggers: glam, glam-cli, glam-sdk, vault create/manage, tokenized vault, share class, DeFi vault, treasury, asset management, access control, delegate permissions, Jupiter swap, Drift perpetuals/spot/vaults, Kamino lending/borrow/vaults/farms, staking (Marinade/native/SPL/Sanctum/LST), cross-chain USDC (CCTP), timelock, subscription/redemption, NAV pricing, token transfer. Supports CLI and TypeScript SDK.
Implements Syncfusion Windows Forms GridGroupingControl for advanced data management with grouping, sorting, filtering, and hierarchical display. Use this when working with multi-level grouping, master-detail grids, nested table relationships, or data summaries with aggregates. The skill covers group-by operations, Excel-like filtering, dynamic record filters, hierarchical data structures, and enterprise-level grid capabilities.
Interpreted crypto wallet data for AI agents. Use when an agent needs portfolio values, token positions, DeFi positions, NFT holdings, transaction history, PnL data, token prices, charts, gas prices, swap quotes, or DApp information across 41+ chains. Zerion transforms raw blockchain data into agent-ready JSON with USD values, protocol labels, and enriched metadata. Supports x402 pay-per-request ($0.01 USDC on Base) and API key access. Triggers on mentions of portfolio, wallet analysis, positions, transactions, PnL, profit/loss, DeFi, token balances, NFTs, swap quotes, gas prices, or Zerion.
Guide for interpreting ResolveProjectReferences time in MSBuild performance summaries. Only activate in MSBuild/.NET build context. Activate when ResolveProjectReferences appears as the most expensive target and developers are trying to optimize it directly. Explains that the reported time includes wait time for dependent project builds and is misleading. Guides users to focus on task self-time instead. Do not activate for general build performance -- use build-perf-diagnostics instead.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.