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Found 1,000 Skills
USDC is Circle's stablecoin deployed across multiple blockchain ecosystems including EVM chains (Ethereum, Base, Arbitrum, Polygon, Arc) and Solana. Use this skill to check balances, send transfers, approve spending, and verify transactions. Triggers on: USDC balance, send USDC, transfer USDC, approve USDC, USDC allowance, verify USDC transfer, USDC contract address, USDC on Solana, Solana USDC, check balance, SPL token, Associated Token Account, ATA, ERC-20 USDC, parseUnits, formatUnits, 6 decimals, viem, @solana/kit.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use this skill when forecasting ANY univariate time series — sales, sensor readings, stock prices, energy demand, patient vitals, weather, or scientific measurements — without training a custom model. Supports both basic forecasting and advanced covariate forecasting (XReg) with dynamic and static exogenous variables. Automatically checks system RAM/GPU before loading the model, validates dataset fit before processing, supports CSV/DataFrame/array inputs, and returns point forecasts with calibrated prediction intervals. Includes a preflight system checker script that MUST be run before first use to verify the machine can load the model and handle your specific dataset.
Use this skill when you need to verify cross-file data/asset consistency in a content-driven site — not "does it render?" (that's `web-visual-verification`) but "do the data, files, and references all line up?". Triggers on phrases like "盤點內容", "稽核資產", "對照 course-data 跟 markdown", "找缺圖", "task ID 有沒有重複", "quiz 編號 vs 硬編碼總數", "audit", "content audit", "content drift", "asset coverage", "three-way sync check", "cross-file consistency", "find missing illustrations", "資料一致性檢查", "找出該補插圖的地方", or any moment when the user senses divergence between source files and deployed data. Output is a human-readable markdown report, not a pass/fail. Pair with `web-visual-verification` for full pre-release coverage.
Run /check before merge to confirm a change is sound. Two modes: `/check verify` drives the real app and proves behavior against the spec (every acceptance criterion met, every specced surface built); `/check review` runs a senior code review on a different model than wrote the code. Verify after /develop, review before a PR. Writes findings to docs/reviews/; never edits your code.
Verifies that git commits address security audit findings without introducing bugs. This skill should be used when the user asks to "verify these commits fix the audit findings", "check if TOB-XXX was addressed", "review the fix branch", "validate remediation commits", "did these changes address the security report", "post-audit remediation review", "compare fix commits to audit report", or when reviewing commits against security audit reports.
Verifies factual claims in documents using web search and official sources, then proposes corrections with user confirmation. Use when the user asks to fact-check, verify information, validate claims, check accuracy, or update outdated information in documents. Supports AI model specs, technical documentation, statistics, and general factual statements.
Create a structured format for documenting feature requirements as user stories. JSON files with testable acceptance criteria that AI agents can verify and track.
Receive and verify SendGrid webhooks. Use when setting up SendGrid webhook handlers, debugging signature verification, or handling email delivery events.
Find, install, and configure MCP servers. Use proactively for MCP discovery, OAuth setup, env vars, stdio vs SSE transport, or troubleshooting MCP connections. Examples: - user: "Add the filesystem MCP server" → read server file, add to mcpServers in opencode.json, verify transport type - user: "How do I use MCP with GitHub?" → check catalog, install @modelcontextprotocol/server-github, configure OAuth token - user: "MCP not connecting" → check transport type (stdio/SSE), verify args/command, check env vars are passed - user: "What MCPs are available?" → run list_mcps.py, show catalog with auth types and install commands
Audit tasks in docs/todo/done/ to verify claimed implementations actually exist in the codebase. Use when reviewing completed tasks, validating work before release, or periodically auditing task accuracy. Moves unverified tasks back to pending/.
List all Langfuse models with their pricing. Use when checking model costs, verifying pricing configuration, or getting an overview of model definitions.
Guides creation of effective Agent Skills with proper structure and validation. Use when users want to create a new skill, update an existing skill, or need guidance on skill design patterns, SKILL.md format, or verify.py implementation. NOT when just using existing skills (use those skills directly).