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Found 44 Skills
Provides NodeReal MegaNode blockchain infrastructure APIs for 25+ chains including BSC, Ethereum, opBNB, Optimism, Polygon, Arbitrum, and Klaytn. Covers standard JSON-RPC endpoints, Enhanced APIs (nr_ methods for ERC-20 token balances, NFT holdings, asset transfers), MegaFuel gasless transactions via BEP-322 paymaster, Direct Route MEV protection, Debug/Trace APIs, WebSocket subscriptions, ETH Beacon Chain consensus layer, Portal API usage monitoring, API Marketplace (NFTScan, Contracts API, SPACE ID, Greenfield, BNB Staking, PancakeSwap, zkSync), non-EVM chains (Aptos, NEAR, Avalanche), and JWT authentication. Use when building blockchain dApps with NodeReal, querying token or NFT data, setting up RPC infrastructure, configuring gasless transactions, protecting against MEV, tracing transactions, verifying smart contracts, resolving .bnb domains, or monitoring validators and API usage.
Resolve implementation ambiguities before planning begins. Two modes: Discussion mode surfaces gray areas with concrete options for greenfield work. Assumptions mode reads the codebase, forms evidence-based opinions, and asks the user to correct only what's wrong (brownfield work). Use for "discuss ambiguities", "resolve gray areas", "clarify before planning", "assumptions mode", "what are the gray areas", "before we plan". Do NOT use for broad design exploration (use feature-design) or for planning itself (use feature-plan).
Read and summarize an existing codebase before any design or implementation work begins. Use as a prerequisite when the project is not greenfield.
Single entry point for one-shot, end-to-end DatoCMS project setup orchestration — the only skill that bundles prerequisites, chains related recipes, and takes a greenfield or partially configured project to a working state in one pass. Covers five setup lanes: (1) frontend foundation (bootstrap a new Next.js/Nuxt/SvelteKit/Astro integration from scratch); (2) frontend features (draft mode, visual editing, web previews, content link, real-time updates, responsive images, SEO, robots/sitemaps, site search, revalidation/cache tags — applied together with their prerequisites); (3) migrations (CLI profiles, baseline migrations, shared histories, release workflow, sandbox reset loops, diff-based generation); (4) onboarding imports (WordPress, Contentful — content plus assets); (5) platform automation (CMA scripting patterns and project-level automation). Use when the user wants a named outcome scaffolded in full rather than a single file patched, when multiple related features need to land together (e.g. "set up visual editing" implies draft mode + content link + web previews), or when the request is a broad "set up X" that needs routing to the smallest matching recipe bundle.
Guided Shape Up workflow for taking projects from idea to working software. Orchestrates the /shaping and /breadboarding skills through a structured process: Frame, Shape, Breadboard, Slice, Build. Works for both greenfield (0-1) and existing projects. Use when: starting a new project or feature, planning a significant change to an existing codebase, user says "shape this", "let's shape", "shape up", or wants to go from idea to implementation with structured problem/solution separation. Proactively guides each phase and suggests next steps.
Systematic GitHub Actions workflow authoring skill for AI coding agents. Analyzes repositories to determine project type, language ecosystem, and deployment targets, then generates production-grade CI/CD workflows with proper security hardening, caching, and optimization. Handles greenfield projects (no workflows exist), brownfield updates (modify, optimize, secure existing workflows), and workflow audits with workflow-specific guidance for each. Use when the user requests GitHub Actions workflows: CI pipelines, CD deployments, release automation, scheduled jobs, or any .github/workflows YAML authoring. Also use when existing workflows need auditing, optimizing, securing, or restructuring. Triggers on phrases like "set up CI", "add CI/CD", "GitHub Actions workflow", "release automation", "deploy on tag", "publish to npm/PyPI", "schedule a job", "cron workflow", "matrix build", "workflow.yml", "actions/checkout", "permissions", "harden this pipeline", "pin actions to SHA", "OIDC", "least privilege", "supply-chain", "audit my workflows", "speed up CI", or "cache dependencies". Triggers when creating or editing files under `.github/workflows/`, `action.yml`/`action.yaml` (composite or Docker actions), or `.github/dependabot.yml`. Triggers when the user mentions migrating from GitLab CI, CircleCI, Travis, Jenkins, Drone, or Buildkite to GitHub Actions. Do NOT use for non-GitHub CI systems (GitLab CI, CircleCI, Jenkins) unless the user is migrating TO GitHub Actions. Do NOT use for general bash scripting, Makefiles, or local-only build configuration.
Initialize or migrate a repo into the ai-memory pattern: the .ai-memory.toml routing marker (workspace/project), the recall/write routing snippet in CLAUDE.md/AGENTS.md, and the ai-memory MCP server entry. Includes the qmd→ai-memory migration for repos still on the old wiki/qmd stack. Use when the user asks to set up ai-memory in a project (greenfield or brownfield), wire the MCP, enable auto-capture, or migrate off qmd.
Guides Site Reliability Engineering—SLI/SLO and error budgets, reliability dashboards and burn-rate alerting, production readiness reviews, capacity planning for availability, toil reduction, dependency and failure-mode analysis, release reliability (canaries, rollback criteria), and service-owner incident mitigation tied to customer impact. Use when defining or operating SLOs, measuring error budget burn, improving service reliability, running PRRs before launch, planning scalable resilient capacity, or leading technical mitigation during outages—not for CI/CD pipeline implementation (devops), incident program and paging policy design (incident-management-engineer), cloud access and patch tickets (cloud-system-administrator), load-test profiling (performance-engineer), rollout cutover strategy (deployment-strategist), or greenfield cloud build-out (cloud-engineer).
Your pathfinder for navigating unknown codebases. Investigates with precision, implements surgically, and never assumes — if it doesn't know, it says so. Maintains a .notebook/ knowledge base that grows across sessions, turning every discovery into lasting intelligence. Summons available skills, MCPs, and docs when the mission demands. Use when fixing bugs, implementing features, refactoring, investigating flows, or any development task in unfamiliar territory. Triggers on "fix this", "implement this", "how does this work", "investigate this flow", "help me with this code". Do NOT use for greenfield scaffolding, CI/CD, or infrastructure provisioning.
Disciplined spec-driven test-driven development workflow for building software with AI coding agents. Transforms ambiguous requests into verified implementations through structured specification, test derivation, and strict TDD. Handles greenfield projects, brownfield enhancements (with or without existing tests), refactors, and complex bug fixes with workflow-specific guidance for each. Use when the user requests a new feature, module, enhancement, refactor, API, data pipeline, CLI tool, or system with multiple requirements, edge cases, or unclear specifications. Also use for complex bug fixes requiring root cause analysis. Triggers on phrases like "add a feature", "implement", "build a new module", "build an API", "build a CLI", "build a data pipeline", "refactor", "fix this bug", "write tests for", "TDD", "test-first", "the requirements are unclear", "characterization tests", or "spec this out". Triggers when modifying code with adjacent test files (`tests/`, `*_test.py`, `*.test.ts`, `*.spec.ts`, `spec/`, `__tests__/`) or test framework config (pytest.ini, jest.config.*, go.mod with testing imports, Cargo.toml with [dev-dependencies], package.json with a test script). Triggers when the user mentions edge cases, invariants, acceptance criteria, EARS notation, or red-green-refactor. Do NOT use for simple one-line fixes, cosmetic changes, formatting, renames, dependency bumps, or tasks where requirements are already fully specified with tests provided.
Technology-agnostic guidance for modular systems: bounded contexts, clear boundaries, composability, state isolation, explicit contracts, failure containment, scaffolding workflows, split/merge criteria, sub-units inside a context, and compliance review signals. Use when designing or reviewing module structure, service boundaries, package layout, cross-cutting dependencies, "how should we split this?", modularity assessments, coupling between domains, greenfield context design, or architecture discussions without assuming a specific framework, language, or repository layout. Do NOT use for executing the full Patterns 1–5 repo decomposition pipeline or per-pattern inventories (use modular-decomposition), phased extraction roadmaps as the main deliverable (use decomposition-planning-roadmap), or end-to-end legacy migration strategy (use legacy-migration-planner).
Run /audit on a greenfield project, an existing codebase with missing docs, or one area (/audit src/auth) to bootstrap the project's AI context, the AGENTS.md files every later skill reads. Writes tool agnostic AGENTS.md plus thin CLAUDE.md pointers, adding only what is missing; never overwrites curated content.