Loading...
Loading...
Found 271 Skills
Agent onboarding automation for AIBTC first-hour setup. Use when a new or existing agent needs a structured bootstrap flow: wallet readiness, AIBTC registration check, heartbeat health checks/check-in, safe skill-pack installs, and a one-command doctor summary with next actions.
Design and generate CI/CD pipelines from detected project stack signals. Covers GitHub Actions, GitLab CI, CircleCI, and Buildkite with caching, matrix builds, deployment strategies (blue-green, canary, rolling), environment gates, and security scanning. Use when bootstrapping CI, migrating pipelines, or optimizing build times.
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts. BOOTSTRAP MODE - Triggers: "Create a data context skill", "Set up data analysis for our warehouse", "Help me create a skill for our database", "Generate a data skill for [company]" → Discovers schemas, asks key questions, generates initial skill with reference files ITERATION MODE - Triggers: "Add context about [domain]", "The skill needs more info about [topic]", "Update the data skill with [metrics/tables/terminology]", "Improve the [domain] reference" → Loads existing skill, asks targeted questions, appends/updates reference files Use when data analysts want Claude to understand their company's specific data warehouse, terminology, metrics definitions, and common query patterns.
Bootstrap new projects with strong typing, linting, formatting, and testing. Supports Python, TypeScript, and other languages with research fallback.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
Personal wiki at ~/.ultrabrain/ that accumulates knowledge across sessions using an LLM-maintained-wiki pattern. Use when the user asks factual, technical, or decision-oriented questions that may have been previously captured (check index.md before answering), or explicitly asks to capture/記下來/save session content, ingest/整合 raw entries into the wiki, lint/檢查 the vault, or bootstrap a new vault. Skip for small talk, current-file questions, or code-execution requests.
Selectively pull upstream improvements from a Laravel starter kit (laravel/vue-starter-kit, laravel/react-starter-kit, laravel/svelte-starter-kit, laravel/livewire-starter-kit) into a project bootstrapped from one. Use when the user wants to update, sync, or migrate features from their starter kit. Applies one feature at a time on a dedicated branch; never auto-merges customized files.
Test-driven development workflow — write failing tests first, implement minimum code, run full suite, commit. Use when implementing features, fixing bugs, or adding test coverage. Includes mock bootstrap phase for projects with mockReset:true.
This skill should be used when users encounter cspell unknown word warnings, spelling errors from cspell diagnostics, or CI/linting failures on unrecognized words. Also applies when users ask to add words to the cspell dictionary, suppress or ignore cspell warnings, choose between cspell:words and cspell:ignore directives, or bootstrap cspell config in a new project
Generate complete project from PRD + stack template — directory structure, configs, CLAUDE.md, git repo, and GitHub push. Use when user says "scaffold project", "create new project", "start new app", "bootstrap project", or "set up from PRD". Uses SoloGraph for patterns and Context7 for latest versions. Do NOT use for planning features (use /plan) or PRD generation (use /validate).
Evaluates and optimizes agent skills using a DSPy-powered GEPA (Generate/Evaluate/Propose/Apply) loop. Loads scenario YAML files as DSPy datasets, scores outputs with pattern-matching metrics, and optimizes prompts via BootstrapFewShot or MIPROv2 teleprompters. Also generates new scenario YAML files from skill descriptions.
Bootstrap, install, and operate an external task-management CLI as the source of truth for agent execution tracking (instead of built-in todos). Provides the abstraction layer between spec-management intent (implementation plans and tasks) and concrete CLI commands. MUST be invoked when any implementation-tier artifact (SPEC, STORY, BUG) comes up for implementation — create a tracked plan before writing code. Optional but recommended for complex SPIKEs. For coordination-tier artifacts (EPIC, VISION, JOURNEY), spec-management must decompose into implementable children first — this skill tracks the children, not the container. Also use for standalone tasks that require backend portability, persistent progress across agent runtimes, or external supervision. Use this skill whenever the user asks to track tasks, create an implementation plan, check what to work on next, see task status, manage dependencies between work items, or close/abandon tasks — even if they don't mention "execution tracking" explicitly.