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Found 256 Skills
CLI for bootstrapping and managing wordspace projects
One-time project initializer for Inertia Rails skills. Detects stack and frontend framework (React/Vue/Svelte) from Gemfile and package.json, offers to install recommended deps (alba-inertia, js-routes, pagy, shadcn), and generates a CLAUDE.md section that configures which skill patterns apply. Use when first installing these skills, bootstrapping a new Inertia Rails project, or when the stack changes.
Initializes new Aptos dApp projects using degit to bootstrap from official templates. Triggers on: 'create project', 'scaffold project', 'new dApp', 'new Move project', 'initialize project', 'setup project', 'start new contract', 'init aptos project', 'create fullstack dapp'.
Writes or rewrites README.md files tailored to the project type (CLI, library, app, framework, monorepo, or skill bundle). Discovers project context, selects the right structure, writes section by section, and validates against quality checks. Use when creating a README, writing a README from scratch, rewriting a bad README, bootstrapping project documentation, or asking "write a README for this project."
Production-ready phylogenetics and sequence analysis skill for alignment processing, tree analysis, and evolutionary metrics. Computes treeness, RCV, treeness/RCV, parsimony informative sites, evolutionary rate, DVMC, tree length, alignment gap statistics, GC content, and bootstrap support using PhyKIT, Biopython, and DendroPy. Performs NJ/UPGMA/parsimony tree construction, Robinson-Foulds distance, Mann-Whitney U tests, and batch analysis across gene families. Integrates with ToolUniverse for sequence retrieval (NCBI, UniProt, Ensembl) and tree annotation. Use when processing FASTA/PHYLIP/Nexus/Newick files, computing phylogenetic metrics, comparing taxa groups, or answering questions about alignments, trees, parsimony, or molecular evolution.
BAZDMEG Method workflow checkpoint system for AI-assisted development. Enforce quality gates at three phases: pre-code, post-code, and pre-PR. Use when: (1) starting a new feature or bug fix, (2) finishing AI-generated code before review, (3) preparing a pull request, (4) running a planning interview, (5) auditing automation readiness, (6) preventing AI slop, (7) session bootstrap, (8) source rank, (9) domain gates, (10) bugbook. Triggers: 'bazdmeg', 'pre-code checklist', 'post-code checklist', 'pre-PR checklist', 'planning interview', 'quality gates', 'session bootstrap', 'source rank', 'domain gates', 'bugbook'.
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).
Bootstrap new projects with strong typing, linting, formatting, and testing. Supports Python, TypeScript, and other languages with research fallback.
Use when raising startup capital (pre-seed through Series C+): decide raise vs bootstrap, size a round, build a deck + data room, run investor targeting/outreach, negotiate SAFEs/term sheets, manage diligence, and set investor reporting cadence post-close.
Bootstrap the openspec/ directory structure for Spec-Driven Development in any project. Trigger: When user wants to initialize SDD in a project, or says "sdd init", "iniciar sdd", "openspec init".
Create and maintain Architecture Decision Records (ADRs) optimized for agentic coding workflows. Use when you need to propose, write, update, accept/reject, deprecate, or supersede an ADR; bootstrap an adr folder and index; consult existing ADRs before implementing changes; or enforce ADR conventions. This skill uses Socratic questioning to capture intent before drafting, and validates output against an agent-readiness checklist.
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.