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Found 276 Skills
Generate clickable HTML wireframes for every screen in the PRD. No design tool needed — open in browser.
File Beads epics/issues from a finalized plan/spec AND do the polish pass (clarity, acceptance criteria, sizing, deps). Use when asked to create Beads from a plan/spec (OpenSpec, PRD, design doc), convert an external plan into Beads structure, or review/refine an existing Beads set.
Autonomous PRD implementation loop — turns GitHub issues into shipped code using TDD, code review gates, and Docker sandbox isolation. The execution engine for the grill-me → write-a-prd → prd-to-issues → ralph pipeline.
Use when starting a session, deciding which framework skill applies to the current task, or sequencing them across a feature. Maps the user's intent to one of the five framework skills (ai-driven-prd, init-claude-project, generate-dev-plan, declarative-design, execute-plan) and enforces the cross-skill operating behaviors. Triggers on "which skill should I use", "where do I start", "how do these skills fit together", "I have a PRD now what", "/using-agent-skills".
Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.
Divide-and-conquer implementation from specs/plans. Decomposes a reference document into independent tasks, assigns each to a builder agent, executes in parallel waves respecting dependencies, then integrates results. Use when you have a spec, PRD, plan, or large feature to implement quickly with parallel execution.
Use when asked to "working backwards", "PR/FAQ", "Amazon PR/FAQ", "write a press release", "define a new product", or "write a customer-focused PRD". Helps define products by starting with the customer problem and desired outcome before building. The Working Backwards process (developed at Amazon) forces clarity on customer value before committing engineering resources.
Use when converting a design document, PRD, or task list into beads issues - ensures lossless conversion with proper epic hierarchy, validated dependencies for maximum parallelization, and three independent subagent review passes before execution
Get Shit Done (GSD) orchestrator. Runs the full project pipeline from idea to implementation to documentation: setup → PRD → task list → implementation → decisions doc. Use when the user wants to build something end-to-end, says "let's GSD", "build this from scratch", "get shit done", or wants to run the full development workflow. Coordinates gsdl-setup-project, gsdl-create-prd, gsdl-create-plan, gsdl-execute-plan, and gsdl-document-decisions skills. Spawns subagents per parent task during implementation to preserve context. Accepts an optional project name or source URL: /gsdl [project-name] OR /gsdl [linear|notion|slite] [url] OR /gsdl [url].
Remove signs of AI-generated writing from text to make it sound more natural and human-written. Use when editing or reviewing any form of document including: markdown, technical docs, emails, blog posts, PRDs, or any dedicated writing content. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Authoritative reference for the neo4j-agent-memory Python package — a graph-native memory system for AI agents built on Neo4j — and for the hosted service (NAMS) at memory.neo4jlabs.com. Use this skill whenever the user mentions neo4j-agent-memory, agent memory with Neo4j, context graphs, the POLE+O model, MemoryClient/MemorySettings, the memory MCP server, or any of the framework integrations (LangChain, PydanticAI, CrewAI, AWS Strands, Google ADK, Microsoft Agent Framework, OpenAI Agents, LlamaIndex). Also use when the user mentions the hosted service at memory.neo4jlabs.com, NAMS, the Neo4j Agent Memory Service, the `nams_` API key prefix, or the hosted MCP endpoint. Also use when writing documentation, blog posts, tutorials, PRDs, or code samples for the project, when comparing agent memory approaches, or when positioning graph-native memory against vector-only approaches — even if the user doesn't explicitly name the package.
Guides product management for human data platforms—annotation and labeling products, workforce workflows, task design, quality systems (gold sets, adjudication, inter-annotator agreement), customer ML-team project delivery, contributor experience, and privacy-safe handling of human-generated training data. Use when prioritizing roadmap for labeling/RLHF/eval data platforms, writing PRDs for annotation or QA features, defining success metrics for throughput and quality, scoping enterprise customer workflows, or balancing cost-quality-speed tradeoffs—not for hands-on model training (data-scientist), warehouse/analytics pipelines (data-warehouse-engineer), generic BRD workshops without product lens (business-analyst), AI solution architecture for copilots (applied-ai-architect-commercial-enterprise), or control implementation for audits (compliance-engineer). UX flows: product-designer. Eval harnesses: prompt-engineer-agent-prompts-evals. Pricing/packaging for platform: product-management-monetization.