Product & Planning

stacksjs Skill

Use when implementing real-time features in Stacks - WebSocket broadcasting, public/private/presence channels, emit to users, the Channel class, broadcast discovery, server lifecycle, or realtime configuration. Covers @stacksjs/realtime and config/realtime.ts.

stacksjs Skill

Use when working with the Stacks extension registry - framework extension metadata, package discovery, or the registry system. Covers @stacksjs/registry.

halojerry Bundle

E-commerce growth strategy advisor. Diagnoses current business health using unit economics (CAC, LTV, AOV, contribution margin), identifies the highest-impact growth opportunities across 5 levers (traffic, conversion, AOV, retention, expansion), and builds a prioritized 90-day growth roadmap. Uses the Ansoff Matrix adapted for e-commerce to evaluate market penetration, channel expansion, product expansion, and new market entry. Includes multichannel readiness assessment (Amazon, Walmart, TikTok Shop, Etsy, DTC/Shopify/Shopify) and product line expansion analysis. No API key required. Use when: (1) planning next phase of e-commerce growth, (2) deciding whether to expand to new channels or products, (3) diagnosing why growth has stalled, (4) prioritizing what to fix or build next.

lebsral Bundle

Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone says I want to build an AI that..., how do I make my AI..., or describes any AI/LLM task without naming a specific skill, I need AI but do not know where to start, which AI pattern should I use, what is the best way to add AI to my app, recommend an AI approach, AI feature discovery, too many AI options, overwhelmed by AI frameworks, just tell me what to build, new to DSPy, beginner AI project help, which LLM pattern fits my use case, confused about AI architecture, help me figure out my AI approach.

lebsral Bundle

Use when you need to connect DSPy agents to external MCP tool servers — databases, file systems, APIs, or any MCP-compatible service. Common scenarios - wiring MCP tools into a ReAct or CodeAct agent, discovering tools from an MCP server at runtime, converting MCP tools to DSPy tools, or building agents that use tools hosted on remote servers. Related - dspy-tools, dspy-react, dspy-codeact, ai-taking-actions. Also used for dspy.Tool.from_mcp_tool, MCP with DSPy, connect DSPy to MCP server, use MCP tools in DSPy agent, model context protocol DSPy, DSPy agent with external tools via MCP, MCP tool integration, StdioServerParameters, ClientSession, stdio_client, mcp tool discovery, async MCP connection, acall with MCP tools.

lebsral Bundle

Plan a multi-phase AI feature before building it. Use when you have a PRD or project idea and need to figure out the execution order, which skills to use in what sequence, or how to break an ambitious AI project into phases. Also used for scoping an AI feature, creating a phased rollout plan, figuring out dependencies between AI components, help me figure out how to execute this, plan my AI feature, what order should I build this in, AI project roadmap, break this into phases, phased AI rollout, AI feature planning, multi-phase AI project, AI project dependencies, which skills do I need, AI execution plan

lebsral Bundle

Make AI solve hard problems that need planning and multi-step thinking. Use when your AI fails on complex questions, needs to break down problems, requires multi-step logic, needs to plan before acting, gives wrong answers on math or analysis tasks, or when a simple prompt is not enough for the reasoning required. Covers ChainOfThought, ProgramOfThought, MultiChainComparison, and Self-Discovery reasoning patterns in DSPy., AI gives shallow answers, LLM does not think before answering, chain of thought prompting, make AI show its work, AI fails at math, complex analysis with LLM, multi-step problem solving, AI reasoning errors, LLM logic mistakes, think step by step DSPy, AI cannot do basic arithmetic, deep reasoning with language models, self-consistency for better answers, tree of thought.

lebsral Bundle

Build AI that recommends products, articles, or content based on user preferences. Use when building product recommendations, you-might-also-like features, personalizing feeds, content discovery, ranking by user preference, suggesting related items, building a recommendation engine, collaborative filtering with LLMs, re-ranking search results by relevance, curating personalized playlists, next-best-action suggestions, upsell recommendations, similar item matching, AI-powered content curation.

lebsral Bundle

Find every way users can break your AI before they do. Use when you need to red-team your AI, test for jailbreaks, find prompt injection vulnerabilities, run adversarial testing, do a safety audit before launch, prove your AI is safe for compliance, stress-test guardrails, or verify your AI holds up against adversarial users. Also used for automated attack generation with DSPy, MIPROv2-optimized adversarial testing, red team before launch, AI vulnerability discovery, adversarial testing for LLM, prompt injection attacks, jailbreak testing, AI safety compliance, SOC2 AI audit, OWASP LLM top 10, penetration testing for AI, stress test AI guardrails, can users break my AI, AI safety for regulated industries, test AI before shipping, adversarial prompt dataset.

tody-agent Skill

Customer discovery framework using Jobs-To-Be-Done theory — uncover the functional, social, and emotional jobs customers hire products to do. Produces JTBD canvases with job statements, outcome metrics, and competing solutions. Use alongside cm-brainstorm-idea for evidence-based product decisions.

tody-agent Skill

[Deprecated] discovery merged. Use `cm-skill-index` instead.

tody-agent Skill

CodyMaster ecosystem roadmap, marketplace, and distro validation reference.

tody-agent Skill

[Deprecated] discovery and meta-skill merged. Use `cm-skill-index` instead.

flpbalada Bundle

Convert a discussion into a concise PRD covering users, the problem, requirements, technical implementation, and constraints. Use when the user asks to turn a discussion into a PRD.

flpbalada Bundle

Prioritize product features with the Kano model of customer satisfaction. Use when sorting feature ideas, roadmap items, requirements, or service attributes into must-haves, performance drivers, delighters, low-value work, and reverse effects; use to decide what to protect, improve, add, defer, remove, or validate.

flpbalada Bundle

Organize information into Projects, Areas, Resources, and Archives. Use when setting up notes, files, knowledge bases, personal systems, or migrations from messy folders; avoid for product backlog hierarchy, workflow Kanban, or strategic portfolio planning.

flpbalada Bundle

Build shared understanding before action by interviewing the user about ambiguous implementation, product, planning, or design tasks. Use when goal, scope, constraints, risks, edge cases, success criteria, done state, or next action are unclear; when user wants a thinking partner; or when user needs a handoff-ready task prompt. Do not write PRDs; use to-prd for product requirements.

flpbalada Bundle

Shape long-running autonomous work into one durable goal with stop condition, validation loop, checkpoints, progress format, and pause rules. Use for scoped, verifiable progress across migrations, refactors, prototypes, evals, retry loops, or prompt optimization; avoid loose backlog lists, one-shot answers, hidden preferences or unavailable context, and autonomous work that risks production without human approval.

flpbalada Skill

Deliver a defined goal through orchestrated implementation and independent QA. Use when work should be delegated, tested against Given/When/Then requirements, and repaired until it passes.

flpbalada Skill

Create ethical information gaps that earn attention and resolve with real payoff. Use when writing headlines, notifications, teasers, onboarding reveals, or feature-discovery prompts; avoid fake urgency, vague clickbait, or habit-loop work better handled by Hooked or self-initiated triggers.

flpbalada Bundle

Break a PRD into linear, verifiable implementation milestones grounded in the codebase. Use when planning implementation checkpoints from product requirements.

flpbalada Skill

Model complex systems as nodes, edges, flows, and dependencies. Use when mapping stakeholder networks, causal loops, product ecosystems, architecture dependencies, knowledge graphs, or non-linear strategy problems; avoid when a simple hierarchy, roadmap, or checklist is enough.

flpbalada Bundle

Break a user-provided PRD into ordered, independently verifiable implementation tickets returned directly in the response.

flpbalada Skill

Reframe product strategy around customer progress with Jobs to Be Done. Use when researching motivations, switching behavior, competitive alternatives, unmet needs, feature framing, or value propositions; avoid for backlog story syntax, delivery hierarchy, or business-model economics.

flpbalada Skill

Structure product work from strategic themes to epics, stories, and tasks. Use when organizing backlogs, roadmaps, releases, stakeholder communication, or large initiatives into outcome-linked delivery units; avoid for writing one story only, requirements analysis, or effort estimates.

flpbalada Skill

Drive PRD implementation through a durable /goal, scoped parallel implementation agents, independent QA ownership, acceptance evidence, and persona-based test instructions. Use when a PRD must be delivered end-to-end and every acceptance criterion must be verified before marking the goal complete.

flpbalada Bundle

Estimate line additions and deletions from a PRD or spec by reading the requirements and inspecting the target repo. Use when the user asks for changed LOC, additions, deletions, diff size, PR size, or implementation size before building.

flpbalada Skill

Simplify prompts, system instructions, workflows, rules, and agent playbooks while preserving behavior. Use when logic has too many branches, nested conditions, contradictions, repeated rules, vague failure paths, or inconsistent outputs. Do not rewrite product requirements; use to-prd for PRD conversion.

flpbalada Bundle

Create a letter-from-future planning artifact for startup, product, roadmap, pivot, project kickoff, quarterly planning, or stakeholder alignment. Use when user needs to describe a desired future, backcast milestones, connect vision to customer value, and surface risks, metrics, and next actions. Do not use for formal PRDs; use to-prd.

flpbalada Skill

Map value creation, delivery, and capture with the Business Model Canvas. Use when evaluating startups, pivots, product strategy, revenue streams, cost structure, partners, channels, or business-model coherence; avoid for deep customer-job research, roadmap breakdowns, or macro-environment scans.

flpbalada Skill

Write clear user stories with user, capability, benefit, and testable acceptance criteria. Use when creating or improving backlog items, Given/When/Then criteria, INVEST checks, and user-centered requirements; avoid for roadmap hierarchy, broad requirement audits, or effort sizing.

flpbalada Skill

Make structured product decisions with explicit goals, options, tradeoffs, evidence, and decision records. Use when stakeholders disagree, multiple valid paths exist, scope is contested, or choices need rationale; avoid for requirements cleanup, roadmap hierarchy, or effort sizing alone.

flpbalada Skill

Verify completed work against a PRD's Given/When/Then requirements.

flpbalada Bundle

Identify product-management anti-patterns and replace them with better practices. Use when reviewing PM behavior, stakeholder chaos, vague requirements, output-focused roadmaps, weak discovery, team dysfunction, or PM onboarding; avoid personal critique without concrete behavior and impact.

jayden-dang Bundle

Use when asking why code or a design exists this way — design rationale, rejected alternatives, regression history, postmortems, or where a threshold came from — produces a cited confidence-graded note. Not for how the runtime flows (tour-system / scan) or library API facts (research).

jayden-dang Bundle

Chart or advance a multi-session decision map until the route to a destination is clear.