Product & Planning

vfarcic Skill

Start working on a PRD implementation

yokeloop Skill

Turns the current conversation and codebase understanding into a PRD, publishes it as a GitHub issue, and saves a local copy in .yoke/ai. Does not interview — synthesizes what is already known. Activates when the user writes "prd", "create a prd", "write a prd", "turn this into a prd", "product requirements doc", "draft requirements".

yokeloop Bundle

Breaks a plan, spec, or PRD into independently-grabbable GitHub issues using vertical slices (tracer bullets), publishes them in dependency order, and saves a local index in .yoke/ai. Activates when the user writes "issues", "break into issues", "create tickets", "split into tasks", "decompose into issues", "make implementation tickets", "tracer bullets".

cristiano-pacheco Bundle

Create a PRD in Obsidian.

cristiano-pacheco Bundle

Break a PRD into tasks.

cristiano-pacheco Bundle

Create a tech spec from a PRD.

cristiano-pacheco Skill

Find the MVP slice in a PRD or engineering plan.

alessioscarfone Skill

Create or update the shared PRD — discover product vision, map user journeys, define functional requirements, and write success criteria and NFRs. Use when defining product requirements, revising the product vision, or updating the PRD. Do not use for architecture decisions, UX design, or implementing features.

alessioscarfone Bundle

Learn about the SDD process — get a quick overview, deep dives into each artifact type (PRD, architecture, UX, changes, specs), team roles, and available skill commands. Use when onboarding to SDD, looking up how a skill works, or asking about roles and process stages. Do not use for creating or editing SDD documents or implementing changes.

alessioscarfone Skill

Set up SDD on an existing project — inspects the codebase, interviews the user, and generates prd.md, architecture.md, ux.md (if a UI exists), and sdd-tracker.yml. Use when adopting SDD on legacy code or an existing codebase that has no SDD artifacts yet. Do not use for new (greenfield) projects or for updating SDD documents that already exist.

alessioscarfone Skill

Scope a new change end-to-end — writes a proposal, design doc, capability specs, and task list, then updates the shared PRD, UX, and architecture as needed. Use when scoping new product changes, creating a proposal with spec, design, and tasks in one workflow. Do not use for implementing already-proposed changes or updating a single shared document in isolation.

alessioscarfone Skill

Internal skill — auto-invoked by sdd-prd, sdd-arch, sdd-ux, sdd-propose, sdd-implement, sdd-verify, and sdd-archive to create and maintain the SDD workflow tracker file (sdd-tracker.yml). Covers file initialization, shared artifact tracking, change status transitions (proposed → in-progress → done → verified → archived), changelog entries, and project.lastUpdate housekeeping. Invoke after producing or modifying any SDD artifact. Do not invoke directly — use the relevant SDD workflow skills instead.

browseros-ai Skill

Full feature development workflow. Explores codebase, designs, writes PRD, implements, reviews, fixes, and creates PR. Use with "/dev <feature description>".

browseros-ai Skill

Generate questions, clarify unknowns, then write a PRD using pyramid principles. Sub-skill of the /dev workflow.

browseros-ai Skill

Implement a feature from its PRD. Creates a work tree if needed, writes clean code following Google-level standards, and tests iteratively. Sub-skill of the /dev workflow.

proyecto26 Bundle

This skill should be used when the user asks "how does this scale", "scale to millions", "scaling roadmap", "where is the bottleneck", "what breaks first", "10x growth", "vertical vs horizontal scaling", "scale from zero", or "what's the next scale curve". It gives the order-of-magnitude evolution path (single server → tiers → replicas → cache → CDN → stateless tier → multi-region → sharding) and a way to diagnose the *next* bottleneck instead of memorizing one big diagram. Use it whenever a design must grow by orders of magnitude or a load increase is on the table, even if the user doesn't say "scaling".

proyecto26 Bundle

This skill should be used when the user asks "monolith vs microservices", how to "split into services", set "service boundaries", find the right "service granularity", design an "API gateway / BFF", do "service discovery" or add a "service mesh", or worries that services are "too chatty" / "too fine-grained". It gives the recipe for carving a system into services (or deciding not to) and wiring how they find and call each other. Use it whenever a design has more than one service — or someone is tempted to add more — even if the user doesn't say "microservices".

aztecprotocol Skill

Build or adjust a Linear cycle — for a whole team or just yourself. Size capacity from last-3-cycle velocity, fill with backlog bugs/high-priority items first and project work after (in your chosen project focus order), with no unassigned issues. Use when planning/prepping a cycle for a team, or when one member wants to fill/rebalance their own cycle work.

hams-ollo Skill

Author one or more atomic, agent-ready task files for the .tasks/ work-tracking system, at a gold-standard bar. Turns a rough idea, a bug report, or a ROADMAP.md Feature into task files with honest touched_files, a real parent up-link, resolved depends_on, and a mechanically-verifiable acceptance command, then self-checks them with validate.py. Deliberately elicits and decomposes before writing, because a sharp task is worth 3-10x its authoring time downstream. Assigns collision-safe ids from the existing backlog. Use when the user says "write a task for X", "add this to the backlog", "spec out this bug", "turn this into task files", "break this feature into tasks", or hands over an idea they want made executable. Requires an existing .tasks/ system (run init-worktracking first if absent). Feeds fix-batch: one task file per worktree-isolated agent.

hams-ollo Skill

Delegate a batch of independent, already-scoped task files (bugs, chores, small features) to parallel isolated agents, each sandboxed in its own git worktree, with a mandatory independent verification pass before anything is treated as mergeable. Use this whenever asked to "fix these bugs", "work through the backlog", "spin up agents to fix X, Y, Z", or "parallelize these fixes", especially when there are 2 or more distinct, independently-fixable items and the user wants them actioned rather than just discussed. It is the parallel-execution step of the kit spine: new-task authors the task files, fix-batch dispatches them, verifier-agent proves them, reconcile-worktrees lands them. Do not use it for a single fix (just do it directly) or for changes that are inherently sequential or interdependent (one agent's output feeds the next's input), since worktree isolation assumes the items do not need to see each other's work in progress.

hams-ollo Skill

Packages the current state of a project into a handoff for a person: a business partner, a client, or a team member. Produces a document or a short message, in plain language, tuned to the reader, with internals and costs redacted when the reader is a client. Use this whenever the user wants to bring a person up to speed, ask a collaborator to decide something, prep for a meeting, or draft an update. Trigger on phrases like: "write an update for my partner", "draft a message to a teammate about where we are", "summary for the client", "onboard the new dev", "prep for the meeting with the other engineering team", "what should I tell a stakeholder about the open questions", "put together a handoff doc for whoever picks this up", or any request to explain project status to a person rather than an agent. If the handoff target is a fresh agent session or a spawned subagent that will execute work, use agent-handoff instead.

hams-ollo Bundle

Scaffold a portable, agent-optimized spec-driven work-tracking system into the current repository: AGENTS.md (global rules + agent reading protocol), a .tasks/ directory of atomic task files, and (by tier) ROADMAP.md, CHANGELOG.md, a validate.py checker, and thin CLAUDE.md/Cursor/Copilot pointers. Choose a footprint tier (lite/standard/team) so small repos are not buried. Seeds AGENTS.md by inspecting the repo (package manager, test runner, CI, layout) instead of leaving blanks. Safe to re-run, and detects a pre-existing tracker before writing. Use when the user wants to set up work-item tracking, a backlog, a todo/changelog system, or task files that parallel subagents can pick up, or says things like "add work tracking here", "set up the task system", or "scaffold the backlog". Feeds the new-task and fix-batch skills: one task file per worktree-isolated agent.

novainsilico Bundle

Find and shortlist biomedical publications from PubMed for knowledge, data, or reusable-model evidence. Use for query framing, PMID/DOI discovery, bibliographic normalization, evidence prioritization, and best-effort public full-text retrieval before synthesis, extraction, or modeling. Do not use for ClinicalTrials.gov-only scoping, systematic reviews, quantitative extraction, curve digitization, calibration, or model implementation.

novainsilico Bundle

Extract or digitize reported biomedical values from papers, figures, tables, supplements, images, or web sources into traceable CSV/Markdown, optionally as a calibration-ready Jinkō data table. Use when numeric evidence must be transcribed, normalized, unit-converted, or bound to model observables. Do not use for literature discovery, evidence synthesis, or inventing values absent from the source.

novainsilico Bundle

Find and shortlist ClinicalTrials.gov registry and posted-results records for biomedical modeling evidence. Use for NCT discovery, status/phase/results screening, endpoint and population inventory, comparator landscapes, and ongoing-trial intelligence. Do not use for PubMed publication discovery, quantitative extraction, protocol authoring, Jinkō trial execution, calibration, model building, or systematic reviews.

ooiyeefei Bundle

Universal challenge tracker with flexible cadence, intelligent insights, and cross-challenge learning detection. Use when user wants to track any personal challenge - learning, habits, building, fitness, creative, or custom. Supports daily, weekly, or N-day check-ins with type-adaptive preferences, backlog, and context files.

ooiyeefei Bundle

Design, critique, or scaffold surveys grounded in Caroline Jarrett, Dillman, and Tourangeau methods. Use when designing a new survey, critiquing an existing one, scoring or clustering responses, or turning questions into an app. Triggers on "survey", "questionnaire", "user research", "customer discovery", "intent capture", "interview script", "lint my survey", "score responses", or "how to ask better questions". When the bundled MCP server is connected, prefer its deterministic tools (`critique_survey`, `get_template`, `design_survey_session`, `score_response`, `cluster_responses`) over manual reasoning. Captures real past behavior over hypotheticals, supports text/voice/AI-interviewer modalities, and includes templates for event organizers, startup founders, and gig-economy workers.

ooiyeefei Bundle

This skill should be used when the user asks to "analyze my product", "research competitors", "find feature gaps", "create feature request", "prioritize backlog", "generate PRD", "plan roadmap", "what should we build next", "competitive analysis", "gap analysis", "sync issues", or mentions product management workflows. Provides AI-native PM capabilities for startups with signal-based feature tracking, the WINNING prioritization filter, and GitHub Issues integration with deduplication.

prime-radiant-inc Skill

Layer 1 skill for SDK and ecosystem analysis. SDK discovery strategy, behavioral extraction methodology, integration test mining, output formats. Loaded by the analyzer agent for SDK and ecosystem work.

prime-radiant-inc Skill

Layer 1 intelligence source discovery - auto-detect available sources, search for public information, negotiate with user, produce inventory manifest

prime-radiant-inc Skill

Layer 2 synthesis methodology. Feature discovery, architecture reverse engineering, API extraction, cross-source synthesis with conflict resolution, module mapping. Transforms raw Layer 1 intelligence into structured synthesis documents. Loaded by the analyzer agent during Layer 2.

harshsinghmp Bundle

Quarterly personal and project strategic retreat facilitator. Conducts multi-scale deep audits of project health, architecture debt, deprecated system purges, Wheel of Life alignment, and next-quarter OKR handoffs across the LifeOS ecosystem. Generates quarterly-retreat.md.

jp-solumhealth Skill

Product intelligence for sprint planning, engineering roadmap, and execution priorities. Aggregates feature requests, bug reports, and usage patterns from customer calls (Fireflies), CRM notes (HubSpot), support interactions, and onboarding friction to produce a prioritized backlog and sprint recommendations. Use this skill when the user says "product insights", "sprint planning", "what should we build", "roadmap review", "backlog prioritization", "product roadmap", "engineering priorities", "what are customers asking for", "product sprint", "feature priorities", "bug triage", or any variation of wanting data-driven input for product decisions and engineering execution.

k-dense-ai Bundle

Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

k-dense-ai Bundle

Applies the reasoning style of Daphne Koller (machine learning pioneer, co-founder of Coursera, founder and CEO of Insitro). Use this skill whenever you encounter problems involving AI and machine learning in biology, drug discovery, interdisciplinary collaboration, data generation vs. data mining, or transitioning from academia to industry. Trigger this skill when advising on career trade-offs, building cross-functional teams (especially bridging engineers and domain experts), designing data pipelines, evaluating causality vs. correlation, or applying AI to physical systems ('where bits meet atoms'). Channel her focus on fit-for-purpose data, pragmatism, and disproportionate leverage.

k-dense-ai Bundle

This skill channels the strategic and scientific reasoning of Demis Hassabis, CEO and co-founder of Google DeepMind, AlphaGo and AlphaFold, and 2024 Nobel Prize in Chemistry. Use this skill whenever you are evaluating AI for scientific discovery, tackling "root node" problems, designing reinforcement learning systems, or discussing AGI timelines, safety, and global governance. Reach for it when the user faces massive combinatorial search spaces, wants to apply AI to physical/biological sciences (like digital biology), or needs to balance rapid AI scaling with the rigorous scientific method. Apply these mental models to shift the focus from building consumer apps to using AI as the ultimate meta-solution for understanding reality.