Browse skills

Send and receive iMessage, SMS, and RCS from application code via the Sendblue HTTP API — text, media, group messages, send styles, reactions, typing indicators, status callbacks, and inbound webhooks.

Send iMessage and SMS from the shell via the @sendblue/cli npm package — outbound sends, contact management, and account setup with no API client or webhook server required.

Text the user's phone when a long-running task, agent turn, or scheduled job finishes — via @sendblue/cli for outbound, optionally wired to a Claude Code Stop hook for automatic fire.

Run Bumblebee supply-chain inventory and exposure scans on macOS/Linux to detect compromised packages, extensions, and MCP host configs.

Lower Big-O on existing code via a one-transformation-at-a-time playbook with verify-revert-stop. For new code use lemmaly; for math-level wins escalate to mathguard.

Guide stuck or overwhelmed users through targeted branching questions until they reach concrete next steps.

Use when planning and synthesizing product/user research as a method-and-repository discipline — selecting the right method for the goal (generative interviews vs usability test vs concept test vs validation), computing method-based saturation/sample size with an explicit confidence level, or synthesizing coded observations into insights while flagging single-source anecdotes. Never fabricates user insight; an insight requires recurrence across independent participants. Distinct from product-team/ux-researcher-designer (persona/journey artifacts), product-discovery (discovery-sprint planning), and experiment-designer (live A/B) — this is the research-ops method + insight-repository layer.

Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.

Use when managing the money for an internal R&D program or portfolio — building a multi-period program budget with the F&A (indirect) split, tracking burn rate and runway against value-inflection milestones, or routing R&D cost items to a capitalize-vs-expense determination. Every budget output surfaces its assumptions block; capitalize-vs-expense is decision-support only and routes to a named finance owner — it never books an entry or decides accounting treatment. Distinct from finance/financial-analysis (corporate DCF, close, valuation) and research/grants (funding discovery — this manages money already won).

Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.

2D game development principles. Sprites, tilemaps, physics, camera.

Accessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.

Use when planning, funding, scoping, or synthesizing enterprise research across workstreams — clinical study design, R&D program finance, market sizing/surveys, or product/user research. Triggers on "design this clinical study", "what sample size", "R&D budget", "burn rate", "capitalize or expense", "TAM SAM SOM", "market sizing", "survey design", "segment the market", "plan user interviews", "usability test", "synthesize research insights". Forks context to route to one of four Research-Operations sub-skills (clinical-research, research-finance, market-research, product-research) and returns a digest. Distinct from ra-qm-team (regulatory submission), finance (corporate close/valuation), research/grants (funding discovery), product-team (persona/journey/live experiments), and marketing-skill (campaign analytics).

Analyze auto-memory for promotion candidates, stale entries, consolidation opportunities, and health metrics. Use when the user runs /si:review or asks what has been learned and what should be promoted or pruned.

Use when doing upstream market-research methodology — sizing a market as TAM/SAM/SOM computed BOTH top-down and bottoms-up (never a single unsourced number), planning a survey sample size with finite-population correction and per-segment minimums, or scoring candidate market segments against Kotler's measurable/substantial/accessible/differentiable/actionable criteria. Outputs always show the method and the assumptions. For market-research analysts and product-marketing at the sizing/survey/segmentation moment. Distinct from marketing-skill (campaign analytics, attribution, demand-gen) — this is the evidence-building methodology, not live-campaign optimization.

Generates publication-quality figures for ML papers from research context. Given a paper section or description, extracts system components and relationships to generate architecture diagrams via Gemini. Given experiment results or data, auto-selects chart type and generates data-driven figures via matplotlib/seaborn. Use when creating any figure for a conference paper.

Add premium animated UI components built with Framer Motion and Tailwind CSS using Aceternity UI. Use when: adding animated effects (3D cards, glowing backgrounds, spotlight effects, moving borders), building visually stunning UIs, creating impressive hero sections and feature showcases in React/Next.js.

Run GitHub Actions locally with act. Use when a user asks to test GitHub Actions workflows locally, debug CI pipelines without pushing, or run workflows offline.

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

Use when a task spans multiple AEC technologies (Blender, IfcOpenShell, Bonsai, Sverchok) and you need to decide which tool handles which step, or when orchestrating multi-step BIM pipelines such as IFC creation to visualization to authoring. Prevents the common mistake of mixing IfcStore.get_file() with ifcopenshell.open() in the same context, or calling Bonsai operators outside a Bonsai-loaded session. Provides decision trees for technology selection, workflow sequencing, and bridge patterns between tools. Keywords: cross-technology, workflow orchestration, BIM pipeline, technology selection, Blender IfcOpenShell Bonsai integration, multi-step AEC, bridge pattern, which tool should I use, combine Blender and IFC, multi-tool workflow, how to connect tools.