All Skills

When the user wants to optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't...

Turns a rough request into working software via a governed build loop: align first, plan, then iterate one task at a time with deterministic backpressure and holdout-scenario judging.

Markdown project memory for AI agents. Use for decisions, architecture, conventions, monorepo scopes, `.lore/`, or `lore` commands; not native `/init`/`/compact` or generic init/compress/audit/query.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Unified Diary System: A context-preserving automated logger for multi-project development.

Rewrite the previous response more briefly while preserving the substance.

Optimize content for AI search and LLM citations across AI Overviews, ChatGPT, Perplexity, Claude, Gemini, and similar systems. Use when improving AI visibility, answer engine optimization, or citation readiness.

Use the `gh` CLI for issues, pull requests, Actions runs, and GitHub API queries.

Interact with GitHub issues - create, list, and view issues.

Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Compare AI agent-made UI variants locally in a real app, then keep one and clean up unused temporary code.

Modern Angular (v20+) expert with deep knowledge of Signals, Standalone Components, Zoneless applications, SSR/Hydration, and reactive patterns.

Build native UI with the @expo/ui package: real SwiftUI on iOS and Jetpack Compose on Android rendered from React in an Expo or React Native app. Covers universal cross-platform components (Host, Column, Row, Button, Text, List, and more imported from @expo/ui), drop-in replacements...

Use HasData APIs for web scraping and structured web data extraction.

Human-in-the-loop web studio to tune AI-generated output by eye. Stand up a local interactive studio (sliders, pickers, drag handles) or an inline edit/highlight/comment annotation studio for prose & media, instead of guessing values or shipping a static comparison grid.

When the user wants help with pricing decisions, packaging, or monetization strategy. Also use when the user mentions 'pricing,' 'pricing tiers,' 'freemium,' 'free trial,' 'packaging,' 'price increase,' 'value metric,' 'Van Westendorp,' 'willingness to pay,' 'monetization,' 'how much...

Audit a component or page for accessibility issues and fix them

Design user flows and navigation structure following proven UX patterns

Ask which skill or flow fits your situation. A router over the user-invoked skills in this repo.

Use when the user says "task:", "fix:", "todo:", "log task:", "log fix:", "run task:", "run fix:", or asks to log, run, list, or manage project tasks. Also use when asked to generate or update a changelog from completed tasks.

Apply opinionated, production-grade Python design and review standards. Use for substantial Python implementation, refactoring, API design, module design, exception boundaries, interfaces, or a deep code-quality review; not for a quick standalone script.

Configure and maintain Python projects, dependencies, packaging, environments, linting, formatting, type checking, testing, builds, and publishing. Use for pyproject.toml, an existing repository toolchain, or repository-level Python tooling work, not for a standalone script.

Use when work requires inter-agent communication, cross-layer coordination, or configurable autonomy with Claude Code Agent Teams

Use when answering difficult or disputed TypeScript questions about assignability, inference, narrowing, variance, generics, conditional or mapped types, declarations, or compiler-option semantics.

Use to build a data-subject erasure plan for support conversations — GDPR right to erasure, CCPA deletion, right to be forgotten. Trigger for "delete this customer's data", DSR or SAR erasure request, GDPR deletion from our helpdesk, redacting personal data from tickets, or working out what can and cannot be erased.

Use to compute and monitor regulated complaint deadlines — acknowledgement, holding response, final response — against business-day calendars, and to find cases about to breach. Trigger for "are we meeting our complaint deadlines", "complaint SLA breaches", "final response deadline", "which complaints are about to breach", complaint ageing reports, or building a complaints deadline monitor.

Use to test whether a support control actually operates, the way an auditor would test it, rather than confirming it exists on paper. Trigger for "test our controls", "does this control actually work", second-line assurance over support, control effectiveness testing, preparing for an internal audit, or a control that passed review and then failed in practice.

Use to find the activation blockers visible in support contacts from new customers, before they show up in retention numbers months later. Trigger for "why aren't new customers activating", "what do new customers contact us about", onboarding drop-off, time-to-value analysis, first-90-days support experience, or a retention problem that traces back to activation.

Use to build a defensible cost per contact and cost to serve by driver, channel or customer segment, for automation business cases and budget decisions. Trigger for "what does a ticket cost us", "cost per contact", "what would automating this save", "cost to serve by segment", building a business case for a bot or a process change, or a savings claim that looks too good.

Use to quantify what support quality failures cost — rework, credits, escalation handling, complaints and churn — with the causal limits stated. Trigger for "what do our quality failures cost", "cost of rework in support", "business case for improving quality", "how much is bad support costing us", or a quality investment that needs a number attached.

Use to run customer support communications during an outage or major incident — banners, macros, status page, volume planning, and postmortem inputs. Trigger for "incident comms playbook", "support during outage", status page and help centre banner, macro for widespread issue, staffing surge, or freeze non-incident work.

Use to analyse touch time vs elapsed time across the resolution path — queue wait, customer wait, back-office wait, and where elapsed time hides real delay. Trigger for "flow efficiency", "touch time vs elapsed time", "where is the time going", "customers waiting on us", back-office bottlenecks, idle time in cases, or end-to-end time much longer than handle time.

Use to find duplicate or repeated support conversations and produce a reviewable merge plan. Trigger for "find duplicate tickets", "customers contacting us twice about the same thing", deduplicating a helpdesk, cleaning up ticket volume, or measuring how much of your contact volume is the same issue submitted more than once.

Use to audit a CX dashboard for tiles nobody acts on and metrics nobody agrees on — assign an owner per tile, apply kill criteria, and run an actionability test before adding or keeping a chart. Trigger for "clean up the dashboard", "too many metrics", "which KPIs should we keep", dashboard audit, metrics review workshop, "nobody looks at this anymore", or before a BI migration or exec-pack refresh.

Use to verify that the chain from conversation to evaluation to decision is complete and reconstructible, so a QA score or a customer outcome can be explained months later. Trigger for "can we prove why this decision was made", "is our audit trail complete", "reconstruct how this score was produced", evidencing an AI-assisted decision, or an auditor asking how a conclusion was reached.