Browse skills

Run an independent peer interpretation of third-party feedback via codex. Returns structured intent interpretations without applying changes. Use when the user asks to "peer interpret feedback", "get a second opinion on this feedback", or "interpret feedback independently".

Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.html. Use when the user asks to "map the codebase", "map codebase", "architecture report", "codebase overview", "architecture overview", "what am I looking at", or "explain this codebase".

Detect agentic coding infrastructure in a project: CLAUDE.md, AGENTS.md, installed skills, MCP servers, hooks, and cross-tool compatibility (Claude Code and Codex CLI). Returns structured findings about agentic readiness without applying changes. Use when the user asks to "review agentic setup", "check agentic setup", "agentic readiness", "is this project set up for AI coding", or "review AI coding setup".

Commit, push, and optionally create or update a PR for the current staged changes. Use when the user asks to "ship", "ship it", "ship changes", "commit push and PR", or "ship this".

Execute an approved split plan by creating separate branches, commits, and PRs for each change group. Use when the user asks to "split and ship", "ship the split plan", "create separate PRs", or "split changes into branches".

Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.

Done-for-you .faf generator. One-click AI context for any project - new, legacy, or famous. Auto-detects stack, scores readiness, works everywhere.

Darwinian idea evolution engine — toss rough ideas onto an evolution island, let them compete, crossbreed, and mutate through structured rounds to surface your strongest concepts.

Clinically informed wisdom companion blending psychology and philosophy into a structured thinking partner

Use when the user asks to track technical changes, create change records, manage TC lifecycles, or hand off work between AI sessions. Covers init/create/update/status/resume/close/export workflows for structured code change documentation.

Audit a StyleSeed-based component or page for WCAG 2.2 AA issues and apply practical accessibility fixes where the code makes them safe.

Auto-update third-party marketplace plugins on session start

Generates original sports journalism articles by consuming real-time data from the sports-skills skills. Covers game previews, live reports, post-game, team analysis, and player profiles for all supported sports. Use when: the user asks to write, generate, create, or draft an article, preview, report, analysis, summary, or journalistic coverage about games, teams, players, scores, statistics, or sports results. Do not use when: the user only wants raw data without journalistic text — use the sport-specific skill directly (nfl-data, nba-data, football-data, etc.). Do not use when: the user wants to search for news published by third parties — use sports-news.

Design user flows and screen structure using StyleSeed UX patterns such as progressive disclosure, hub-and-spoke navigation, and information pyramids.

Guides creation of Monte Carlo monitors via MCP tools, producing monitors-as-code YAML for CI/CD deployment.

Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.

Expert guide for pushing metadata, lineage, and query logs to Monte Carlo from any data warehouse.

Generates SQL validation notebooks for dbt PR changes with before/after comparison queries.

Ingest a source file from raw/ into the LLM Wiki — read, discuss, write summary page, update cross-references across 5-15 pages, regenerate index, append to log. Usage /wiki-ingest <path-to-source>

Provides guidance for performing causal interventions on PyTorch models using pyvene's declarative intervention framework. Use when conducting causal tracing, activation patching, interchange intervention training, or testing causal hypotheses about model behavior.