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Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

Run comprehensive agent-native architecture review with scored principles

Explore requirements and approaches through collaborative dialogue before writing a right-sized requirements document and planning implementation. Use for feature ideas, problem framing, when the user says 'let's brainstorm', or when they want to think through options before deciding what to build. Also use when a user describes a vague or ambitious feature request, asks 'what should we build', 'help me think through X', presents a problem with multiple valid solutions, or seems unsure about scope or direction — even if they don't explicitly ask to brainstorm.

Refresh stale or drifting learnings and pattern docs in docs/solutions/ by reviewing, updating, replacing, or archiving them against the current codebase. Use after refactors, migrations, dependency upgrades, or when a retrieved learning feels outdated or wrong. Also use when reviewing docs/solutions/ for accuracy, when a recently solved problem contradicts an existing learning, or when pattern docs no longer reflect current code.

Document a recently solved problem to compound your team's knowledge

Generate and critically evaluate grounded improvement ideas for the current project. Use when asking what to improve, requesting idea generation, exploring surprising improvements, or wanting the AI to proactively suggest strong project directions before brainstorming one in depth. Triggers on phrases like 'what should I improve', 'give me ideas', 'ideate on this project', 'surprise me with improvements', 'what would you change', or any request for AI-generated project improvement suggestions rather than refining the user's own idea.

Transform feature descriptions or requirements into structured implementation plans grounded in repo patterns and research. Use when the user says 'plan this', 'create a plan', 'write a tech plan', 'plan the implementation', 'how should we build', 'what's the approach for', 'break this down', or when a brainstorm/requirements document is ready for technical planning. Best when requirements are at least roughly defined; for exploratory or ambiguous requests, prefer ce:brainstorm first.

[BETA] Structured code review using tiered persona agents, confidence-gated findings, and a merge/dedup pipeline. Use when reviewing code changes before creating a PR.

Explains your product's technical architecture in plain English — so you understand what's being built, can make informed decisions, and can describe it to others. Use when an agent has set up your tech stack and you're not sure how the pieces connect, or when you need to explain your system to a potential co-founder, investor, or hire. Produces an architecture-overview.md in plain language with a simple diagram.

Prioritizes what to build in what order, given your MVP brief and tech stack. Use when you have a feature list and don't know where to start, or when you're building in the wrong order and feeling stuck. Produces a build-sequence.md with a sequenced backlog and a clear "build this first" decision.

Recommends the specific third-party services to use for auth, payments, email, storage, and other common needs. Use when building a feature that requires an external service and you're unsure which tool to use or how to set it up. Produces an integrations-plan.md with specific tool choices and the order to add them.

Recommends the right technology stack for your product, branching by product type (consumer mobile, B2B SaaS, internal tool, API/developer tool, marketplace, AI-native). Use before starting to build, when you're unsure what tools to use, or when an agent has suggested a stack that feels overwhelming. Produces a stack-decision.md with specific tool recommendations and setup order.

The recurring founder ritual — run after every meaningful ship or weekly, whichever comes first. Guides you through honest reflection, user signal synthesis, MPP/PMF assessment, and one clear decision for the next cycle. Produces a dated cycle record that compounds over time. Use whenever you've shipped something and want to figure out what it taught you and what to do next.

Diagnoses why your product isn't gaining traction after multiple build cycles. Spawns 5 parallel hypothesis agents — one per failure mode — each gathering independent evidence. Synthesizes into a ranked diagnosis with a specific 2-cycle prescription. Triggered automatically by build-cycle after 3+ flat cycles, or invoked directly when stuck. Produces diagnosis.md.

Gives an honest, scored assessment of whether your product has crossed the Minimum Proud Product threshold. Uses 5 parallel scoring agents — one per criterion — then synthesizes into a composite score and the single most important gap to close. Use every 3–4 build cycles or before any major share/launch moment. Produces mpp-scorecard.md.

Establishes the visual tone, typography, color, and feel of your product before a line of UI is written. Use when you're about to build your UI and need a clear aesthetic direction, or when your product looks generic and you can't articulate why. Produces a design-brief.md with specific font, color, and visual direction choices your agent can implement immediately.

Diagnose training issues with Tinker in Pi or another coding-agent shell — slow steps, hanging sessions, output mismatches, error messages, renderer problems, and deployment issues. Use this skill whenever a user reports that training is slow, steps take too long, sessions are hanging, model outputs differ between Tinker and external engines (vLLM, SGLang), they get a confusing error message, training quality is poor (high KL, bad outputs), or they suspect something is wrong. Also trigger when users ask "is this a Tinker issue or my issue?", "is Tinker down?", report unexpected wait times, see output quality regressions, get opaque errors, or want to profile/debug their training or deployment pipeline. This skill walks through systematic triage to determine root cause.

Conduct post-training research for LLMs using the Tinker API in Pi or another coding-agent shell — replicate paper results, explore new training ideas, run and monitor experiments, and document findings. Use this skill whenever the user wants to do research, replicate experiments from a paper or repo, investigate training hypotheses, run experiment sweeps, explore post-training techniques (SFT, RL, DPO, distillation, etc.), set up training, write training code, choose a model, tune hyperparameters, manage checkpoints, export weights, or analyze training logs — even if they just say "try this idea" or "let's see what happens if...".

Research what people are saying about a topic in the last 30 days using the local last30days engine from pi across Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, Digg, and optional web/social sources.

Research what people actually say about any topic in the last 30 days. Pulls posts and engagement from Reddit, X, YouTube, TikTok, Hacker News, Polymarket, GitHub, and the web.

Review a product idea, spec, or codebase for agent-native quality. Use when checking whether a new product, MVP, or AI feature truly treats agents as first-class citizens.

Turn a product idea into an agent-native architecture and build plan. Use when designing a new product, MVP, internal tool, or AI feature where agents should be first-class citizens and achieve outcomes in a loop with tools.

Design, build, secure, evaluate, and ship production-grade Vercel Eve agents from Pi. Use when the user wants to create, plan, improve, or audit an Eve agent; map requirements to Eve tools, skills, connections, channels, schedules, subagents, sandbox, auth, approvals, and evals; or migrate from a demo agent to a durable production agent.

Beginner-first open-model fine-tuning workflow using FineTuneKit and Unsloth. Use whenever the user wants to fine-tune, adapt, improve, evaluate, export, or debug an open-source LLM with Unsloth, especially from Pi, Claude Code, or Codex. Covers dataset checks, model/hardware selection, project generation, SFT training, evals, chat testing, and safe iteration.

Brutally honest daily brief for a founder. Reads today's calendar, recent GitHub activity (PRs, issues, action-required notifications), and git activity across local projects, then writes a yesterday-score + truth-memo + top 3 actions + reply drafts. Use when the user says: daily brief, morning brief, what should I work on today, what happened yesterday, EOD, standup, founder brief.