Browse Skills

Ponytail Debt scans a codebase for `ponytail:` comments and compiles them into a debt ledger, flagging entries with no trigger as rot risks. Use it when asked to list shortcuts or review what was deferred, as a one-shot report that changes nothing.

Ponytail Gain displays a compact scoreboard of ponytail's measured impact: less code, less cost, more speed, using benchmark medians from five tasks and three models. Use /ponytail-gain to see savings and link to per-repo debt and audit commands.

Ponytail Help displays a one-shot reference card for all ponytail modes, skills, and commands. Use it when you need to recall how to invoke ponytail lite, full, ultra, reviews, audits, and configuration options.

Ponytail Review flags over-engineering in code diffs: dead code, reinvented standard library functions, unneeded dependencies, and speculative abstractions. Use it when asking for a simplicity review or 'what can we delete'.

Cavecrew provides a decision guide for when to spawn caveman-style subagents — investigator, builder, reviewer — to locate code, make small edits, or review diffs, returning compressed output that saves main context. Use it when delegating tasks in Claude Code to reduce token usage.

Caveman compresses AI output into terse, technically accurate caveman-style responses, cutting token usage by 65% while preserving meaning. Use when the user requests caveman mode, brevity, or token efficiency.

Caveman Commit generates concise Conventional Commits messages that cut noise while preserving intent and reasoning. Use it when asked to write a commit message or when staging changes.

Caveman Compress compresses natural language memory files (CLAUDE.md, todos, preferences) into caveman-speak to save input tokens, preserving code, URLs, paths, and structure. It backs up the original and overwrites the file.

Caveman Discover finds every LLM workflow in the current repository and labels it so Caveman Cloud groups spend by what the code actually does. Use when the user asks to discover workflows or break LLM spend down by workflow.

Caveman Evidence Review reads Caveman Cloud data read-only, covering costs, Cave Score, Cave Plan, workflows, traces, latency, errors, compression, routing, and verified savings. Use it when you need to know where LLM spend goes, why cost or quality changed, or which workflows need attention.

Caveman Experiment Manager inspects Caveman Cloud's eval-gated experiment lifecycle, reads current state and results, and reports one supported recommendation or blocks unsafe actions. Use it when asked to start, approve, cancel, promote, or roll back a Caveman experiment.

Caveman Explore is a read-only repository explorer that returns compact path:line citations for where code lives. Use it proactively for cold-start exploration, broad cross-file localization, or when a direct search has failed and you need to find where something lives.