pace

Generate a config.yaml for the pace personal dashboard from a user's natural-language description of interests. Maps topics to content adapters (RSS, Hacker News, Reddit, GitHub, arXiv, YouTube, Mastodon, etc.), composes transform pipelines, designs flexbox layouts, and optionally wires up LLM-powered summarization and ranking. Use when asked to configure, set up feeds for, or customize a pace dashboard.

Install and run the pace personal dashboard. Covers cloning, dependency install via Bun, Docker and Docker Compose deployment, CLI flags (--config, --port), environment variable overrides (PACE_CONFIG, PORT), themed starter configs, and troubleshooting common startup errors. Use when asked to set up, install, deploy, or run a pace dashboard.

Generate a config.yaml for the pace personal dashboard from a user's natural-language description of interests. Maps topics to content adapters (RSS, Hacker News, Reddit, GitHub, arXiv, YouTube, Mastodon, etc.), composes transform pipelines, designs flexbox layouts, and optionally wires up LLM-powered summarization and ranking. Use when asked to configure, set up feeds for, or customize a pace dashboard.

Install and run the pace personal dashboard. Covers cloning, dependency install via Bun, Docker and Docker Compose deployment, CLI flags (--config, --port), environment variable overrides (PACE_CONFIG, PORT), themed starter configs, and troubleshooting common startup errors. Use when asked to set up, install, deploy, or run a pace dashboard.

Use when the user specifies a task and a duration, and the work should be done iteratively by subagents over that time period

Timeboxed ideation on a topic using propose-and-critique subagent pairs. Use when the user wants to brainstorm, explore ideas, discover features, generate options, or think through possibilities for a specified duration. Triggers on requests like "brainstorm X for 30 minutes", "ideate on X", "spend an hour thinking about X", "what features should we build", "explore options for X".

Use when the user wants to systematically fix AI code slop — duplicated logic, over-engineering, silent error swallowing, convention drift, cargo-cult patterns, and other LLM-introduced architectural decay — over a specified duration

Use when the user requests integration testing, feature validation, or test plan execution

Systematically explore and test any software project (CLI, API, Backend, Library, etc.) to find bugs, usability issues, and edge cases. Produces a structured report with full reproduction evidence (exact commands, inputs, logs, and tracebacks) for every issue.

Manage .facts files — atomic, validatable truth statements about a project. Install, check, list, add, edit, remove, and lint facts via the CLI. ALWAYS read this skill when the user mentions facts in any capacity.

Operate on @draft facts — collaboratively refine them into precise, actionable @spec facts. Resolve ambiguities, fill gaps, eliminate contradictions, and sharpen labels until every fact is ready to implement. Use when asked to refine facts, clarify the spec, review facts for quality, or "work on facts" with the user.

Scan the codebase and classify every fact by lifecycle stage — tag @draft, @spec, or @implemented based on what the code actually shows. Add missing facts, fix inaccurate ones, remove obsolete ones. Use when asked to discover facts, bootstrap or update a fact sheet, scan the codebase for truths, sync facts to match the code, or audit the fact sheet for accuracy.

Operate on @spec facts — implement them in code, then tag @implemented. Use when asked to implement facts, implement the spec, build from the fact sheet, make facts true, or work through unimplemented requirements.