Run a multi-agent review of code comments and markdown documentation for unnecessary content, then fix the issues. Covers what-restating comments, name-mirroring doc comments, status-update prose, and other documentation noise. Use when the user asks to \"simplify docs\", \"simplify documentation\", \"clean up comments\", \"clean up docs\", \"review documentation\", \"strip unnecessary comments\", \"reduce doc noise\", or \"run simplify-docs\".
npx skills add https://github.com/tobihagemann/turbo --skill simplify-docs
Review code comments and markdown documentation for unnecessary content, then fix the issues.
Determine what to review:
git diff --cached), use that.git diff, git diff --cached, git diff HEAD) based on the current git state. If there are no git changes, default to a full-tree sweep of source files plus top-level markdown.Launch both agents below with spawn_agent / wait_agent using inherited model defaults so they run concurrently. Pass the scope from Step 1 to each agent. Every sub-agent's prompt must direct it to treat the shared working tree and its git index as read-only and to reach its findings by reading and reasoning; fixes happen in Step 3.
Review code files in scope. Flag a comment when it adds no information beyond what the code already says:
Keep these: comments that capture a load-bearing constraint the code itself cannot express — a hidden constraint or invariant, a workaround for a specific bug (ideally with a reference), a non-obvious performance characteristic, a pointer to a spec or RFC section, or behavior that would surprise a future reader and lead them to "fix" working code. Greenfield test: would you write this comment if the code had been greenfield from day one?
For each finding, propose: delete it, compress to the load-bearing WHY, or flag a refactor that would make the comment unnecessary.
Review markdown files in scope (READMEs, AGENTS.md, docs/, contributor guides). Flag passages that add no information beyond what the reader can derive from current state:
Keep these: passages that explain motivation, capture constraints or tradeoffs the code can't express, document interfaces meant for outside readers, or record decisions whose rationale would otherwise be lost.
For each flagged passage, propose: delete it, tighten it, or rewrite it as timeless current-state prose.
Wait for both agents to complete. Aggregate their findings, then apply each fix directly, skipping false positives. When uncertain whether a comment captures a non-obvious WHY, keep it.
When done, briefly summarize what was removed or rewritten (or confirm the docs were already clean).
Then call update_plan to mark this step completed and continue with the next step of the active workflow.
Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementing any feature or bugfix, before writing implementation code
Use when you have a spec or requirements for a multi-step task, before touching code
Use when creating new skills, editing existing skills, or verifying skills work before deployment
Use when writing or improving README files. Not all READMEs are the same — provides templates and guidance matched to your audience and project type.
| Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Official Opentrons Protocol API for OT-2 and Flex robots. Use when writing protocols specifically for Opentrons hardware with full access to Protocol API v2 features. Best for production Opentrons protocols, official API compatibility. For multi-vendor automation or broader equipment control use pylabrobot.
Take tobihagemann/turbo-simplify-docs from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.