Writing

Defines how to author, revise, and validate SKILL.md files so agent skills stay executable, scoped, and testable. It focuses on turning vague know-how into reusable operational instructions with clear triggers, deterministic steps, and verification checks.

Converts raw meeting transcripts into structured, actionable notes with decision logs, assigned action items, and key context preserved for future AI retrieval. This skill bridges the gap between what was discussed in a meeting and what AI agents need to know when acting on outcomes days or weeks later.

Extracts architecture decisions, API contracts, and usage patterns directly from code to produce accurate documentation that stays in sync with implementation. Documentation-from-code treats code as the source of truth and generates prose from it rather than maintaining documentation as a separate artifact that diverges over time.

Writes product requirements documents that AI agents can act on reliably, with explicit constraints, edge cases, and acceptance criteria that minimize the gap between what you mean and what the agent builds. This skill bridges the ambiguity of natural language product specs and the precision that AI agents require to produce consistent results.

Removes the common AI-generated writing patterns—significance inflation, filler -ing constructions, em-dash chains, and formulaic closers—that make machine-generated prose feel generic or overproduced. Runs a final 'still obviously AI?' audit pass before shipping any prose intended for human readers.

Tightens Chinese drafts by removing translationese, slogan-like endings, stacked abstractions, and stiff AI rhythm while preserving factual accuracy. This addresses the specific failure modes of machine-translated or AI-generated Chinese text: word-for-word English structures, Western rhetorical patterns that feel unnatural to Chinese readers, and filler phrases that add length without meaning.

Frames a pull request so reviewers understand the risk profile, what has been tested, and where to focus their limited attention. This produces faster, more useful reviews because reviewers spend less time reconstructing context and more time evaluating the actual changes.

Converts vague or frozen requirements into precise, step-by-step implementation plans with file-level touchpoints, decision checkpoints, and verifiable acceptance criteria before any code is written. This bridges the gap between what stakeholders want and what engineers can actually ship, reducing mid-sprint surprises and wasted refactors.

Structures how you respond to code review feedback so the review process stays focused, respectful, and productive. This skill separates substantive feedback from nitpicks, tracks follow-ups without losing them, and produces a record that makes merges faster and post-mortems clearer.