Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
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Collaborative document creation via a structured three-stage workflow. Use for writing specs, PRDs, design docs, proposals, RFCs, and any long-form document where quality and clarity matter. Brainstorms 5-20 options per section, builds iteratively, and tests with reader sub-agents.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
"Thoroughness here prevents confusion when actual stakeholders read the document."
This skill guides collaborative document creation through three structured stages: context gathering, iterative section refinement, and reader testing. The goal is to transfer the author's full intent into a document that works for readers who lack that context.
Triggers for this skill:
Do not use for: short single-purpose outputs (emails, commit messages, code comments). Those are better handled inline.
Close knowledge gaps before writing anything.
Ask clarifying questions:
Accept info-dumps: If the user provides a brain dump, meeting notes, or links to existing docs — absorb everything. Extract the key requirements, constraints, and decisions already made.
Meta-context check:
Stage 1 output: A clear scope statement: "We are writing a [type] for [audience] that enables [decision/action]. The document is [scope]."
Build the document iteratively, one section at a time.
Scaffold first: Create a full document outline with placeholder sections immediately. This gives the author a map of where we're going and lets them see the overall structure before committing to any section.
# [Document Title]
## Overview
[placeholder — will cover: purpose, scope, key decisions]
## Background
[placeholder — will cover: context, motivation, prior art]
## Proposal
[placeholder — will cover: what we're building, why this approach]
## Alternatives Considered
[placeholder — will cover: what we ruled out and why]
## Implementation Plan
[placeholder — will cover: phases, timeline, dependencies]
## Open Questions
[placeholder — will cover: unresolved decisions needing input]
Per-section workflow:
Brainstorm: Generate 5-20 options for how to structure/frame this section
Draft: Write the selected approach fully
Refine: Make targeted edits based on feedback
str_replace to replace specific phrases or paragraphsConfirm: Get explicit "this section is done" before moving to the next
Option generation example (for an "Alternatives Considered" section):
Here are 5 options for structuring "Alternatives Considered":
1. **Elimination table** — Each alternative as a row, columns for pros/cons/why-rejected. Fast to skim.
2. **Narrative paragraphs** — One paragraph per alternative with story arc of why we considered and rejected it. Good for complex tradeoffs.
3. **Decision log format** — Date + decision + rationale per item. Good when alternatives were explored over time.
4. **Comparative matrix** — All options including chosen approach on axes like complexity/risk/value. Visual.
5. **One-liner summary** — Brief acknowledgment that we considered X, Y, Z, with a single sentence each. Good when alternatives are obvious.
Which resonates, or should I blend elements?
Test whether the document works for readers who lack the author's context.
Why this matters: Authors know too much. Blind spots are invisible to them. A reader sub-agent with no conversation context surfaces exactly what real readers will struggle with.
Spawn a reader sub-agent with ONLY the document content (no prior conversation context):
You are reading this document for the first time with no prior context.
Read this document: [full document text]
Answer these questions:
1. What is this document trying to accomplish? (Should match author intent)
2. What questions does it leave unanswered that a reader would need answered to act?
3. Are there any sections where the reasoning is unclear or jumps without explanation?
4. What would a skeptical reader's strongest objection be?
5. Is there anything that only makes sense if you already know [the thing the author knows]?
Integrate reader feedback:
Completion criteria:
str_replace for precisionWhen making edits, specify the exact text to replace:
REPLACE:
"The system will be fast."
WITH:
"The system targets p99 latency < 100ms under 1000 concurrent users."
Never reprint the entire document to show one changed sentence. This respects the author's time and makes it easy to see exactly what changed.
If the author writes in a particular style (casual/formal, first-person/third-person, active/passive), match it in all additions. Note their stylistic choices explicitly:
Sections: Executive Summary, Problem Statement, Goals & Non-Goals, User Stories, Requirements, Success Metrics, Timeline, Open Questions
Sections: Summary, Background, Design, Implementation Details, Testing Plan, Rollout Plan, Alternatives Considered, Open Questions
Sections: Abstract, Motivation, Detailed Design, Drawbacks, Alternatives, Unresolved Questions
Sections: Executive Summary, Problem, Proposed Solution, Business Case, Risks, Timeline, Resources Required, Next Steps
Before starting a co-authoring session:
cat .claude/context/memory/learnings.md | grep -i "doc\|writing\|spec\|prd"
After completing a document, record patterns that worked:
.claude/context/memory/learnings.md.claude/context/memory/issues.mdtechnical-writer agent — Documentation structure and stylebrainstorming — Extended brainstorming sessions for complex sectionsspec-gathering — Requirements gathering before doc creationprd-generator — Structured PRD generation workflow还没有评价。欢迎分享你的使用感受。
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Ensure accessibility in UI components including semantic HTML, ARIA attributes, keyboard navigation, and WCAG 2.2 AA compliance.
中文简介正在准备中。当前显示原文说明。
Use when you want to improve response quality through meta-cognitive reasoning. Applies 15+ reasoning methods to reconsider and refine initial outputs.
中文简介正在准备中。当前显示原文说明。
N-round opposing-stance debates for trade-off analysis. Assigns pro/con roles to agents, runs structured debate rounds with quality scoring, and produces a moderator synthesis with confidence-rated recommendation. Generalizable to architecture, technology, security, and design decisions.
中文简介正在准备中。当前显示原文说明。
Force adversarial code review stance that eliminates confirmation bias — reviewer must find issues or re-analyze
中文简介正在准备中。当前显示原文说明。
Creates specialized AI agents on-demand when no existing agent matches a request. Use when the Router cannot find a suitable agent for a task. Enables self-evolution by generating persistent agents.
中文简介正在准备中。当前显示原文说明。
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations
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