Skillquality 0.46

doc-writing

Write documents using the HWPR/AWOR framework -- separating human value judgments from AI-expanded content so critical information is not buried. Triggers when the user requests writing, rewriting, or reviewing document quality.

Price
free
Protocol
skill
Verified
no

What it does

Doc Writing (HWPR/AWOR)

In the AI era, human value judgments get buried in AI-expanded long documents. This skill uses HWPR/AWOR markers so readers (human or AI) can quickly locate what the human actually thought.

For the detailed template, see examples/TEMPLATE-HWPR.md.


Core Concepts

  • [HWPR] (Human Wrote, Please Read): Unknown context + value judgments written by a human. Must be short (3-5 sentences).
  • [AWOR] (AI Wrote, Optional Read): Detailed content expanded by AI. Can be deleted, modified, or replaced.

Rules

  1. Never modify [HWPR] content — AI may only read HWPR paragraphs; it must not rewrite, rephrase, merge, or "polish" them
  2. HWPR must be short — Each HWPR paragraph must not exceed 3-5 sentences; write only: unknown context + value judgments
  3. Value judgments with humility — Use phrasing like "I believe" / "current judgment" / "possibly" in HWPR, acknowledging potential error
  4. [AWOR] can be freely modified — AI-expanded content may be replaced, deleted, or rewritten at any time
  5. Consistent marker format — Use bold markers **[HWPR]** and **[AWOR]** as headers, followed by paragraph titles
  6. HWPR uses blockquote — HWPR body text uses > block quotes for visual distinction

Execution Flow

Mode A: Write a New Document

Trigger Conditions

User requests "help me write a document," "write a proposal," "draft a PRD," etc.

Step 1: Guide HWPR Extraction

Ask the user questions to extract core value judgments:

To write an effective document, I need you to provide the following HWPR content (keep it brief, 1-3 sentences per item):

1. **Background**: Why are we doing this? What is the core problem?
2. **Judgment**: What do you think we should do? Why this direction?
3. **Trade-offs**: What was deliberately given up? What are the known risks?

Step 2: Confirm HWPR

Organize the user's answers into HWPR paragraphs and display them for user confirmation. Once confirmed, HWPR is never modified afterwards.

Step 3: Generate Complete Document

Following the TEMPLATE-HWPR.md structure, expand corresponding AWOR paragraphs after each HWPR paragraph.


Mode B: Rewrite an Existing Document

Trigger Conditions

User provides an existing document and requests "restructure using HWPR/AWOR," "split and label," etc.

Step 1: Identify Potential HWPR

Read the full text and mark sentences/paragraphs that appear to contain human value judgments (identification criteria: contains subjective decisions, trade-offs, "we chose" / "gave up" language, etc.).

Step 2: Confirm with User

List the identified results and ask the user to confirm each one:

I identified the following as potentially your value judgments (HWPR) in the document. Please confirm:

1. yes/no "We chose option B because..." (paragraph X)
2. yes/no "Abandoned real-time push, switched to polling..." (paragraph Y)
3. yes/no ...

Step 3: Split, Label + Expand

Extract confirmed HWPR into **[HWPR]** paragraphs, mark remaining content as **[AWOR]**, and expand where necessary.


Mode C: Review a Document

Trigger Conditions

User requests "review the document," "check HWPR formatting," etc.

Review Checklist

Check and report the following issues:

Check ItemIssue Description
Missing markersParagraph has no [HWPR] or [AWOR] marker
HWPR too longHWPR paragraph exceeds 5 sentences
HWPR contains AI styleHWPR has obvious AI-expansion artifacts (boilerplate, "in summary," etc.)
AWOR contains value judgmentsAWOR contains "we decided" / "gave up" etc. that should be HWPR content
Incorrect marker formatNot using the standard **[HWPR]** / **[AWOR]** format

Output format: List each issue + suggested fix.


Examples

Bad — HWPR too long, mixed with AI style

**[HWPR]** Background and Judgment
> After in-depth analysis of user behavior data and multi-dimensional competitive market research,
> our team discovered that the core problem lies in the new user onboarding experience not being smooth enough,
> which has led to a first-day retention rate of only 35%, significantly below the industry average of 50%.
> Based on the above analysis, we believe we should start by simplifying the onboarding flow,
> improving user experience through reducing step count and optimizing interaction design... (200 words)

Problem: HWPR is too long; contains AI boilerplate ("after in-depth analysis," "multi-dimensional," "significantly below").

Good — HWPR is concise, focused on judgments

**[HWPR]** Background
> New user first-day retention is 35%. I believe the main cause is onboarding being too complex (5 steps).
> Plan to simplify to 2 steps first, targeting 45% retention.

**[AWOR]** Detailed Analysis
User growth data over the past three quarters: Q1 retention 38%, Q2 35%, Q3 33%, showing a continuous decline.
Competitor comparison: Product A's onboarding has only 2 steps with 52% first-day retention...

Exemptions

ScenarioCondition
Pure record documentsMeeting minutes and other pure records without value judgments — HWPR may be omitted
Existing mature templatesWeekly reports and other documents with fixed formats — only add HWPR to "judgment/decision" sections

References

Capabilities

skillsource-addxaiskill-doc-writingtopic-agent-skillstopic-ai-agenttopic-ai-engineeringtopic-claude-codetopic-code-reviewtopic-cursortopic-devopstopic-enterprisetopic-sretopic-windsurf

Install

Installnpx skills add addxai/enterprise-harness-engineering
Transportskills-sh
Protocolskill

Quality

0.46/ 1.00

deterministic score 0.46 from registry signals: · indexed on github topic:agent-skills · 16 github stars · SKILL.md body (5,603 chars)

Provenance

Indexed fromgithub
Enriched2026-04-22 01:02:11Z · deterministic:skill-github:v1 · v1
First seen2026-04-21
Last seen2026-04-22

Agent access