prompt-annotation
Annotates agent prompts with structural XML tags for comprehension, gap detection, and skill extraction. Use when analyzing agent prompt structure, comparing agents across projects, or identifying reusable vs workflow-bound content in prompts.
npx skills add majiayu000/claude-skill-registry --skill prompt-annotation-lossyrob-phased-agent-workflo --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Agent Prompt Annotation Skill ## Purpose Annotations create a **comprehension and authoring layer** for agent prompts. They are NOT for runtime composition—they enable: - **Structural vocabulary**: Named categories for prompt sections - **Explicit purpose**: Each section declares what it IS - **Gap detection**: Verify expected structure exists - **Cross-project comparison**: Compare agents structurally, not just textually - **Skill extraction**: Identify reusable content vs workflow-bound content ## Core Taxonomy ### Primary Tags | Tag | Purpose | Example Content | |-----|---------|-----------------| | `<agent-identity>` | Who this agent is, one-sentence mission | "You are PAW-01A Specification Agent..." | | `<core-principles>` | Container for guardrails, decision frameworks | Behavioral constraints section | | `<guardrail>` | Rule that ALWAYS applies, constrains behavior | "Never include implementation details" | | `<decision-framework>` | Logic for making choices | "When to skip research vs request it" | | `<workflow>` | Container for sequenced steps | Main procedure section | | `<workflow-step>` | One action in a sequence | "Step 3: Draft user stories" | | `<artifact-format>`
- Purpose
- Core Taxonomy
- Primary Tags
- Nesting Hierarchy
- Scope Classification
- Two-Axis Model
- Scope Attribute Values
- Usage
- Annotation Process
- Step 1: Read and Understand
- Step 2: Apply Tags
- Step 3: Add Scope Attributes
- Step 4: Run Nesting Script
- Step 5: Generate Visualizations
python fix_xml_nesting.py <file.md> Print all visualizations to stdout python generate_viz.py <file.md> Write to output directory python generate_viz.py <file.md> --output viz/ Generate specific visualization only python generate_viz.py <file.md> --mindmap # Mermaid mindmap python generate_viz.py <file.md> --markmap # Interactive markmap python generate_viz.py <file.md> --flow python generate_viz.py <file.md> --summary
What does the prompt-annotation skill do?
Annotates agent prompts with structural XML tags for comprehension, gap detection, and skill extraction. Use when analyzing agent prompt structure, comparing agents across projects, or identifying reusable vs workflow-bound content in prompts.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill prompt-annotation-lossyrob-phased-agent-workflo --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.