Agent-Skills.md

Agent Skills: Prompt Engineering Standards

Prompt engineering standards for AI interactions with cache optimization. Use when writing AI prompts, optimizing context usage, or structuring AGENTS.md files. Covers prompt caching, token efficiency, and progressive disclosure patterns.

UncategorizedID: tianjianjiang/smith/smith-prompts

Repository

tianjianjiang/smith
tianjianjiang

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pnpm dlx add-skill https://github.com/tianjianjiang/smith/tree/HEAD/smith-prompts

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smith-prompts/SKILL.md

Skill Metadata

Name
smith-prompts
Description
Prompt engineering standards for AI interactions with cache optimization. Use when writing AI prompts, optimizing context usage, or structuring AGENTS.md files. Covers prompt caching, token efficiency, and progressive disclosure patterns.

Prompt Engineering Standards

<metadata>
  • Load if: Writing AI prompts, optimizing context usage
  • Prerequisites: @smith-principles/SKILL.md
</metadata>

CRITICAL: Prompt Caching (Primacy Zone)

<required>

Cache reduces costs 90%, latency 85%

Structure for caching:

  1. Static content first (methodology, rules)
  2. Tool definitions in consistent order
  3. Project context (AGENTS.md, docs)
  4. Dynamic content last (recent changes)

Cache breakpoints: Every ~1024 tokens. Prefix must be identical for cache hit.

</required> <forbidden>
  • Reordering tools between calls
  • Injecting dynamic content into static sections
  • Modifying cached prefix unnecessarily
  • Using Markdown tables (see @smith-skills/SKILL.md - use bullet lists instead)
</forbidden>

AGENTS.md Cache-Friendly Structure

<!-- STATIC - cached -->
<metadata>

Scope, Load if, Prerequisites

</metadata>

<required>

Critical NEVER/ALWAYS rules

</required>

<forbidden>

Anti-patterns

</forbidden>

<!-- CACHE BREAKPOINT (~1024 tokens) -->

<!-- DYNAMIC - not cached -->
<examples>

Code examples that evolve

</examples>

Token Efficiency

Progressive Disclosure

Three-level loading:

  1. Metadata only (50 tokens)
  2. Core concepts when triggered (200 tokens)
  3. Full details when accessed (1000+ tokens)

Sparse Attention

<required>

Efficient file reading:

  1. Grep to find location
  2. Read with offset/limit for large files
  3. Read only necessary context (±20 lines)
</required> <forbidden>
  • Loading full files when targeted reads suffice
  • Reading documentation when metadata answers the question
  • Repeating user's question in responses
</forbidden>

Structured Output

Platform mechanisms:

  • OpenAI: JSON Schema with strict: true (100% compliance)
  • Anthropic: Tool use with flexible schemas
  • Gemini: responseSchema with retry
<required>

Schema design:

  • Match existing project patterns
  • Include descriptions for complex fields
  • Define required vs optional fields
  • Keep nesting ≤3 levels
</required> <related>
  • @smith-ctx/SKILL.md - Progressive disclosure, reference-based communication
  • @smith-xml/SKILL.md - Approved XML tags
</related>

ACTION (Recency Zone)

<required>

For caching:

  • Place static content before dynamic
  • Maintain consistent tool order
  • Target >80% cache hit rate

For efficiency:

  • Use Grep before Read
  • Read incrementally (narrow → expand)
  • Use file:line references
</required>