Git-Fg

architecting-prompts

"Applies 2026 Complexity-Based Guidance standards with Attention Management, Sycophancy Prevention, and XML/Markdown decision matrix. Provides theory, patterns, and quality evaluation criteria for AI prompt design. Use when designing, optimizing, or auditing AI prompts, system instructions, or multi-stage chains. Do not use for generating prompt files, basic conversational AI, or single-step interactions."

Git-Fg 1 Updated 4mo ago

Resources

2
GitHub

Install

npx skillscat add git-fg/thecattoolkit/architecting-prompts

Install via the SkillsCat registry.

SKILL.md

Prompt Architecture & Design Standards

Operational Protocol

  1. Analyze Intent: Determine if the goal is Drafting, Optimizing, or Auditing a prompt.
  2. Consult Standards: PROACTIVELY load references/core-standards.md for Attention Management rules.
  3. Select Pattern (Signal-to-Noise Rule):
    • Markdown-First (Default): Use for 80% of tasks.
    • Hybrid XML: Use ONLY if:
      • Data Isolation (>50 lines raw data)
      • Strict Constraints (NEVER/MUST rules)
      • Internal Monologue (Complex reasoning)
  4. Apply Theory: Use references/optimization.md for refinement workflows.
  5. Verify: Apply references/quality.md gates before final output.

Core Principles (Quick Reference)

Attention Management

Use Markdown headers for hierarchy. XML tags (Max 15, No Nesting) ONLY for semantic data isolation or thinking scaffolding.

Sycophancy Prevention (Truth-First)

If user suggests flawed path → CONTRADICT immediately. No "Great idea!" or superlatives. Speak in code, files, commands.

Signal-to-Noise Rule

  • Default: Markdown (80% of prompts) - fewer tokens, Claude-native
  • Upgrade to XML/Markdown hybrid only when:
    • Data Isolation: >50 lines of raw data
    • Constraint Weight: NEVER/MUST rules that cannot be broken
    • Internal Monologue: Complex reasoning requiring step-by-step

Knowledge Index (Progressive Disclosure)

Reference Purpose Load When
core-standards.md Attention, Sycophancy, Quota, XML/MD matrix ALWAYS consult first
design-patterns.md CoT, Few-Shot, Taxonomy, Structural patterns Selecting technique
optimization.md Systematic refinement workflow Improving existing prompts
quality.md Production quality gates Final verification
anti-patterns.md Common mistakes to avoid Prevention
taxonomy.md Single vs Chain vs Meta categorization Storage/planning
execution-protocol.md Standard completion reporting Structured output

Design Patterns

Approved Patterns

  • Chain of Thought (CoT)
  • Few-Shot Learning
  • Structured Output
  • Constraint Encoding

Success Criteria

A prompt meets 2026 standards when:

  • Uses Markdown headers for hierarchy (default)
  • XML tags are < 15 and never nested
  • Instructions are specific, actionable, and truth-focused
  • Examples (if any) are isolated in <example> tags
  • Reasoning is isolated in <thinking> blocks (if needed)
  • Quality gate checklist is included
  • Output format is clearly specified

Note: For generating .md prompt files for Claude-to-Claude pipelines, use generating-prompts skill.