diamitani/rostr-agent

PAL — Prompt Abstraction Layer

Production skill for compiling natural language requests into typed AgentManifest JSON.

About

PAL is a 5-stage compilation pipeline that transforms raw, casual user input into structured agent execution plans. It extracts intent, injects context, enhances prompts, and routes to the right LLM with optimal parameters.

Use this when:

• You need to abstract away from literal user requests to underlying intent
• You're building multi-agent systems and need consistent intent interpretation
• You want to route user input to different LLMs based on task type (Claude for reasoning, GPT for speed, Ollama locally)
• You need to inject context (corporate knowledge, user preferences, system constraints) before execution

Output: Typed AgentManifest JSON with:

• domain (code|design|content|sales|ops|idea|debug|deploy)
• model (claude-opus|gpt-4|ollama-local)
• temperature (0-1 for model config)
• tools (list of MCPs/skills to enable)
• behavior (execution style: auto-approve, ask, report-only)
• enhanced_prompt (context-injected, rewritten request)
• routing_reason (why this domain/model/tools)

Stages

1. Intent Extraction — Parse raw input for goal, domain, subject, constraints, output format
2. Context Injection — Pull relevant context (CLAUDE.md, user prefs, codebase, project memory)
3. Prompt Enhancement — Rewrite input with precision, break into sub-tasks, inject knowledge
4. Compilation — Generate typed AgentManifest with model/temperature/tools/behavior
5. Routing — Route to appropriate LLM + MCPs based on domain and complexity

Example

Input:

make a basketball site for my school

Output:

{
  "domain": "design",
  "model": "claude-opus",
  "temperature": 0.7,
  "tools": ["firecrawl-scrape", "design-html"],
  "behavior": "auto-approve",
  "enhanced_prompt": "Build a Next.js basketball site for a high school. Include: responsive roster page, schedule/scores, photo gallery, contact form. Use mobile-first design, school colors if mentioned, static generation for performance.",
  "routing_reason": "Design-heavy request mapped to Opus for multi-stage creative work; includes visual design + responsive layout"
}

Dependencies

• anthropic — Claude API
• openai — GPT routing
• requests — Web search MCP
• pydantic — Schema validation
• pyyaml — Config parsing

Files

• pal/SKILL.md — This file
• pal/main.py — Execution logic (5-stage pipeline)
• pal/schema.json — Input/output schemas
• pal/requirements.txt — Dependencies
• tests/test_pal.py — Test suite with 5+ cases