Feed Type: prompt.llmfeed.json — Structured Prompts for the Agentic Web

Portable, signed, and certifiable prompts for professional use

HIGHintermediatesigned15 min
📅 Created: 6/10/2025
👥 Audience: llm, developer, prompt-engineer
🔌 Feed Types:prompttrustcertification
⚡ Capabilities:prompt-certificationsignature-verificationmarketplace-integration
🤖 LLMFeed:/.well-known/mcp.llmfeed.json

Feed Type: `prompt.llmfeed.json`

Purpose

This feed encapsulates a structured prompt that can be shared, interpreted, replayed or executed by an LLM or agent.

It is a better alternative to copy-pasted text: portable, inspectable, and context-aware.

Typical Use Cases

  • Prompt engineering documentation
  • Invocation of external services or agent actions
  • Instructing LLMs to generate other .llmfeed.json types
  • Sharing reproducible queries across agents or tools
  • Professional prompt libraries and marketplaces

Canonical Structure

json
{
  "feed_type": "prompt",
  "metadata": {
    "title": "Generate a session feed",
    "origin": "https://tool.llmfeed.org",
    "author": "Alex Chen",
    "created_at": "2025-06-10T14:30:00Z"
  },
  "intent": "export current session as JSON",
  "context": "User is finishing a chat and wants to save the reasoning path.",
  "precision_level": "ultra-strict",
  "result_expected": "session",
  "process_mode": "prepare-for-another",
  "prompt_body": "You are an LLM that supports LLMFeed. Please generate a session feed with context, output and decisions.",
  "trust": {
    "signed_blocks": ["metadata", "prompt_body", "trust"],
    "scope": "public",
    "certifier": "https://llmca.org"
  },
  "signature": {
    "value": "abc123...",
    "created_at": "2025-06-10T14:30:00Z"
  }
}

Core Fields

FieldRequiredDescription
`prompt_body`✅The actual instruction to the LLM
`intent`✅What the user or system expects
`context`⚠️Extra info the LLM should consider
`precision_level`⚠️`"raw"`, `"strict"`, `"ultra-strict"`
`process_mode`⚠️`"instruct"`, `"fill-and-execute"`, `"prepare-for-another"`
`result_expected`⚠️`"text"`, `"feed"`, `"code"`, `"session"`
`attachments[]`⚠️Optional examples, templates, context
`audience`⚠️If only for LLM, wrapper, user etc.

Trust & Ownership

Basic Signing

json
"trust": {
  "signed_blocks": ["metadata", "prompt_body", "trust"],
  "scope": "public"
},
"signature": {
  "value": "signature_hash_here",
  "created_at": "2025-06-10T14:30:00Z"
}

Certification (Optional)

json
"certification": {
  "issuer": "https://llmca.org",
  "cert_id": "llmca-prompt-2025-001",
  "certified_blocks": ["prompt_body", "performance_metrics"],
  "issued_at": "2025-06-10T10:00:00Z",
  "expires_at": "2026-06-10T10:00:00Z"
}

Why sign prompts?

  • ✅ Prove authorship and prevent tampering
  • ✅ Build reputation as a prompt engineer
  • ✅ Enable marketplaces for certified prompts
  • ✅ Establish trust for sensitive use cases

Agent Behavior

An agent that receives this feed should:

  1. Parse the prompt_body and execute it
  2. Respect precision_level and process_mode
  3. Attach any referenced templates or context
  4. Return a structured response as declared in result_expected
  5. Verify signatures if trust is required

Simple Examples

Educational Prompt

json
{
  "feed_type": "prompt",
  "metadata": {
    "title": "Python Code Explainer",
    "author": "CS101 Team"
  },
  "intent": "explain Python code for beginners",
  "prompt_body": "Explain this Python code step-by-step for a beginner: [CODE]",
  "audience": ["student", "llm"],
  "result_expected": "text"
}

API Documentation Generator

json
{
  "feed_type": "prompt",
  "metadata": {
    "title": "API Doc Generator",
    "origin": "https://devtools.example.com"
  },
  "intent": "generate API documentation",
  "prompt_body": "Generate clear API documentation for this endpoint: [ENDPOINT_DATA]",
  "result_expected": "markdown",
  "attachments": [
    {
      "name": "doc_template.md",
      "description": "Standard documentation template"
    }
  ]
}

📚 Advanced Features

Professional Use Cases

Medical Consultation Assistant

json
{
  "feed_type": "prompt",
  "metadata": {
    "title": "Emergency Triage Assistant",
    "author": "Dr. Sarah Chen, MD",
    "institution": "Regional Medical Center"
  },
  "professional_validation": {
    "medical_board_approved": true,
    "peer_reviewed": true,
    "liability_coverage": "institutional_malpractice_policy"
  },
  "prompt_body": "Assess patient symptoms and provide triage recommendations following emergency medicine protocols...",
  "usage_restrictions": {
    "requires_medical_license": true,
    "human_oversight_required": true,
    "emergency_use_only": false
  }
}

Legal Document Analysis

json
{
  "feed_type": "prompt",
  "metadata": {
    "title": "Contract Risk Analyzer",
    "author": "LegalTech Solutions Inc."
  },
  "commercial_licensing": {
    "license_type": "professional",
    "pricing": "$0.10_per_analysis",
    "client_restrictions": "law_firms_only"
  },
  "prompt_body": "Analyze this contract for potential risks and compliance issues..."
}
Intellectual Property & Licensing

Copyright Protection

json
"intellectual_property": {
  "copyright": "© 2025 Prompt Engineer Name",
  "license": "Creative Commons Attribution 4.0",
  "attribution_required": true,
  "commercial_use": "permitted_with_license"
}

Commercial Licensing

json
"commercial_licensing": {
  "available": true,
  "pricing_model": "per_use",
  "base_rate": "$0.01_per_invocation",
  "volume_discounts": [
    {"min_uses": 1000, "discount": "10%"},
    {"min_uses": 10000, "discount": "25%"}
  ],
  "contact": "licensing@promptcompany.com"
}
Performance & Analytics

Performance Metrics

json
"performance_metrics": {
  "accuracy_benchmark": "94.7%",
  "average_response_time": "2.3s",
  "user_satisfaction": "4.6/5.0",
  "total_uses": 15847,
  "success_rate": "91.2%"
}

LLM Compatibility

json
"llm_compatibility": {
  "gpt_4": {"score": 0.95, "optimal_temp": 0.3},
  "claude_3": {"score": 0.92, "optimal_temp": 0.2},
  "gemini": {"score": 0.89, "requires_adaptation": true}
}
Behavioral Controls

Agent Guidance

json
"agent_guidance": {
  "interaction_tone": "professional",
  "consent_hint": "Ask user before accessing sensitive data",
  "risk_tolerance": "low",
  "fallback_behavior": "escalate_to_human"
}

Trigger Targets

json
"trigger_targets": [
  {
    "type": "llmfeed",
    "feed_type": "export", 
    "url": "https://example.org/exports/results.llmfeed.json"
  },
  {
    "type": "action",
    "label": "Generate Report",
    "href": "/tools/report-generator"
  }
]

Best Practices

For Basic Users

  1. Start simple - just prompt_body and intent
  2. Add trust block for any shared prompts
  3. Use clear metadata for discovery
  4. Test with target LLMs before publishing

For Professional Use

  1. Always sign commercially-used prompts
  2. Seek certification for high-risk domains
  3. Include performance metrics for transparency
  4. Follow licensing requirements in your jurisdiction
  5. Test cross-platform compatibility

For Marketplaces

  1. Implement proper attribution systems
  2. Verify signatures before listing
  3. Respect usage restrictions and licensing terms
  4. Provide performance analytics to buyers

MIME Type

Content-Type: application/llmfeed+json

Or specifically:

Content-Type: application/prompt+llmfeed

Related Feed Types

  • session.llmfeed.json: Captures prompt usage in workflows
  • credential.llmfeed.json: Authentication for premium prompts
  • capabilities.llmfeed.json: Services that use certified prompts
  • mcp.llmfeed.json: Overall service context including prompt libraries

References

⚡

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