/skill model-capability-negotiation

{
  "model_id": "{identifier}",
  "capability_manifest": {
    "version": "1.0.0",
    "timestamp": "{iso_timestamp}",
    "capabilities": [
      {
        "name": "{capability_name}",
        "category": "{category}",
        "proficiency": 0.0-1.0,
        "confidence_interval": [0.0, 1.0],
        "benchmarks": ["{benchmark_results}"],
        "limitations": ["{known_limitations}"],
        "cost": {
          "latency_ms": 0,
          "token_cost": 0.0,
          "resource_intensity": "low|medium|high"
        }
      }
    ],
    "signature": "{cryptographic_signature}"
  }
}

<capability-negotiation>
  <session-id>NEG-{timestamp}</session-id>

  <phase name="discovery">
    <action>broadcast_capabilities</action>
    <response>capability_manifests</response>
  </phase>

  <phase name="matching">
    <task-requirements>{task_spec}</task-requirements>
    <candidate-models>
      <model id="{id}" match-score="{score}"/>
    </candidate-models>
  </phase>

  <phase name="bidding">
    <bids>
      <bid model="{id}">
        <proposed-role>{role}</proposed-role>
        <confidence>{confidence}</confidence>
        <cost-estimate>{cost}</cost-estimate>
      </bid>
    </bids>
  </phase>

  <phase name="allocation">
    <assignments>
      <assignment model="{id}" task="{task}" role="{role}"/>
    </assignments>
  </phase>
</capability-negotiation>

def match_capabilities(task_requirements, model_capabilities):
    scores = {}
    for model, caps in model_capabilities.items():
        score = 0
        for req in task_requirements:
            matching_cap = find_best_match(req, caps)
            if matching_cap:
                score += (
                    matching_cap.proficiency * req.weight *
                    (1 - matching_cap.cost.resource_intensity_factor)
                )
        scores[model] = score
    return sorted(scores.items(), key=lambda x: x[1], reverse=True)

Task: "Analyze code repository and generate documentation"

Requirements:
- Code understanding (weight: 1.0)
- Documentation writing (weight: 0.8)
- Technical accuracy (weight: 0.9)

Capability Discovery:
┌─────────┬────────────────┬─────────────┬────────────┐
│ Model   │ Code Analysis  │ Doc Writing │ Technical  │
├─────────┼────────────────┼─────────────┼────────────┤
│ Claude  │ 0.92           │ 0.88        │ 0.90       │
│ Gemini  │ 0.85           │ 0.82        │ 0.88       │
│ GPT     │ 0.88           │ 0.91        │ 0.85       │
└─────────┴────────────────┴─────────────┴────────────┘

Allocation Result:
- Claude: Lead (code analysis)
- GPT: Support (documentation writing)
- Gemini: Validator (technical review)