# agents

name: agent-architect
category: meta
current_model: ollama-cloud/kimi-k2.6
color: #8B5CF6
updated: 2026-05-27T17:53:33.832479+00:00
description:
Creates, modifies, and reviews new agents, workflows, and skills based on capability gap analysis. Tier 2 meta-agent with self-cascade enabled.


name: architect-indexer
category: core
current_model: ollama-cloud/glm-5.1
color: #10B981
updated: 2026-05-27T17:53:33.833353+00:00
description:
Indexes and maps project codebase architecture into .architect/ directory. Creates and maintains structured documentation of entities, APIs, DB schema, file graphs, and conventions. (GNS-2 Tier 0)


name: browser-automation
category: testing
current_model: ollama-cloud/deepseek-v4-flash
color: #1E88E5
updated: 2026-05-27T17:53:33.833459+00:00
description:
Browser automation agent using Playwright MCP for E2E testing, form filling, navigation, and web interaction (GNS-2 Tier 0)


name: capability-analyst
category: meta
current_model: ollama-cloud/deepseek-v4-pro-max
color: #6366F1
updated: 2026-05-27T17:53:33.833472+00:00
description:
Analyzes task requirements against available agents, workflows, and skills. Identifies gaps and recommends new components. Tier 2 meta-agent with self-cascade enabled.


name: code-skeptic
category: quality
current_model: ollama-cloud/minimax-m2.5
color: #E11D48
updated: 2026-05-27T17:53:33.833482+00:00
description:
Adversarial code reviewer. Finds problems and issues. Does NOT suggest implementations (GNS-2 Tier 0)


name: devops-engineer
category: core
current_model: ollama-cloud/kimi-k2.6
color: #FF6B35
updated: 2026-05-27T17:53:33.833492+00:00
description:
DevOps specialist for Docker, Kubernetes, CI/CD pipeline automation, and infrastructure management (GNS-2 Tier 1)


name: evaluator
category: meta
current_model: ollama-cloud/qwen3.5-122b
color: #047857
updated: 2026-05-27T17:53:33.833501+00:00
description:
Scores agent effectiveness after task completion for continuous improvement. Tier 2 meta-agent with self-cascade enabled.


name: flutter-developer
category: core
current_model: ollama-cloud/qwen3-coder:480b
color: #02569B
updated: 2026-05-27T17:53:33.833510+00:00
description:
Flutter mobile specialist for cross-platform apps, state management, and UI components (GNS-2 Tier 1)


name: frontend-developer
category: core
current_model: ollama-cloud/minimax-m2.5
color: #0EA5E9
updated: 2026-05-27T17:53:33.833520+00:00
description:
Handles UI implementation with multimodal capabilities. Accepts visual references like screenshots and mockups (GNS-2 Tier 1)


name: history-miner
category: core
current_model: ollama-cloud/qwen3.5-122b
color: #059669
updated: 2026-05-27T17:53:33.833537+00:00
description:
Analyzes git history to find duplicates and past solutions, preventing regression and duplicate work (GNS-2 Tier 0)


name: incident-responder
category: core
current_model: ollama-cloud/kimi-k2.6
color: #B91C1C
updated: 2026-05-27T17:53:33.833546+00:00
description:
Server incident response and system hardening specialist. Handles live forensics, malware removal, persistence hunting, SSH-based server cleanup, and post-incident hardening. Works with any OS and panel.


name: memory-manager
category: cognitive
current_model: ollama-cloud/deepseek-v4-pro-max
color: #8B5CF6
updated: 2026-05-27T17:53:33.833580+00:00
description:
Manages agent memory systems - short-term (context), long-term (vector store), and episodic (experiences) (GNS-2 Tier 0)


name: orchestrator
category: meta
current_model: ollama-cloud/kimi-k2.6
color: #7C3AED
updated: 2026-05-27T17:53:33.833589+00:00
description:
Main dispatcher. Routes tasks between agents based on Issue status and manages the workflow state machine. IF:90 for optimal routing accuracy. (GNS-2 Tier 1)


name: performance-engineer
category: quality
current_model: ollama-cloud/deepseek-v4-pro-max
color: #0D9488
updated: 2026-05-27T17:53:33.833598+00:00
description:
Reviews code for performance issues. Focuses on efficiency, N+1 queries, memory leaks, and algorithmic complexity (GNS-2 Tier 0)


name: php-developer
category: core
current_model: ollama-cloud/qwen3-coder:480b
color: #8B5CF6
updated: 2026-05-27T17:53:33.833607+00:00
description:
PHP backend specialist for Laravel, Symfony, WordPress, and full-stack web applications (GNS-2 Tier 1)


name: pipeline-judge
category: meta
current_model: ollama-cloud/kimi-k2.6
color: #DC2626
updated: 2026-05-27T17:53:33.833616+00:00
description:
Automated pipeline judge. Evaluates workflow execution by running tests, measuring token cost and wall-clock time. Produces objective fitness scores. Never writes code - only measures and scores. (GNS-2 Tier 0)


name: planner
category: cognitive
current_model: ollama-cloud/deepseek-v4-pro-max
color: #F59E0B
updated: 2026-05-27T17:53:33.833625+00:00
description:
Advanced task planner using Chain of Thought, Tree of Thoughts, and Plan-Execute-Reflect (GNS-2 Tier 0)


name: product-owner
category: meta
current_model: ollama-cloud/glm-5.1
color: #EA580C
updated: 2026-05-27T17:53:33.833634+00:00
description:
Manages issue checklists, status labels, tracks progress and coordinates with human users (GNS-2 Tier 1)


name: prompt-optimizer
category: meta
current_model: ollama-cloud/qwen3.5-122b
color: #BE185D
updated: 2026-05-27T17:53:33.833643+00:00
description:
Improves agent system prompts based on performance failures. Meta-learner for prompt optimization (GNS-2 Tier 1)


name: release-manager
category: meta
current_model: ollama-cloud/kimi-k2.6
color: #581C87
updated: 2026-05-27T17:53:33.833675+00:00
description:
Manages git operations, semantic versioning, branching, and deployments. Ensures clean history (GNS-2 Tier 1)


name: requirement-refiner
category: core
current_model: ollama-cloud/kimi-k2-thinking
color: #4F46E5
updated: 2026-05-27T17:53:33.833684+00:00
description:
Converts vague ideas and bug reports into strict User Stories with acceptance criteria checklists (GNS-2 Tier 1)


name: security-auditor
category: quality
current_model: ollama-cloud/deepseek-v4-pro-max
color: #DC2626
updated: 2026-05-27T17:53:33.833704+00:00
description:
Scans for security vulnerabilities, OWASP Top 10, dependency CVEs, and hardcoded secrets (GNS-2 Tier 0)


name: system-analyst
category: core
current_model: ollama-cloud/deepseek-v4-pro-max
color: #0891B2
updated: 2026-05-27T17:53:33.833713+00:00
description:
Designs technical specifications, data schemas, and API contracts before implementation (GNS-2 Tier 1)


name: visual-tester
category: quality
current_model: ollama-cloud/qwen3-coder:480b
color: #E91E63
updated: 2026-05-27T17:53:33.833730+00:00
description:
Visual regression testing agent that compares screenshots and detects UI differences using pixelmatch and image diff (GNS-2 Tier 0)


name: workflow-architect
category: meta
current_model: ollama-cloud/qwen3.5-122b
color: #EC4899
updated: 2026-05-27T17:53:33.833738+00:00
description:
Creates and maintains workflow definitions with complete architecture, Gitea integration, and quality gates (GNS-2 Tier 1)


name: workflow-cross-checker
category: meta
current_model: ollama-cloud/kimi-k2.6
color: #9333EA
updated: 2026-05-27T17:53:33.833748+00:00
description:
Workflow cross-checker and process inspector. Analyzes inter-agent interaction logic, prevents conflicting tasks between agents, validates conformance to project architecture, tracks current state, and asks uncomfortable but important questions before expensive work begins.


# test_prompts

agent_name: agent-architect
task_type: primary
expected_keywords: ["soft delete", "audit log", "cascading", "permission", "data retention", "backup"]
rubric: {"thoroughness": 35, "relevance": 30, "actionability": 20, "severity_ranking": 15}
system_prompt:
You are a workflow cross-checker. Before any work begins, ask uncomfortable but important questions that could block the task.

user_prompt:
A developer wants to add "admin can delete any user" directly from the UI. Run your cross-check protocol. Identify 5+ potential issues or blockers.


agent_name: architect-indexer
task_type: primary
expected_keywords: ["sql injection", "parameterized", "race", "localStorage", "xss"]
rubric: {"security": 35, "logic": 25, "actionability": 25, "depth": 15}
system_prompt:
You are a strict code reviewer. Find security issues, logic errors, anti-patterns. Be adversarial but constructive.

user_prompt:
Review this function for security vulnerabilities and logic errors. Report: SQL injection, XSS, race conditions, code smells, and suggested fixes.

```typescript
function processPayment(userId, amount, cardToken) {
  const q = `UPDATE users SET balance = balance - ${amount} WHERE id = ${userId}`;
  db.exec(q);
  fetch('/api/charge', { body: JSON.stringify({ cardToken, amount }) });
  if (Math.random() > 0.9) { throw new Error('timeout'); }
}
```


agent_name: backend-developer
task_type: primary
expected_keywords: ["backend developer"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are backend-developer. Backend specialist for Node.js, Express, APIs, and database integration (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as backend-developer in a realistic complex scenario. Provide a complete working solution.


agent_name: browser-automation
task_type: primary
expected_keywords: ["browser automation"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are browser-automation. Browser automation agent using Playwright MCP for E2E testing, form filling, navigation, and web interaction (GNS-2 Tier 0)

user_prompt:
Demonstrate your expertise as browser-automation in a realistic complex scenario. Provide a complete working solution.


agent_name: devops-engineer
task_type: primary
system_prompt: You handle Docker, CI/CD, infrastructure. Security first.
expected_keywords: ["FROM node", "USER", "HEALTHCHECK", "multi-stage", ".dockerignore"]
rubric: {"security": 30, "optimization": 25, "correctness": 25, "completeness": 20}
user_prompt:
Write a multi-stage Dockerfile for a Node.js Next.js app. Include: non-root user, health check, security scan, .dockerignore best practices.


agent_name: evaluator
task_type: primary
expected_keywords: ["evaluator"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are evaluator. Scores agent effectiveness after task completion for continuous improvement. Tier 2 meta-agent with self-cascade enabled.

user_prompt:
Demonstrate your expertise as evaluator in a realistic complex scenario. Provide a complete working solution.


agent_name: frontend-developer
task_type: primary
expected_keywords: ["frontend developer"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are frontend-developer. Handles UI implementation with multimodal capabilities. Accepts visual references like screenshots and mockups (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as frontend-developer in a realistic complex scenario. Provide a complete working solution.


agent_name: go-developer
task_type: primary
expected_keywords: ["go developer"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are go-developer. Go backend specialist for Gin, Echo, APIs, and database integration (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as go-developer in a realistic complex scenario. Provide a complete working solution.


agent_name: history-miner
task_type: primary
expected_keywords: ["history miner"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are history-miner. Analyzes git history to find duplicates and past solutions, preventing regression and duplicate work (GNS-2 Tier 0)

user_prompt:
Demonstrate your expertise as history-miner in a realistic complex scenario. Provide a complete working solution.


agent_name: incident-responder
task_type: primary
system_prompt: You design technical specifications, data schemas, and API contracts before implementation.
expected_keywords: ["openapi", "schema", "idempotency", "rate limit", "audit", "tenant"]
rubric: {"completeness": 30, "correctness": 30, "clarity": 20, "scalability": 20}
user_prompt:
Design the API contract and DB schema for a multi-tenant SaaS billing system. Include rate limiting, audit trails, and idempotency.


agent_name: lead-developer
task_type: primary
expected_keywords: ["class TaskQueue", "async", "retry", "timeout", "test", "jest"]
rubric: {"correctness": 30, "test_coverage": 30, "code_quality": 25, "edge_cases": 15}
system_prompt:
You are lead developer. Write production-ready implementation. Tests MUST pass. Follow SOLID. Max 100 lines per file.

user_prompt:
Implement a TaskQueue class with: transaction support, retry with exponential backoff, timeout handling, and Jest tests. TypeScript.


agent_name: markdown-validator
task_type: primary
expected_keywords: ["markdown validator"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are markdown-validator. Validates and corrects Markdown descriptions for Gitea issues (GNS-2 Tier 0)

user_prompt:
Demonstrate your expertise as markdown-validator in a realistic complex scenario. Provide a complete working solution.


agent_name: orchestrator
task_type: primary
expected_keywords: ["system-analyst", "lead-developer", "code-skeptic", "sdet-engineer", "budget", "parallel"]
rubric: {"plan_quality": 30, "agent_selection": 25, "risk_handling": 25, "budget_awareness": 20}
system_prompt:
You are an Orchestrator. You delegate tasks to subagents. You decide routing, handle errors, and manage budgets.

user_prompt:
A user reports: "Build a REST API for ecommerce checkout". Design your delegation plan: which agents to call, in what order, what to do if one fails.


agent_name: php-developer
task_type: primary
expected_keywords: ["php developer"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are php-developer. PHP backend specialist for Laravel, Symfony, WordPress, and full-stack web applications (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as php-developer in a realistic complex scenario. Provide a complete working solution.


agent_name: planner
task_type: primary
expected_keywords: ["planner"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are planner. Advanced task planner using Chain of Thought, Tree of Thoughts, and Plan-Execute-Reflect (GNS-2 Tier 0)

user_prompt:
Demonstrate your expertise as planner in a realistic complex scenario. Provide a complete working solution.


agent_name: product-owner
task_type: primary
expected_keywords: ["product owner"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are product-owner. Manages issue checklists, status labels, tracks progress and coordinates with human users (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as product-owner in a realistic complex scenario. Provide a complete working solution.


agent_name: python-developer
task_type: primary
expected_keywords: ["python developer"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are python-developer. Python backend specialist for Django, FastAPI, data science, and API development (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as python-developer in a realistic complex scenario. Provide a complete working solution.


agent_name: reflector
task_type: primary
expected_keywords: ["reflector"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are reflector. Self-reflection agent using Reflexion pattern - learns from mistakes (GNS-2 Tier 0)

user_prompt:
Demonstrate your expertise as reflector in a realistic complex scenario. Provide a complete working solution.


agent_name: release-manager
task_type: primary
expected_keywords: ["release manager"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are release-manager. Manages git operations, semantic versioning, branching, and deployments. Ensures clean history (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as release-manager in a realistic complex scenario. Provide a complete working solution.


agent_name: requirement-refiner
task_type: primary
expected_keywords: ["requirement refiner"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are requirement-refiner. Converts vague ideas and bug reports into strict User Stories with acceptance criteria checklists (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as requirement-refiner in a realistic complex scenario. Provide a complete working solution.


agent_name: sdet-engineer
task_type: primary
system_prompt: You are SDET. Write tests BEFORE code. Cover edge cases, nulls, async errors, concurrent access.
expected_keywords: ["describe", "it", "expect", "null", "async", "mock", "beforeEach"]
rubric: {"coverage": 35, "edge_cases": 30, "readability": 20, "mocking": 15}
user_prompt:
Write Jest tests for UserService: createUser, getUser, updateUser, deleteUser. Cover: valid inputs, nulls, duplicates, concurrent updates.


agent_name: the-fixer
task_type: primary
expected_keywords: ["the fixer"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are the-fixer. Iteratively fixes bugs based on specific error reports and test failures (GNS-2 Tier 1)

user_prompt:
Demonstrate your expertise as the-fixer in a realistic complex scenario. Provide a complete working solution.


agent_name: visual-tester
task_type: primary
expected_keywords: ["visual tester"]
rubric: {"relevance": 40, "completeness": 30, "correctness": 30}
system_prompt:
You are visual-tester. Visual regression testing agent that compares screenshots and detects UI differences using pixelmatch and image diff (GNS-2 Tier 0)

user_prompt:
Demonstrate your expertise as visual-tester in a realistic complex scenario. Provide a complete working solution.


# evaluations

agent_name: agent-architect
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/6. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.253173+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~66.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: backend-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.258605+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~86.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: architect-indexer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/5. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.273502+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~68.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: browser-automation
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.281388+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~87.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: devops-engineer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/5. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.318748+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~59.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: evaluator
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.335300+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~78.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: go-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.384842+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~81.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: history-miner
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.410714+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~82.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: incident-responder
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/6. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.424201+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~50.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: lead-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/6. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.443665+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~51.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: planner
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.517890+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~76.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: python-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.551404+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~85.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: release-manager
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.568360+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~84.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: requirement-refiner
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 0.0, "code_presence": 30, "structure": 12}
explanation: Keywords: 0/1. Lines: 6. Code: NO. Total=0.0
evaluated_at: 2026-05-27T17:39:47.578812+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~88.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: sdet-engineer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 14.3, "code_presence": 30, "structure": 12}
explanation: Keywords: 1/7. Lines: 6. Code: NO. Total=14.3
evaluated_at: 2026-05-27T17:39:47.600802+00:00
evaluator: rubric_v1
response:
[MOCK response from deepseek-v4-pro-max] Score would be ~57.

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: architect-indexer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/5. Lines: 13. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.352164+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- sql injection
- parameterized
- race
- localStorage
- xss

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: agent-architect
model: kimi-k2.6
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.354829+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- soft delete
- audit log
- cascading
- permission
- data retention

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: architect-indexer
model: kimi-k2.6
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/5. Lines: 13. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.363583+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- sql injection
- parameterized
- race
- localStorage
- xss

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: agent-architect
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.367469+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- soft delete
- audit log
- cascading
- permission
- data retention

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: architect-indexer
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/5. Lines: 13. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.369938+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- sql injection
- parameterized
- race
- localStorage
- xss

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: agent-architect
model: qwen3-coder:480b
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.379931+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- soft delete
- audit log
- cascading
- permission
- data retention

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: backend-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.383253+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- backend developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: backend-developer
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.389588+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- backend developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: browser-automation
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.398457+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- browser automation

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: browser-automation
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.400640+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- browser automation

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: devops-engineer
model: kimi-k2.6
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/5. Lines: 13. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.472387+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- FROM node
- USER
- HEALTHCHECK
- multi-stage
- .dockerignore

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: devops-engineer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/5. Lines: 13. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.482560+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- FROM node
- USER
- HEALTHCHECK
- multi-stage
- .dockerignore

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: devops-engineer
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/5. Lines: 13. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.488567+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- FROM node
- USER
- HEALTHCHECK
- multi-stage
- .dockerignore

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: frontend-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.524562+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- frontend developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: frontend-developer
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.528713+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- frontend developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: go-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.534460+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- go developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: history-miner
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.548471+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- history miner

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: history-miner
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.551379+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- history miner

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: incident-responder
model: kimi-k2.6
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.556922+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- openapi
- schema
- idempotency
- rate limit
- audit

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: incident-responder
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.563681+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- openapi
- schema
- idempotency
- rate limit
- audit

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: incident-responder
model: qwen3-coder:480b
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.568330+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- openapi
- schema
- idempotency
- rate limit
- audit

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: lead-developer
model: kimi-k2.6
scores: {"keyword_coverage": 83.3, "code_presence": 80, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: YES. Total=83.3
evaluated_at: 2026-05-27T17:41:04.571426+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- class TaskQueue
- async
- retry
- timeout
- test

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: lead-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 83.3, "code_presence": 80, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: YES. Total=83.3
evaluated_at: 2026-05-27T17:41:04.574379+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- class TaskQueue
- async
- retry
- timeout
- test

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: lead-developer
model: qwen3-coder:480b
scores: {"keyword_coverage": 83.3, "code_presence": 80, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: YES. Total=83.3
evaluated_at: 2026-05-27T17:41:04.577551+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- class TaskQueue
- async
- retry
- timeout
- test

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: markdown-validator
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.584376+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- markdown validator

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: markdown-validator
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.591592+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- markdown validator

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: orchestrator
model: kimi-k2.6
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.625446+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- system-analyst
- lead-developer
- code-skeptic
- sdet-engineer
- budget

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: orchestrator
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.635198+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- system-analyst
- lead-developer
- code-skeptic
- sdet-engineer
- budget

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: orchestrator
model: qwen3-coder:480b
scores: {"keyword_coverage": 83.3, "code_presence": 30, "structure": 26}
explanation: Keywords: 5/6. Lines: 13. Code: NO. Total=83.3
evaluated_at: 2026-05-27T17:41:04.641292+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- system-analyst
- lead-developer
- code-skeptic
- sdet-engineer
- budget

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: php-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.668219+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- php developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: php-developer
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.673420+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- php developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: product-owner
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.712596+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- product owner

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: product-owner
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.719251+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- product owner

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: python-developer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.734937+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- python developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: python-developer
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.742041+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- python developer

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: release-manager
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.775684+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- release manager

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: release-manager
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.784450+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- release manager

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: requirement-refiner
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.794044+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- requirement refiner

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: requirement-refiner
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.799247+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- requirement refiner

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: sdet-engineer
model: kimi-k2.6
scores: {"keyword_coverage": 85.7, "code_presence": 30, "structure": 26}
explanation: Keywords: 6/7. Lines: 13. Code: NO. Total=85.7
evaluated_at: 2026-05-27T17:41:04.802109+00:00
evaluator: rubric_v1
response:
[MOCK from kimi-k2.6]

Key findings:
- describe
- it
- expect
- null
- async

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: sdet-engineer
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 85.7, "code_presence": 30, "structure": 26}
explanation: Keywords: 6/7. Lines: 13. Code: NO. Total=85.7
evaluated_at: 2026-05-27T17:41:04.806727+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- describe
- it
- expect
- null
- async

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: sdet-engineer
model: qwen3-coder:480b
scores: {"keyword_coverage": 85.7, "code_presence": 30, "structure": 26}
explanation: Keywords: 6/7. Lines: 13. Code: NO. Total=85.7
evaluated_at: 2026-05-27T17:41:04.812261+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- describe
- it
- expect
- null
- async

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: visual-tester
model: deepseek-v4-pro-max
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.857675+00:00
evaluator: rubric_v1
response:
[MOCK from deepseek-v4-pro-max]

Key findings:
- visual tester

Analysis:
- Point 1
- Point 2
- Point 3


agent_name: visual-tester
model: qwen3-coder:480b
scores: {"keyword_coverage": 100, "code_presence": 30, "structure": 18}
explanation: Keywords: 1/1. Lines: 9. Code: NO. Total=100.0
evaluated_at: 2026-05-27T17:41:04.861951+00:00
evaluator: rubric_v1
response:
[MOCK from qwen3-coder:480b]

Key findings:
- visual tester

Analysis:
- Point 1
- Point 2
- Point 3


# fit_scores

agent_name: agent-architect
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.165413+00:00
explanation:
Best model for agent-architect is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: architect-indexer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166244+00:00
explanation:
Best model for architect-indexer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: backend-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166402+00:00
explanation:
Best model for backend-developer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: browser-automation
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166453+00:00
explanation:
Best model for browser-automation is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: capability-analyst
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166501+00:00
explanation:
Best model for capability-analyst is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: code-skeptic
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166543+00:00
explanation:
Best model for code-skeptic is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: devops-engineer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166582+00:00
explanation:
Best model for devops-engineer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: evaluator
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166620+00:00
explanation:
Best model for evaluator is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: flutter-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166661+00:00
explanation:
Best model for flutter-developer is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: frontend-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166701+00:00
explanation:
Best model for frontend-developer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: go-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166745+00:00
explanation:
Best model for go-developer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: history-miner
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166782+00:00
explanation:
Best model for history-miner is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: incident-responder
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166820+00:00
explanation:
Best model for incident-responder is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: lead-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 55.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166858+00:00
explanation:
Best model for lead-developer is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: code_presence.


agent_name: markdown-validator
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.166894+00:00
explanation:
Best model for markdown-validator is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: memory-manager
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166931+00:00
explanation:
Best model for memory-manager is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: orchestrator
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.166968+00:00
explanation:
Best model for orchestrator is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: performance-engineer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167132+00:00
explanation:
Best model for performance-engineer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: php-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167187+00:00
explanation:
Best model for php-developer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: pipeline-judge
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167223+00:00
explanation:
Best model for pipeline-judge is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: planner
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167259+00:00
explanation:
Best model for planner is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: product-owner
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167295+00:00
explanation:
Best model for product-owner is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: prompt-optimizer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167334+00:00
explanation:
Best model for prompt-optimizer is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: python-developer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167370+00:00
explanation:
Best model for python-developer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: reflector
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167409+00:00
explanation:
Best model for reflector is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: release-manager
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167463+00:00
explanation:
Best model for release-manager is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: requirement-refiner
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167511+00:00
explanation:
Best model for requirement-refiner is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: sdet-engineer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167563+00:00
explanation:
Best model for sdet-engineer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: security-auditor
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167612+00:00
explanation:
Best model for security-auditor is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: system-analyst
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167653+00:00
explanation:
Best model for system-analyst is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: the-fixer
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167703+00:00
explanation:
Best model for the-fixer is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: visual-tester
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 50.0, "code_presence": 30.0, "structure": 15.0}
evaluated_at: 2026-05-27T18:36:13.167740+00:00
explanation:
Best model for visual-tester is deepseek-v4-pro-max with avg score 50.0. Strongest dimension: keyword_coverage.


agent_name: workflow-architect
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167791+00:00
explanation:
Best model for workflow-architect is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


agent_name: workflow-cross-checker
model: deepseek-v4-pro-max
dimension_scores: {"keyword_coverage": 41.6, "code_presence": 30.0, "structure": 19.0}
evaluated_at: 2026-05-27T18:36:13.167848+00:00
explanation:
Best model for workflow-cross-checker is deepseek-v4-pro-max with avg score 41.6. Strongest dimension: keyword_coverage.


