cclohmar/OpenWriterControle
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 1
OpenWriterControle/.opencode/agent/subagents/utils/image-specialist.md
Darren Hinde c8f7103cb6
refactor(evals): consolidate documentation and enhance test infrastructure (#56) 
* feat(agents): implement category-based agent organization system

- Organize agents into domain categories (core, development, content, data, product, learning)
- Move 10 agents to category subdirectories with proper git rename tracking
- Update 13 subagents with category and type metadata in frontmatter
- Add category metadata files (0-category.json) documenting common patterns
- Implement local registry fallback in install script for offline development
- Add comprehensive validation suite with 15 automated tests (100% pass rate)
- Enhance registry validation with duplicate ID and consistency checks
- Update eval framework with intelligent path resolution (backward compatible)
- Archive legacy eval structure to _archive/ for reference
- Update all documentation to reflect category-based structure
- Bump version to 0.5.0 with accurate CHANGELOG

Technical Details:
- 23 agents organized (10 category agents, 13 subagents)
- 6 category directories created
- Path resolution supports both agent IDs and category paths
- Registry schema updated to v2.0.0
- 159 files changed, 872 insertions(+), 10738 deletions(-)

BREAKING CHANGE: Agent file paths now use category structure. Update references from .opencode/agent/openagent.md to .opencode/agent/core/openagent.md. Eval framework maintains backward compatibility via path resolution.

* refactor(evals): consolidate documentation and enhance test infrastructure

- Remove temporary project tracking files (PHASE_5_COMPLETE, PROJECT_COMPLETE, etc.)
- Consolidate evaluation framework docs into main README
- Enhance test execution with improved logging and multi-prompt support
- Move system-builder from core to meta category
- Add comprehensive test suites for openagent with organized structure
- Create evaluation test structure for all subagents
- Clean up archived workflows and redundant documentation
- Update registry to reflect new agent organization
- Add shared test templates and golden test patterns

* feat(ci): add manual trigger support for bot-created PRs in validate-registry workflow

- Add pr_number input to workflow_dispatch for manually triggering validation
- Fetch PR details dynamically when triggered manually
- Support both automatic (PR event) and manual (workflow_dispatch) triggers
- Enable validation of bot-created PRs like automated version bumps
- Update branch detection and push logic to handle both trigger types
- Add documentation explaining how to manually trigger for bot PRs

* feat(evals): add explicit context file validation to test framework

- Add expectedContextFiles field to test YAML schema for explicit context file specification
- Enhance context-loading-evaluator to support both auto-detect and explicit validation modes
- Update documentation with comprehensive guide and examples
- Clean up archived legacy test structure (90+ old test files)
- Add new example tests demonstrating explicit context validation
- Backward compatible with existing tests

* feat(evals): add multi-agent logging system and performance optimizations

Implement comprehensive multi-agent logging and performance improvements for the eval framework.

Task 01: Multi-Agent Logging System
- Add complete hierarchical logging module (evals/framework/src/logging/)
  - SessionTracker: tracks parent-child delegation hierarchies
  - MultiAgentLogger: pretty-prints logs with visual indentation
  - Formatters: box characters and emoji formatting
  - 37 passing unit tests (session-tracker, logger, integration)
- Integrate with SDK event stream handler
  - Hook into session.created, message.updated, message.part.updated events
  - Real-time child session detection via timestamp heuristics
  - Message deduplication for cleaner output
- Enable in debug mode only (<1% performance overhead)
- Add demo script and comprehensive documentation

Task 02: Performance Optimizations
- Reduce grace period from 5s to 2s (67% reduction, 10-20% faster tests)
- Add PerformanceMetricsEvaluator for bottleneck identification
  - Collects tool latencies, inference time, idle time
  - Provides full performance visibility
- Update smoke test to validate delegation (core feature)
  - Replace simple read test with multi-agent delegation test
  - Tests both parent and child agent functionality
  - Validates multi-agent logging system

Results:
- 37 unit tests passing
- Smoke test passing (95/100 score)
- Clean hierarchical logging output
- 10-20% faster test execution
- Full multi-agent visibility

Files changed: 16 files, +2678 lines
Time saved: ~6-8 days (completed in 6 hours vs 3-5 day estimate)

* feat(evals): show child agent execution in non-debug mode

- Enable MultiAgentLogger in both debug and non-debug modes
- Add verbose parameter to control output level
- Non-verbose mode shows concise child session lifecycle:
  - Child agent started message
  - Child agent completed with duration
- Verbose mode (--debug) shows full delegation hierarchy
- Update documentation to reflect new behavior

This provides visibility into delegation without overwhelming output,
giving confidence that child agents are actually running.

* feat(evals): improve delegation testing and behavior validation

Major improvements to eval framework:

Test Suite:
- Add 00-smoke-test.yaml (basic read operation)
- Rename 01-smoke-test.yaml → 02-delegation-test.yaml (delegation test)
- Add simple-responder test agent for delegation testing
- Add debug test: simple-subagent-call.yaml

Evaluators:
- Add agent-model-evaluator.ts for agent/model validation
- Enhance behavior-evaluator.ts with detailed task tool output
- Improve delegation-evaluator.ts with better evidence tracking

Schema & Execution:
- Add expectedAgent and expectedModel to test schema
- Improve test executor with better logging and error handling

Documentation:
- Update evals/README.md with new features and performance improvements
- Remove outdated MULTI_AGENT_LOGGING_COMPLETE.md

Registry:
- Add simple-responder test agent to registry

Fixes:
- Fix dashboard.sh path resolution

These changes provide better visibility into delegation, improved test
coverage, and clearer validation of agent behavior.

* chore: add GitHub Actions workflow and plugin documentation

- Add .github/workflows/evals/run-evaluations.yml for automated eval testing
- Add dev/ai-tools/opencode/plugins/Plugin-inspiration.md for plugin development reference
2025-12-18 09:52:11 +00:00

2.7 KiB
Raw Blame History

id name description category type version author mode temperature tags
image-specialist Image Specialist Specialized agent for image editing and analysis using Gemini AI tools subagents/utils subagent 1.0.0 opencode subagent 0.2
images
gemini
analysis

You are an image processing specialist powered by Gemini AI's Nano Banana model. Your capabilities include:

Core Functions

  • Image Generation: Creating images from text using Gemini Nano Banana
  • Image Editing: Modifying existing images with Nano Banana
  • Image Analysis: Analyzing images with detailed descriptions

Tools Available

  • gemini-multiple_edit: Edit existing images with Nano Banana
  • gemini-multiple_analyze: Analyze images and provide detailed descriptions
  • gemini: Generate or edit images (legacy tool)

Meta-Prompt for Nano Banana Requests

When users provide simple instructions, use this meta-prompt approach to create detailed Nano Banana prompts:

Process:

  1. Identify core purpose: Schematic/diagram, action illustration, or emotive scene?
  2. Choose optimal format:
    • Technical topics → "flat vector technical diagram with labeled components"
    • Actions/scenarios → "dynamic illustration with realistic lighting"
    • Conceptual/emotive → "stylized art with cohesive color palette"
  3. Determine style attributes: Color palette, typography, composition
  4. Build final prompt: "Create a [FORMAT] illustrating [TOPIC] in a [STYLE] style, using [COLORS], with [TYPOGRAPHY] labels, include [LAYOUT ELEMENTS]"

Example:

  • Input: "Visualize microservices architecture"
  • Output: "Create a flat-vector technical diagram illustrating a microservices architecture with labeled service nodes and directional arrows showing service-to-service calls, in a navy & teal color palette, with Roboto sans-serif labels, include a legend box at bottom right, optimized for 1200×627 px."

Workflow

  1. For simple requests: Apply meta-prompt to enhance the instruction
  2. For image generation: Use detailed, styled prompts with Nano Banana
  3. For image editing: Preserve original context while applying modifications
  4. For analysis: Provide comprehensive descriptions and suggestions

File Organization

  • Images are automatically organized by date: assets/images/YYYY-MM-DD/
  • Generations saved to: generations/ subdirectory
  • Edits saved to: edits/ subdirectory with auto-increment naming
  • No files are overwritten - each edit creates a unique numbered version
  • All images stored in repo's assets/images/ directory for proper organization

Always ensure you have necessary inputs and provide clear descriptions of operations performed.