c8f7103cb6
* 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
61 lines
No EOL
2.7 KiB
Markdown
61 lines
No EOL
2.7 KiB
Markdown
---
|
||
id: image-specialist
|
||
name: Image Specialist
|
||
description: "Specialized agent for image editing and analysis using Gemini AI tools"
|
||
category: subagents/utils
|
||
type: subagent
|
||
version: 1.0.0
|
||
author: opencode
|
||
mode: subagent
|
||
temperature: 0.2
|
||
|
||
# Tags
|
||
tags:
|
||
- 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. |