8.0 KiB
Context Bundle Example: Create Data Analyst Agent
Session: 20250121-143022-a4f2 Created: 2025-01-21T14:30:22Z For: TaskManager Status: in_progress
Task Overview
Create a new data analyst agent for the OpenAgents Control repository. This agent will specialize in data analysis tasks including data visualization, statistical analysis, and data transformation.
User Request
"Create a new data analyst agent that can help with data analysis, visualization, and statistical tasks"
Relevant Standards (Load These Before Starting)
Core Standards:
.opencode/context/core/standards/code-quality.md→ Modular, functional code patterns.opencode/context/core/standards/test-coverage.md→ Testing requirements and TDD.opencode/context/core/standards/documentation.md→ Documentation standards
Core Workflows:
.opencode/context/core/workflows/feature-breakdown.md→ Task breakdown methodology
Repository-Specific Context (Load These Before Starting)
Quick Start (ALWAYS load first):
.opencode/context/openagents-repo/quick-start.md→ Repo orientation and common commands
Core Concepts (Load based on task type):
.opencode/context/openagents-repo/core-concepts/agents.md→ How agents work.opencode/context/openagents-repo/core-concepts/evals.md→ How testing works.opencode/context/openagents-repo/core-concepts/registry.md→ How registry works.opencode/context/openagents-repo/core-concepts/categories.md→ How organization works
Guides (Load for specific workflows):
.opencode/context/openagents-repo/guides/adding-agent.md→ Step-by-step agent creation.opencode/context/openagents-repo/guides/testing-agent.md→ Testing workflow.opencode/context/openagents-repo/guides/updating-registry.md→ Registry workflow
Key Requirements
From Standards:
- Agent must follow modular, functional programming patterns
- All code must be testable and maintainable
- Documentation must be concise and high-signal
- Include examples where helpful
From Repository Context:
- Agent file must be in
.opencode/agent/data/directory (category-based organization) - Must include proper frontmatter metadata (id, name, description, category, type, version, etc.)
- Must follow naming convention:
data-analyst.md(kebab-case) - Must include tags for discoverability
- Must specify tools and permissions
- Must be registered in
registry.json
Naming Conventions:
- File name:
data-analyst.md(kebab-case) - Agent ID:
data-analyst - Category:
data - Type:
agent
File Structure:
- Agent file:
.opencode/agent/data/data-analyst.md - Eval directory:
evals/agents/data/data-analyst/ - Eval config:
evals/agents/data/data-analyst/config/eval-config.yaml - Eval tests:
evals/agents/data/data-analyst/tests/ - README:
evals/agents/data/data-analyst/README.md
Technical Constraints
- Must use category-based organization (data category)
- Must include proper frontmatter metadata
- Must specify tools needed (read, write, bash, etc.)
- Must define permissions for sensitive operations
- Must include temperature setting (0.1-0.3 for analytical tasks)
- Must follow agent prompt structure (context, role, task, instructions)
- Eval tests must use YAML format
- Registry entry must follow schema
Files to Create/Modify
Create:
.opencode/agent/data/data-analyst.md- Main agent definition with frontmatter and promptevals/agents/data/data-analyst/config/eval-config.yaml- Eval configurationevals/agents/data/data-analyst/tests/smoke-test.yaml- Basic smoke testevals/agents/data/data-analyst/tests/data-analysis-test.yaml- Data analysis capability testevals/agents/data/data-analyst/README.md- Agent documentation
Modify:
registry.json- Add data-analyst agent entry.opencode/context/navigation.md- Add data category context if needed
Success Criteria
- Agent file created with proper frontmatter metadata
- Agent prompt follows established patterns (context, role, task, instructions)
- Eval test structure created with config and tests
- Smoke test passes
- Data analysis test passes
- Registry entry added and validates
- README documentation created
- All validation scripts pass
Validation Requirements
Scripts to Run:
./scripts/registry/validate-registry.sh- Validates registry.json schema and entries./scripts/validation/validate-test-suites.sh- Validates eval test structure
Tests to Run:
cd evals/framework && npm run eval:sdk -- --agent=data/data-analyst --pattern="smoke-test.yaml"- Run smoke testcd evals/framework && npm run eval:sdk -- --agent=data/data-analyst- Run all tests
Manual Checks:
- Verify frontmatter includes all required fields
- Check that tools and permissions are appropriate
- Ensure prompt is clear and follows standards
- Verify eval tests are meaningful
Expected Output
Deliverables:
- Functional data analyst agent
- Complete eval test suite
- Registry entry
- Documentation
Format:
- Agent file: Markdown with YAML frontmatter
- Eval config: YAML format
- Eval tests: YAML format with test cases
- README: Markdown documentation
Progress Tracking
- Context loaded and understood
- Agent file created with frontmatter
- Agent prompt written
- Eval directory structure created
- Eval config created
- Smoke test created
- Data analysis test created
- README documentation created
- Registry entry added
- Validation scripts run
- All tests pass
- Documentation updated
Instructions for Subagent
IMPORTANT:
- Load ALL context files listed in "Relevant Standards" and "Repository-Specific Context" sections BEFORE starting work
- Follow ALL requirements from the loaded context
- Apply naming conventions and file structure requirements
- Validate your work using the validation requirements
- Update progress tracking as you complete steps
Your Task: Create a complete data analyst agent for the OpenAgents Control repository following all established conventions and standards.
Approach:
-
Load Context: Read all context files listed above to understand:
- How agents are structured (core-concepts/agents.md)
- How to add an agent (guides/adding-agent.md)
- Code standards (standards/code-quality.md)
- Testing requirements (core-concepts/evals.md)
-
Create Agent File:
- Create
.opencode/agent/data/data-analyst.md - Add frontmatter with all required metadata
- Write agent prompt with:
- Context section (system, domain, task, execution context)
- Role definition
- Task description
- Instructions and workflow
- Tools and capabilities
- Examples if helpful
- Create
-
Create Eval Structure:
- Create directory:
evals/agents/data/data-analyst/ - Create config:
config/eval-config.yaml - Create tests directory:
tests/ - Create smoke test:
tests/smoke-test.yaml - Create capability test:
tests/data-analysis-test.yaml - Create README:
README.md
- Create directory:
-
Update Registry:
- Add entry to
registry.jsonfollowing schema - Include: id, name, description, category, type, path, version, tags
- Add entry to
-
Validate:
- Run validation scripts
- Run eval tests
- Fix any issues
Constraints:
- Agent must be in
datacategory - Must follow functional programming patterns
- Must include proper error handling
- Must specify appropriate tools (read, write, bash for data tasks)
- Temperature should be 0.1-0.3 for analytical precision
- Eval tests must be meaningful and test actual capabilities
Questions/Clarifications:
- What specific data analysis capabilities should be emphasized? (visualization, statistics, transformation)
- Should the agent support specific data formats? (CSV, JSON, Parquet)
- Should the agent integrate with specific tools? (pandas, matplotlib, etc.)
- What level of statistical analysis? (descriptive, inferential, predictive)
Note: This is an example context bundle. In practice, the subagent would receive this file and follow the instructions to complete the task.