Execute a research task using compositional workflow planning. Characterizes the task, selects an approach from a library of research strategies, executes it, and self-evaluates output quality.
Research this topic: $ARGUMENTS
Rate the research task on each dimension (1-5):
| Dimension | 1 (Low) | 5 (High) |
|---|---|---|
| Scope | Point question (single fact) | Frontier mapping (state of entire field) |
| Domain structure | Single field, established methods | Distant cross-domain analogy |
| Evidence type | Empirical data, measurements | Theoretical arguments, first-principles |
| Time horizon | What's true right now | What could become true (speculative) |
| Fidelity | Ballpark / directional | Rigorous / publication-grade |
Query the MAP-Elites workflow library for strategies matching this task's coordinates:
sqlite3 research-workflows/workflows.db "SELECT id, name, archetype, fitness_score FROM workflows WHERE status IN ('active', 'seed') ORDER BY fitness_score DESC LIMIT 10;"
For more targeted retrieval, filter by behavioral region:
sqlite3 research-workflows/workflows.db "SELECT w.id, w.name, ws.name as step_name, ws.description, ws.rationale FROM workflows w JOIN workflow_steps ws ON w.id = ws.workflow_id WHERE w.archetype = '<archetype>' AND w.status IN ('active', 'seed') ORDER BY w.fitness_score DESC, ws.step_order;"
Use retrieved workflows to inform Phase 2 strategy selection. Prefer workflows with high fitness scores and usage counts.
Based on the characterization and library query, select the most appropriate research archetype:
Exploratory (high scope, moderate fidelity)
Confirmatory (low scope, high fidelity)
Analytical (moderate scope, high evidence)
Generative (high domain structure, moderate time horizon)
Applied (low time horizon, high fidelity)
Don't just pick one strategy. Identify which sub-techniques from multiple strategies suit this specific task:
Execute the composed workflow using available tools:
web_search_exa, web_search_advanced_exa) — semantic/neural search, cross-domain retrievalfirecrawl_scrape, firecrawl_search) — structured web scrapingScore your output on each dimension (0-1):
Report the scores honestly. Low scores on surprise or actionability are useful signals — they indicate the research question may need reframing.
Log this execution and update the workflow library:
# Log the execution
sqlite3 research-workflows/workflows.db "INSERT INTO executions (task_description, task_scope, task_domain_structure, task_evidence_type, task_time_horizon, task_fidelity, score_coherence, score_grounding, score_compression, score_surprise, score_actionability, score_composite) VALUES ('<description>', <scope>, <domain>, <evidence>, <horizon>, <fidelity>, <coherence>, <grounding>, <compression>, <surprise>, <actionability>, <composite>);"
If composite score exceeds the current occupant of this behavioral region, the composed workflow becomes a candidate for library insertion.
## Research: [Topic]
### Characterization
Scope: [1-5] | Domain: [1-5] | Evidence: [1-5] | Horizon: [1-5] | Fidelity: [1-5]
### Strategy
[Which archetypes were combined and why]
### Findings
[Structured findings — use headers, tables, or lists as appropriate]
### Quality Assessment
Coherence: [0-1] | Grounding: [0-1] | Compression: [0-1] | Surprise: [0-1] | Actionability: [0-1]
### Key Takeaway
[Single paragraph — the compressed thesis]
### Recommended Next Steps
[What to do with these findings]