Skills Overview
How Trovella's research skills orchestrate structured, multi-step research via prompt-driven workflows and MCP tool integration.
Skills are Trovella's mechanism for teaching an external AI assistant how to conduct structured research. A skill is a markdown file installed in the user's Claude Code environment that instructs the AI platform on interview techniques, plan design, step execution, and output delivery. Skills contain no executable code -- they are prompts that shape the AI platform's behavior.
The Three-Layer Architecture
Trovella's research system separates concerns across three layers:
| Layer | Role | Contains code? |
|---|---|---|
| Skills (the instructor) | Teach the AI platform how to research -- interview patterns, plan templates, execution strategies, output formatting | No -- markdown prompts only |
| AI platform (the researcher) | Does the actual work -- searches, reads, reasons, analyzes, writes | External (Claude Code, etc.) |
| MCP tools (the project manager) | Stores plans, tracks progress, enforces structure, enables resume | Yes -- @repo/mcp TypeScript |
This separation means Trovella's server makes zero LLM API calls for the primary research use case. All reasoning happens externally on the AI platform, subsidized by the user's own subscription. Trovella provides the structure, persistence, and observability.
Skill Inventory
Four skills compose the research system:
| Skill | File | Purpose |
|---|---|---|
/research | .claude/skills/research/SKILL.md | Router skill -- interviews the user, classifies depth, delegates to scan or deep |
/research-scan | .claude/skills/research-scan/SKILL.md | Quick scan -- 2-4 step plans, fast turnaround, no checkpoints |
/research-deep | .claude/skills/research-deep/SKILL.md | Deep dive -- 5-8+ step plans, checkpoints, branching, critique |
/research-output | .claude/skills/research-output/SKILL.md | Deliverable generation -- formats results, stores output, captures feedback |
The user only sees /research. The routing to scan or deep is invisible -- the experience is seamless.
How Skills Relate to Other Components
Skills sit at the top of the research stack, driving everything below:
- Plan Orchestration -- skills design research plans by calling
create_research_plan, then execute them step-by-step through the pull-based loop (get_next_step/submit_step_result). See Plan Orchestration for the state machine, branching, and stall detection. - Reasoning -- skills instruct the AI platform on how to reason (analyst perspective, critic perspective, synthesizer perspective), but the actual LLM calls happen externally. The
extract_datatool is the one exception where Trovella makes a server-side AI call. See Reasoning for the AI integration layer. - Tool Protocol -- skills reference MCP tools by name in their instructions. The AI platform calls these tools via the MCP HTTP transport. See Tool Protocol for authentication, the tool catalog, and logging middleware.
- Admin Dashboard -- every skill invocation is tracked in the
skill_executiontable and visible at/admin/skill-executions. See Skill Executions View for the admin UI.
Pages in This Topic
Skill Definitions
What each skill does, its internal structure, and the prompt patterns it uses.
Execution Flow
The end-to-end lifecycle from user prompt through interview, routing, planning, execution, output delivery, and feedback capture.
Routing Logic
How the router skill decides between quick scan and deep dive, including the classification criteria, depth signals, and default behavior.
Lifecycle Tracking
How skill executions are tracked in the database, the log_skill_execution MCP tool, the tRPC admin router, and metadata conventions.