# Capability Packages Guide ## What Is a Capability Package A capability package bundles everything an agent needs to handle a specific domain into a single reusable unit. Activating one produces an `AgentDefinition` that the coordinator registers and routes tasks to. Each package carries: - `system_prompt` — domain-optimized instructions injected as the LLM system message before every task execution - `preferred_model` / `preferred_provider` — e.g. `claude-opus-4-6` for deep code reasoning, `claude-sonnet-4-6` for cost-efficient writing tasks - `task_types` — strings matched by the coordinator's `extract_task_type` heuristic against task titles and descriptions to select this agent - `mcp_tools` — list of MCP tool IDs (`file_read`, `git_diff`, etc.) activated for this agent via `vapora-mcp-server` - `temperature` / `max_tokens` / `priority` / `parallelizable` — execution parameters controlling output quality, cost, scheduling order, and concurrency ## Built-in Capabilities | ID | Role | Model | Temp | Max Tokens | Use Case | |----|------|-------|------|------------|----------| | `code-reviewer` | `code_reviewer` | `claude-opus-4-6` | 0.1 | 8192 | Security and correctness review; JSON output with severity levels and `merge_ready` flag | | `doc-generator` | `documenter` | `claude-sonnet-4-6` | 0.3 | 16384 | Source-to-documentation generation with rustdoc/JSDoc/docstring output | | `pr-monitor` | `monitor` | `claude-sonnet-4-6` | 0.1 | 4096 | PR health check; `READY` / `NEEDS_REVIEW` / `BLOCKED` status output | The `code-reviewer` uses Opus 4.6 because review tasks benefit from deep reasoning over complex code patterns. Temperature 0.1 ensures reproducible findings across repeated runs on the same diff. `pr-monitor` is `parallelizable = false` — concurrent runs on the same PR would produce conflicting status reports. ## Activating Built-ins at Runtime The agent server calls `CapabilityRegistry::with_built_ins()` at startup automatically. All three built-ins are registered and their executors spawned before the HTTP listener opens — no action required when running the standard agent server (`crates/vapora-agents`). For programmatic use: ```rust use vapora_capabilities::CapabilityRegistry; let registry = CapabilityRegistry::with_built_ins(); // "code-reviewer", "doc-generator", "pr-monitor" are now registered let def = registry.activate("code-reviewer")?; // def.role == "code_reviewer" // def.system_prompt == Some("") // def.llm_model == "claude-opus-4-6" // def.llm_provider == "claude" ``` `activate` returns an `AgentDefinition` from `vapora-shared`. The system prompt is embedded in the definition and available at `def.system_prompt` — the executor injects it before every task without any further lookup. ## Overriding a Built-in ### Via TOML Config File Override fields are applied on top of the existing built-in spec. Only fields present in TOML are changed; everything else keeps its default. An unknown override `id` is skipped with a warning, not an error. ```toml # config/capabilities.toml # Switch code-reviewer to Sonnet for cost savings [[override]] id = "code-reviewer" preferred_model = "claude-sonnet-4-6" max_tokens = 16384 # Replace the doc-generator system prompt for your tech stack [[override]] id = "doc-generator" system_prompt = """ You are a technical documentation specialist for Rust async systems. Follow rustdoc conventions. All examples must be runnable. """ ``` Load and apply at startup (or on config reload): ```rust use vapora_capabilities::{CapabilityRegistry, CapabilityLoader}; let registry = CapabilityRegistry::with_built_ins(); CapabilityLoader::load_and_apply("config/capabilities.toml", ®istry)?; ``` `load_and_apply` reads the file, parses TOML, and applies overrides + custom entries in one call. The call is idempotent — re-applying the same file replaces existing specs rather than erroring. ### Via the Registry API Directly ```rust use vapora_capabilities::{CapabilityRegistry, CapabilitySpec, CustomCapability}; let registry = CapabilityRegistry::with_built_ins(); // Fetch the current spec, mutate it, push it back let mut spec = registry.get("code-reviewer").unwrap().spec(); spec = spec.with_model("claude-sonnet-4-6").with_max_tokens(16384); registry.override_spec("code-reviewer", spec)?; // Returns CapabilityError::NotFound if the id is not registered // Returns CapabilityError::InvalidSpec if the spec id does not match the target id ``` ## Adding a Custom Capability Custom entries in TOML are full `CapabilitySpec` definitions — all fields are required. They are registered with `register_or_replace`, so re-applying the config is safe. ```toml [[custom]] id = "db-optimizer" display_name = "Database Optimizer" description = "Analyzes and optimizes SurrealQL queries and schema" agent_role = "db_optimizer" task_types = ["db_optimization", "query_review", "schema_review"] system_prompt = """ You are a SurrealDB performance expert. Analyze queries and schema definitions for: index usage, full-table scans, unnecessary JOINs, missing composite indexes. Output JSON: { "issues": [...], "optimized_query": "...", "index_suggestions": [...] } """ mcp_tools = ["file_read", "code_search"] preferred_provider = "claude" preferred_model = "claude-sonnet-4-6" max_tokens = 4096 temperature = 0.1 priority = 75 parallelizable = true ``` The `task_types` list must overlap with words present in task titles or descriptions. The coordinator's heuristic tokenizes the task text and checks for matches against registered task-type strings. If no match is found, the task falls back to default role assignment. Use lowercase snake\_case strings that reflect verbs and nouns users will write in task titles (`"query_review"`, `"db_optimization"`). ## Environment Variables The agent server reads provider credentials from the environment at startup to configure the LLM router. | Variable | Effect | |----------|--------| | `LLM_ROUTER_CONFIG` | Path to a `llm-router.toml` file; takes precedence over all individual API key variables | | `ANTHROPIC_API_KEY` | Enables the `claude` provider; default model `claude-sonnet-4-6` | | `OPENAI_API_KEY` | Enables the `openai` provider; default model `gpt-4o` | | `OLLAMA_URL` | Enables the `ollama` provider (e.g. `http://localhost:11434`) | | `OLLAMA_MODEL` | Model used with Ollama (default: `llama3.2`) | | `BUDGET_CONFIG_PATH` | Path to budget config file (default: `config/agent-budgets.toml`) | If none of `LLM_ROUTER_CONFIG`, `ANTHROPIC_API_KEY`, `OPENAI_API_KEY`, or `OLLAMA_URL` are set, executors run in stub mode — tasks are accepted and return placeholder responses. This is intentional for integration tests and offline development. ## Checking What Is Registered ```rust let ids = registry.list_ids(); // sorted alphabetically: ["code-reviewer", "doc-generator", "pr-monitor"] let count = registry.len(); // 3 // Check and activate a specific capability if registry.contains("db-optimizer") { let def = registry.activate("db-optimizer")?; println!("role: {}, model: {}", def.role, def.llm_model); } // Iterate all registered capabilities (order is HashMap-based, not sorted) for cap in registry.list_all() { let spec = cap.spec(); println!("{}: {} ({})", spec.id, spec.display_name, spec.preferred_model); } ``` ## Capability Spec Field Reference | Field | Type | Description | |-------|------|-------------| | `id` | `String` | Unique kebab-case identifier (e.g., `"code-reviewer"`) | | `display_name` | `String` | Human-readable name shown in UIs and logs | | `description` | `String` | Brief purpose description embedded in the agent's log entries | | `agent_role` | `String` | Role name used by the coordinator for task routing (e.g., `"code_reviewer"`) | | `task_types` | `Vec` | Keywords matched against task text by the coordinator heuristic | | `system_prompt` | `String` | Full system message injected before every task execution | | `mcp_tools` | `Vec` | MCP tool IDs available to this agent via `vapora-mcp-server` | | `preferred_provider` | `String` | LLM provider name (`"claude"`, `"openai"`, `"ollama"`) | | `preferred_model` | `String` | Model ID within the provider (e.g., `"claude-opus-4-6"`) | | `max_tokens` | `u32` | Maximum output tokens per task execution | | `temperature` | `f32` | Sampling temperature 0.0–1.0; lower = more deterministic | | `priority` | `u32` | Assignment priority 0–100; higher = preferred when multiple agents match | | `parallelizable` | `bool` | Whether multiple instances may run concurrently for the same task type | ## Writing Your Own Built-in Built-ins are unit structs in `crates/vapora-capabilities/src/built_in/`. Follow this pattern: ```rust // crates/vapora-capabilities/src/built_in/sql_optimizer.rs use crate::capability::{Capability, CapabilitySpec}; const SYSTEM_PROMPT: &str = r#"You are a SurrealDB query optimization expert. Analyze the provided query or schema definition. Output JSON: { "issues": [...], "optimized": "...", "indexes": [...] }"#; #[derive(Debug)] pub struct SqlOptimizer; impl Capability for SqlOptimizer { fn spec(&self) -> CapabilitySpec { CapabilitySpec { id: "sql-optimizer".to_string(), display_name: "SQL Optimizer".to_string(), description: "Optimizes SurrealQL queries and schema definitions".to_string(), agent_role: "sql_optimizer".to_string(), task_types: vec![ "sql_optimization".to_string(), "query_review".to_string(), "schema_review".to_string(), ], system_prompt: SYSTEM_PROMPT.to_string(), mcp_tools: vec!["file_read".to_string(), "code_search".to_string()], preferred_provider: "claude".to_string(), preferred_model: "claude-sonnet-4-6".to_string(), max_tokens: 4096, temperature: 0.1, priority: 75, parallelizable: true, } } } ``` Then wire it into the module and registry: ```rust // crates/vapora-capabilities/src/built_in/mod.rs mod sql_optimizer; pub use sql_optimizer::SqlOptimizer; ``` ```rust // crates/vapora-capabilities/src/registry.rs — inside with_built_ins() registry.register(SqlOptimizer).expect("sql-optimizer id collision"); ``` The `expect` on `register` is intentional — built-in IDs are unique by construction, and a collision at startup indicates a programming error that must be caught during development, not at runtime.