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feat(coding-agent): introduced fine grained per agent advisor configuration

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- Replaced blanket subagent advisor global settings with fine-grained per-agent configuration and frontmatter support.
- Added dashboard keybindings and inline override editors for managing agent advisor patterns.
- Implemented settings migration logic to convert legacy global options into per-agent settings.
- Updated session persistence and execution layers to restore and enforce per-agent advisor behaviors.
This commit is contained in:
can1357
2026-08-13 04:59:50 +02:00
parent 1132c3e31c
commit e8eed95130
27 changed files with 586 additions and 91 deletions
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docs/task-agent-discovery.md
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@@ -27,7 +27,7 @@ It covers runtime behavior as implemented today, including precedence, invalid-d
Task agents normalize into `AgentDefinition` (`src/task/types.ts`):
- required `name`, `description`, and `systemPrompt`
- optional `tools`, `spawns`, prioritized `model` list, `thinkingLevel`, `output`, `blocking`, `autoloadSkills`, `readSummarize`, `prewalk`
- optional `tools`, `spawns`, prioritized `model` list, `thinkingLevel`, `output`, `blocking`, `autoloadSkills`, `readSummarize`, `prewalk`, `advisor`
- `source`: `"bundled" | "user" | "project"` (extension agents are tagged with their extension root's project/user level)
- optional `filePath`
@@ -43,7 +43,8 @@ Parsing comes from frontmatter via `parseAgentFields()` (`src/discovery/helpers.
- `thinking-level` / `thinking` selects the agent's configured effort. When `task.enableEffort` (default `false`) exposes it, a task item's coarse `effort` (`lo`, `med`, `hi`) takes precedence at launch. OMP maps that hint to the selected model's lowest, middle, or highest supported effort, then clamps it to `task.maxEffort` (default `max`). The ceiling is carried across retry-fallback model switches. If the selected model has no supported effort at or below the ceiling, the spawn fails; models without a controllable effort surface instead fall back to their normal selector.
- `blocking: true` makes the parent wait for that agent even when async task execution is enabled
- `autoloadSkills` names skills from the parent session to inject before the first child prompt; unknown names are ignored
- `prewalk: true` starts the subagent on its resolved model and hands off to the default prewalk target (the `smol` role) at its first edit/write, exactly like the session-level `--prewalk`; a string value (e.g. `prewalk: "@smol"` or `prewalk: "openai/gpt-5-mini"`) picks a custom target. The `task.agentPrewalk` settings record (agent name → `"on"` / `"off"` / pattern, toggled per agent from `/agents` with `P`) overrides the frontmatter. Resolution happens in `runSubprocess` (`src/task/executor.ts`). An unavailable target is skipped instead of failing the spawn. A resolved target is skipped only when both its model identity and its effective thinking mode/level match the starting selection after model clamping; a same-model effort downgrade is a real hand-off and still arms and switches at the first edit/write.
- `prewalk: true` starts the subagent on its resolved model and hands off to the default prewalk target (the `smol` role) at its first edit/write, exactly like the session-level `--prewalk`; a string value (e.g. `prewalk: "@smol"` or `prewalk: "openai/gpt-5-mini"`) picks a custom target. The `task.agentPrewalk` settings record (agent name → `"on"` / `"off"` / pattern, edited per agent from `/agents` with `P`) overrides the frontmatter. Resolution happens in `runSubprocess` (`src/task/executor.ts`). An unavailable target is skipped instead of failing the spawn. A resolved target is skipped only when both its model identity and its effective thinking mode/level match the starting selection after model clamping; a same-model effort downgrade is a real hand-off and still arms and switches at the first edit/write.
- `advisor: true` pairs spawned sessions of the agent with an advisor running the model resolved for the `advisor` role; a string value (e.g. `advisor: "deepseek/deepseek-v4-flash"` or `advisor: "@smol:high"`) sets an explicit advisor model pattern (optional `:level` suffix), applied as the spawned session's `modelRoles.advisor`. The `task.agentAdvisor` settings record (agent name → `"on"` / `"off"` / pattern, edited per agent from `/agents` with `A`) overrides the frontmatter. Resolution happens in `runSubprocess` (`src/task/executor.ts`); subagents default to no advisor, and the effective opt-in is persisted in `session_init` so cold revival restores it.
## Role-backed custom agents