Task Agent Discovery and Selection

This document describes how the task subsystem discovers agent definitions, merges multiple sources, and resolves a requested agent at execution time.

It covers runtime behavior as implemented today, including precedence, invalid-definition handling, and spawn/depth constraints that can make an agent effectively unavailable.

Implementation files

• src/task/discovery.ts
• src/task/agents.ts
• src/task/types.ts
• src/task/index.ts
• src/task/commands.ts
• src/prompts/agents/task.md
• src/prompts/tools/task.md
• src/discovery/helpers.ts
• src/config.ts
• src/task/executor.ts

Agent definition shape

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, advisor
• source: "bundled" | "user" | "project" (extension agents are tagged with their extension root’s project/user level)
• optional filePath

Parsing comes from frontmatter via parseAgentFields() (src/discovery/helpers.ts):

• missing name or description => invalid (null), caller treats as parse failure
• tools accepts CSV or array; if provided, yield is auto-added
• spawns accepts *, CSV, or array
• backward-compat behavior: if spawns missing but tools includes task, spawns becomes *
• output is passed through as opaque schema data
• read-summarize: false (normalized to readSummarize) forces the subagent’s read tool to return verbatim file content instead of structural summaries — runSubprocess applies it as a read.summarize.enabled: false override on the subagent’s isolated settings (src/task/executor.ts). scout and librarian ship with it disabled. Defaults to enabled when the field is absent.
• model accepts one selector, CSV, or an array. Entries are tried in order after role aliases are expanded.
• 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. musepi 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, configured per agent from the /agents hub via its prewalk strip) 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, configured per agent from the /agents hub via its advisor strip) 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

musepi discovers user agents from ~/.musepi/agent/agents/*.md and project agents from .musepi/agents/*.md.

Give the agent a role alias in frontmatter, then dispatch it by name. For model routing, task dispatch sets only agent; it does not set a worker model:

~/.musepi/agent/agents/reviewer.md:

---
name: reviewer
description: Review a change for correctness.
model: "@review"
---

Review the assigned change and report concrete findings.

Set the role mapping in ~/.musepi/agent/config.yml:

modelRoles:
  review: openai/gpt-5.4:high

@review resolves through modelRoles.review. Each modelRoles.<role> value stores a concrete model selector and may append a thinking suffix such as :high (src/config/model-resolver.ts). Changing that mapping affects subsequent task resolutions without editing agent definitions.

For a dispatch, set the agent name and task:

{
  "context": "Review the current change in this repository.",
  "tasks": [
    { "agent": "reviewer", "task": "Report concrete correctness findings." }
  ]
}

/model’s Roles view can assign and persist custom role mappings such as review, fast, and good. Changing only the active or default session selection does not remap those roles.

Watch running agents

After dispatch, press Alt+A to open Agent Hub. Its live roster shows each task agent’s status, current activity, model, age, and usage. Select an agent to read its transcript and steer it directly; parked agents can be revived from the same view.

vibe_spawn tier routing

vibe_spawn maps fast to bundled sonic and good to bundled task. Both resolve through task.agentModelOverrides before their bundled agent model defaults (src/vibe/runtime.ts, src/task/agents.ts).

Route these tiers through roles by keeping aliases in task.agentModelOverrides and concrete selectors only in modelRoles:

task:
  agentModelOverrides:
    sonic: "@fast_worker"
    task: "@good_worker"
modelRoles:
  fast_worker: openai/gpt-5-mini
  good_worker: openai/gpt-5.4:high

The vibe_spawn cli remains fast or good; update modelRoles to change the worker model.

Bundled agents

Bundled agents are embedded at build time (src/task/agents.ts) using text imports.

EMBEDDED_AGENT_DEFS defines:

• scout, designer, reviewer, librarian from prompt files
• task and sonic from shared task.md body plus injected frontmatter; no bundled agent sets prewalk — the generic task agent’s hand-off is armed by the task.prewalk setting (default off), or per agent via /agents / task.agentPrewalk / user agent frontmatter

Loading path:

1. loadBundledAgents() parses embedded markdown with parseAgent(..., "bundled", "fatal")
2. results are cached in-memory (bundledAgentsCache)
3. clearBundledAgentsCache() is test-only cache reset

Because bundled parsing uses level: "fatal", malformed bundled frontmatter throws and can fail discovery entirely.

Filesystem and plugin discovery

discoverAgents(cwd, home) (src/task/discovery.ts) merges agents from OMP-native roots and Claude plugin roots before appending bundled definitions. Cross-harness roots such as .claude/agents, .codex/agents, and .gemini/agents are intentionally skipped — their frontmatter schema is not the OMP task-agent contract (TASK_AGENT_CONFIG_SOURCE = ".musepi" filters both dir lists).

Discovery inputs

1. Nearest project .musepi agents dir from findAllNearestProjectConfigDirs("agents", cwd) (filtered to .musepi; first hit only)
2. User .musepi agents dir from getConfigDirs("agents", { project: false }) (filtered to .musepi; first hit only)
3. Claude plugin roots (listClaudePluginRoots(home, cwd)) with agents/ subdirs — only when isProviderEnabled("claude-plugins"); project-scope plugins sort before user-scope
4. Bundled agents (loadBundledAgents())

Actual source order

1. project .musepi/agents
2. user ~/.musepi/agent/agents
3. plugin agents/ dirs (project-scope first, then user-scope)
4. bundled agents last

Merge and collision rules

Discovery uses first-wins dedup by exact agent.name:

• A Set<string> tracks seen names.
• Loaded agents are flattened in directory order and kept only if name unseen.
• Bundled agents are filtered against the same set and only added if still unseen.

Implications:

• Project .musepi overrides user .musepi.
• Non-bundled agents override bundled agents with the same name.
• Name matching is case-sensitive (Task and task are distinct).
• Within one directory, markdown files are read in lexicographic filename order before dedup.

Invalid/missing agent file behavior

Per directory (loadAgentsFromDir):

• unreadable/missing directory: treated as empty (readdir(...).catch(() => []))
• file read or parse failure: warning logged, file skipped
• parse path uses parseAgent(..., level: "warn")

Frontmatter failure behavior comes from parseFrontmatter:

• parse error at warn level logs warning
• parser falls back to a simple key: value line parser
• if required fields are still missing, parseAgentFields fails, then AgentParsingError is thrown and caught by caller (file skipped)

Net effect: one bad custom agent file does not abort discovery of other files.

Agent lookup and selection

Lookup is exact-name linear search:

• getAgent(agents, name) => agents.find(a => a.name === name)

In spawn execution (TaskTool.#executeSync → #runSpawn):

1. agents are rediscovered at execution time (discoverAgents(this.session.cwd))
2. requested params.agent is resolved through getAgent
3. missing agent returns immediate tool response:
   • Unknown agent "...". Available: ...
   • no subprocess runs

Description vs execution-time discovery

TaskTool.create() builds the tool description from discovery results at initialization time. #executeSync rediscovers agents, so the runtime set can differ from what was listed in the earlier tool description if agent files changed mid-session. The async entry path still uses the initialization-time list to decide whether an agent is marked blocking before scheduling.

Model and structured-output precedence

Runtime model precedence is resolved by resolveEffectiveSubagentPolicy():

1. task.agentModelOverrides[agentName]
2. agent frontmatter model
3. the parent session model fallback

Runtime output schema precedence is:

1. the task item’s explicit outputSchema
2. agent frontmatter output
3. parent session outputSchema

The task item’s optional schemaMode overrides the parent session mode; the default is permissive.

The model-facing prompt (src/prompts/tools/task.md) no longer carries the old structured-output mismatch warning; it tags read-only agents and warns against offloading reasoning to scout/sonic instead.

Command discovery interaction

src/task/commands.ts is parallel infrastructure for workflow commands (not agent definitions), but it follows the same overall pattern:

• discover from capability providers first
• deduplicate by name with first-wins
• append bundled commands if still unseen
• exact-name lookup via getCommand

In src/task/index.ts, command helpers are re-exported with agent discovery helpers. Agent discovery itself does not depend on command discovery at runtime.

Availability constraints beyond discovery

An agent can be discoverable but still unavailable to run because of execution guardrails.

Disabled-agent settings

TaskTool.#executeSync checks task.disabledAgents after resolving the agent. If the requested name is disabled, execution returns an immediate error listing enabled alternatives when available.

Parent spawn policy

TaskTool.#executeSync checks session.getSessionSpawns():

• "*" => allow any
• "" => deny all
• CSV list => allow only listed names

If denied: immediate Cannot spawn '...'. Allowed: ... response.

Blocked self-recursion env guard

PI_BLOCKED_AGENT is read at tool construction. If request matches, execution is rejected with recursion-prevention message.

Recursion-depth gating (task tool availability inside child sessions)

In runSubprocess (src/task/executor.ts):

• depth computed from taskDepth
• task.maxRecursionDepth controls cutoff
• when at max depth:
  • task tool is removed from child tool list
  • child spawns env is set to empty

So deeper levels cannot spawn further tasks even if the agent definition includes spawns.

Plan mode behavior

When parent plan mode is enabled, TaskTool.#runSpawn builds an effectiveAgent before launching subprocesses:

• prepends the plan-mode subagent system prompt
• restricts tools to read, search, find, lsp, and web_search, plus ast_grep when the agent’s own tool list declares it (PLAN_MODE_AGENT_TOOL_ALLOWLIST)
• clears child spawns
• clears prewalk (read-only exploration must not receive the prewalk plan/implement nudges)

The same effectiveAgent is used for subprocess launch, model/thinking overrides, and output-schema selection.