autosearch-delegate-subtask

name	autosearch:delegate-subtask
description	Define the execution contract for isolating a research sub-task — input schema, budget, return summary, evidence list, failure status. Complements decompose-task (which only splits the problem) by giving each split a bounded, auditable execution unit the runtime AI can farm out to a sub-agent or parallel session.
version	0.1.0
layer	meta
domains	["workflow","delegation"]
scenarios	["subagent-execution","isolated-subtask","budgeted-research"]
trigger_keywords	["delegate","subtask","sub-agent","parallel","isolate","budget"]
model_tier	Standard
auth_required	false
cost	free
experience_digest	experience.md

Delegate Subtask — Execution Contract

decompose-task splits a problem into sub-questions. This skill says how to execute each sub-question with a stable, auditable contract: inputs, budget, outputs, failure modes. Borrowed from MiroThinker + DeepAgents + deer-flow + DeepResearchAgent subagent patterns.

Contract

input:
  id: str                     # stable subtask id, e.g. "sub_1" / "sub_1a"
  parent_id: str | null       # linking back to the decompose-task output
  question: str               # one specific sub-question
  rationale: str              # why this subtask matters for the parent goal
  scope: list[str]            # channels / tools the subtask may touch
  budget:
    latency_seconds: int
    cost_usd: float
    tool_calls: int           # max total tool invocations
  context_seed: list[dict]    # evidence already gathered the subtask should start with
  stop_conditions: list[str]  # e.g. "answer rubrics satisfied" / "budget exhausted"

output:
  id: str                     # echoes input.id
  status: "success" | "partial" | "failure"
  summary: str                # 3-6 sentences; what was found
  evidence: list[dict]        # slim-dict Evidence items the subtask produced
  citations: list[str]        # URL list, matched to evidence
  follow_ups: list[str]       # open questions, if partial
  metrics:
    latency_ms: int
    cost_usd: float
    tool_calls: int
    channels_hit: list[str]
  failure_reason: str | null

Invocation Policy

• One subtask per thread/session — isolation matters. Do not merge two subtasks' tool calls into one session.
• Budget is the governor. Subtask must halt when ANY budget axis is exhausted and report status: "partial".
• Read context_seed, don't re-search it. Seed is evidence the parent already has; subtask should build on, not duplicate.
• Return slim evidence — use autosearch's Evidence.to_slim_dict() shape so the parent can dedupe/merge.
• Follow-ups are first-class. If a subtask runs out of budget but finds a promising lead, emit that in follow_ups for the parent planner to decide.

Concurrency Guard

Delegate subtasks are cheap in parallel but expensive in total cost. Runtime AI should apply:

• max_parallel_subtasks: 4 — hard cap, inspired by deer-flow's subagent_limit_middleware.
• max_subtasks_per_session: 12 — escalate to user if the plan generates more.
• subtask_timeout_headroom: 1.2x — if any axis exceeds 1.2× its budget, kill immediately.

When This Skill Is Used

• Runtime AI decomposed a complex research question into 3+ sub-questions and wants to execute them in parallel sessions.
• A single sub-question is so expensive that the parent planner wants it quarantined (cost / time).
• The parent planner wants per-subtask accountability (which sub-questions succeeded; which were over-budget; where to follow up).

When NOT Used

• Trivial single-query research — overkill; just call a channel directly.
• Cross-cutting reflection / synthesis — that's synthesize-knowledge, not a subtask boundary.

Related Skills

• Produces input from → decompose-task.
• Feeds output to → assemble-context / synthesize-knowledge / citation-index.
• Cost controlled by → autosearch:model-routing (Standard tier default for the subtask body; Best for the final consolidation).

MCP Tool Usage

Use the delegate_subtask MCP tool to run a query across multiple channels in parallel:

delegate_subtask(
  task_description="Find Chinese UGC discussions about Cursor AI editor",
  channels=["xiaohongshu", "zhihu", "bilibili"],
  query="Cursor AI 编程助手 用户体验",
  max_per_channel=5
)

Returns {evidence_by_channel: {"xiaohongshu": [...], "zhihu": [...]}, summary: "15 results from 3 channels", failed_channels: [], budget_used: {...}}. Feed evidence_by_channel values directly into citation_add or your synthesis.

Failure Modes

• Budget exhausted before any evidence collected → status: failure, failure_reason: "budget_exhausted_before_first_result".
• Subagent crashed mid-execution → status: failure, failure_reason: "subagent_crash: <exception>". Parent planner decides retry vs. give up.
• Partial success → status: partial, follow_ups populated. Parent planner decides whether to escalate budget or accept partial.

Quality Bar

• Evidence items have non-empty title and url.
• No crash on empty or malformed API response.
• Source channel field matches the channel name.