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delegation-patterns

**Scope**: Subagent instruction templates and parallel execution for research-coordinator-engineer. **Version range**: Claude Code SDK (Task tool / Agent tool) **Generated**: 2026-04-13

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$ npx -y skills add notque/vexjoy-agent --agent claude-code

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How this agent gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
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Context preview

The summary Claude sees to decide when to auto-load this agent.

**Scope**: Subagent instruction templates and parallel execution for research-coordinator-engineer. **Version range**: Claude Code SDK (Task tool / Agent tool) **Generated**: 2026-04-13

Agent definition

delegation-patterns.md

Delegation Patterns Reference

> **Scope**: Subagent instruction templates and parallel execution for research-coordinator-engineer. > **Version range**: Claude Code SDK (Task tool / Agent tool) > **Generated**: 2026-04-13

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Pattern Table

| Pattern | Use When | Subagent Count | Instruction Length | |---------|----------|---------------|-------------------| | Standard parallel dispatch | 3+ independent streams | 3 | 100-200 words each | | Deep-dive single stream | Narrow topic needing depth | 1–2 | 200-300 words | | Broad coverage dispatch | Comparison or enumeration | 1 per item, max 7 | 80-150 words each | | Fallback sequential | Dependencies between streams | 1 at a time | Full detail |

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Correct Patterns

Standard Parallel Dispatch (3 Concurrent)

Deploy all independent subagents in a single message. Sequential dispatch wastes N x latency instead of 1 x latency.

# Deploy all 3 in one message
Task(subject="Research GPU availability 2025-2030",
    content="""Focus on GPU/TPU availability from major cloud providers.
    Include chip production forecasts from TSMC, Samsung, Intel.
    SCOPE: Only compute chips. NOT general semiconductor market.
    DELIVERABLE: 350-500 word summary with 3+ statistics.
    SOURCES: Cloud provider reports, semiconductor analysts (2024-2025).""")

Task(subject="Research energy requirements for AI data centers",
    content="""Power consumption per GPU cluster, grid capacity constraints,
    cooling technology advances.
    SCOPE: Energy for AI compute only. NOT general data center trends.
    DELIVERABLE: 350-500 word summary with 3+ statistics.
    SOURCES: IEA reports, data center operators, academic papers (2024-2025).""")

Task(subject="Research regulatory environment for AI infrastructure",
    content="""Permitting timelines, environmental review, export controls.
    SCOPE: Regulations affecting infrastructure build. NOT AI product regulation.
    DELIVERABLE: 350-500 word summary with specific jurisdictions/timelines.
    SOURCES: Federal Register, EU legislation, news coverage (2024-2025).""")

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Instruction Required Components

Every instruction needs all five:

[1] SCOPE: What is IN scope (one specific sentence)
[2] OUT-OF-SCOPE: What to ignore (prevents scope creep)
[3] REQUIRED DATA POINTS: Concrete data to seek (not just narrative)
[4] DELIVERABLE FORMAT: Word count + format + quality bar
[5] SOURCE GUIDANCE: Where to look + source tier preference

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Adaptive Instruction After Initial Findings

When Wave 1 reveals gaps, Wave 2 instructions address them explicitly:

Research AI data center energy with focus on ACTUAL METERED DATA:
- Find published case studies with metered power figures
- Gap from Wave 1: found projections but no verified metered data
DELIVERABLE: List of 5+ specific measured data points with source and date.

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Pattern Catalog

Define Explicit Scope Boundaries

**Detection**:

grep -rL "OUT OF SCOPE\|Not in scope\|Exclude" research/*/instructions/ 2>/dev/null

**Signal**: `"Research AI trends in 2025."` — No scope = no boundary = subagent covers everything tangentially related.

**Preferred action**: "Research AI model training compute trends for frontier models (GPT-4 class+) in 2024-2025. Focus: training run sizes in FLOP, hardware configs, cost estimates. NOT inference, edge AI, or models below 10B params. DELIVERABLE: 300-400 words with 2+ training run statistics."

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Dispatch Independent Streams in Parallel

**Detection**:

grep -i "wait for\|after.*completes\|once.*done" research/*/plan.md

Three independent topics sequentially = 3x latency for no reason. Move all Task calls into one message.

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Specify Deliverable Format

**Detection**:

grep -rL "word\|paragraph\|bullet\|table" research/*/instructions/ 2>/dev/null

"Provide your findings" accepts any output, makes synthesis unpredictable.

**Preferred action**: "DELIVERABLE: 300-500 word summary with: (1) current status, (2) key restrictions, (3) timeline outlook."

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Error-Fix Mappings

| Symptom | Root Cause | Fix | |---------|------------|-----| | Overlapping subagent content | No scope differentiation | Add OUT-OF-SCOPE to every instruction | | 100 words vs 1000 words | No word count spec | Add word count range | | Subagent drifts | Scope lacks boundaries | Add clear out-of-scope statement | | Synthesis slow | Incompatible output formats | Standardize deliverable format | | Wave 2 duplicates Wave 1 | No Bayesian update | Reference Wave 1 gaps in Wave 2 | | >20 subagents | Over-scoped query | Merge topics or reduce scope |

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Subagent Count Decision Tree

Simple (single data point) → 1 subagent
Medium (3-5 streams) → 3 concurrent
Complex (6-10 streams) → 5-7 concurrent, group by theme
Very complex (>10) → STOP: restructure. Hard limit: 20.

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Detection Commands Reference

grep -rL "OUT OF SCOPE\|Not in scope" research/*/instructions/ 2>/dev/null
find research/ -name "*.md" -path "*/instructions/*" -exec \
  awk 'NF{w+=NF} END{if(w<50)print FILENAME": "w" words"}' {} \; 2>/dev/null
grep -in "wait for\|step [0-9]\+: deploy" research/*/plan.md 2>/dev/null

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See Also

  • `query-classification.md` — Determines subagent count and style
  • `error-catalog.md` — Common delegation failures
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