Subject Hygiene for Task Delegation
Problem
When delegating work to subagents via the Task tool, empty or generic task subjects make conversations:
- Untraceable: Cannot identify what a subagent was working on
- Unreferencable: Cannot discuss specific subagent work later
- Confusing: Multiple subagents with empty subjects are indistinguishable
From 48 Task invocations across 88 sessions, empty task subjects were identified as a major pain point.
Solution
Enforce clear, specific task subjects for every Task tool invocation. A good subject should:
- Not be empty (baseline requirement)
- Be specific and descriptive (what is being done)
- Be reference-able (can be discussed later)
- Follow naming conventions (imperative mood, clear target)
Examples:
❌ Bad subjects:
- "" (empty)
- "research"
- "explore"
- "task"
✅ Good subjects:
- "Explore newsletter component implementation"
- "Search for dark mode patterns in codebase"
- "Analyze error handling in API routes"
- "Find all OAuth configuration files"
How to use it
Before invoking Task tool, verify the subject meets all criteria:
- Length check: Minimum 3-4 words
- Action check: Starts with verb (Explore, Analyze, Search, Find)
- Target check: Specifies what is being acted upon
- Reference check: Could you point to this conversation later and say "the one that [subject]"?
Template for good subjects:
[Action Verb] + [Target/Scope] + [Optional Context]
Examples: - "Explore + newsletter component + implementation details" - "Search + codebase + for dark mode patterns" - "Analyze + API routes + error handling approach" - "Find + all OAuth + configuration files"
Anti-pattern prevention:
Prevents "Empty Subject Anti-Pattern" which makes conversations untraceable and subagent work indistinguishable.
Real-world impact:
From nibzard-web session with 4 parallel subagents: - agent-a7911db: "Newsletter component exploration" - agent-adeac17: "Modal pattern discovery" - agent-a03b9c9: "Search implementation research" - agent-b84c3d1: "Log page analysis"
Clear subjects enabled the main agent to synthesize findings from each subagent effectively.
Trade-offs
Pros:
- Traceable subagent conversations
- Reference-able work items
- Clearer synthesis of parallel work
- Better communication with user
- Easier debugging of delegation issues
Cons:
- Requires upfront thinking about subject
- Longer subject strings (minor overhead)
- No technical enforcement (requires discipline)
When it matters most:
- Parallel subagent delegations (2+ agents)
- Complex research tasks
- Long-running subagent work
- When user needs to review subagent output
References
- SKILLS-AGENTIC-LESSONS.md - Skills based on lessons learned from analyzing 88 real-world Claude conversation sessions
- Related patterns: Sub-Agent Spawning, Parallel Tool Call Learning