explore

name	explore
description	Trigger when user says: "explore options", "what could we do for X", "research approaches", "/empire-research:explore", "investigate approaches", "spawn research team", "what are the options", "options analysis", "explore solutions", "have the team explore". Open-ended exploration: shallow scan enumerates 3–5 candidate approaches, user picks subset to deep-dive, parallel research per approach, consolidated comparison with recommended direction. Findings stay local — never posted externally.
compatibility	Requires network access (web search and fetch); dispatches research subagents.

• Use explore when the solution space is open: user knows the problem, not the options
• Use /empire-research:compare instead when user already has a known set of options to evaluate head-to-head
• If user input names specific options (A vs B vs C), suggest /empire-research:compare and confirm before proceeding here

• Read conversation for problem statement, scope, constraints, success criteria
• Signals to read:
  • Explicit user description of the problem
  • Recent code or files providing technical context
  • Stated constraints (budget, timeline, stack, team size)
  • Prior approaches already ruled out
  • Definition of "good enough" outcome
• If problem statement unclear → ask one clarifying question at a time
• If structured choices help → use AskUserQuestion with concrete options
• MUST state inferred problem statement back to user before any dispatch
• MUST get user confirmation on problem statement
• MUST NOT dispatch any agent until problem is confirmed

• After problem confirmed, dispatch ONE research agent for broad enumeration

• Agent names vary by environment; do not assume a specific agent exists

• Inspect available subagents via the Agent tool's subagent_type parameter

• Pick the available agent whose name/description best matches general research synthesis or broad information retrieval; if multiple candidates fit, prefer the most specific; if none fit, use the most general research-oriented agent available

• Shallow agent instructions:

  • Enumerate 3–5 candidate approaches only

  • One short paragraph per approach — no deep evaluation

  • Required output format:

    1. <Approach Name>
       <One-paragraph description — what it is, how it addresses the problem>
    
    2. <Approach Name>
       ...
    

  • Cap response under 300 words

• Present shallow-scan output to user verbatim before proceeding

• Gate exists because deep-dive spawns one parallel agent per approach (real cost) — user steers spend toward the approaches worth researching
• After shallow scan, present results and ask user:
  • Which approaches to deep-dive (may pick multiple)
  • Whether to add, remove, or reframe any approach
• MUST wait for explicit user selection before deep dispatch
• MUST NOT infer selection and proceed silently
• If user requests a different approach not in list → add it, confirm updated list

• After approaches selected, dispatch the deep research one of two ways

• Preferred — Workflow tool available:

  • Invoke the bundled deep-dive workflow; it fans out one researcher per approach with structured pros/cons/fit:

    Workflow({
      scriptPath: "${CLAUDE_PLUGIN_ROOT}/workflows/explore-deepdive.js",
      args: { problem, constraints, successCriteria, approaches: [{ name, description }] },
    })
    

  • Surface the workflow's log() lines as progress

  • Feed the returned approaches[] into consolidated-report

  • Skip agent-selection and parallel-deep-dispatch — the workflow owns dispatch

• Fallback — Workflow tool unavailable: use agent-selection then parallel-deep-dispatch below

• Fallback path — only when the Workflow tool is unavailable (see dispatch-mode)
• Pick one deep agent per selected approach
• Agent names vary by environment; do not assume a specific agent exists
• Inspect available subagents via the Agent tool's subagent_type parameter
• For each selected approach, identify its dominant signal from these categories:
  • General synthesis, multi-source aggregation
  • Fast targeted retrieval, known-solution space
  • Quantitative datasets, benchmarks, numerical evidence
  • Peer-reviewed or scientific evidence
  • Emerging-tech trajectory, trend analysis
• For each signal that applies, pick the available agent whose name/description best matches; if multiple candidates fit, prefer the most specific; if none fit, use the most general research-synthesis agent available
• MUST always include at least one general research-synthesis agent to anchor the roster
• List chosen agent per approach (using its actual subagent_type value) + one-line rationale BEFORE dispatch
• If confident in every pick → dispatch immediately
• If uncertain about any pick → confirm roster with user before dispatch; allow swaps

• Send single message with multiple Agent tool calls (one per approach)

• Each agent receives:

  • Original confirmed problem statement
  • The specific approach assigned to them
  • All known constraints and success criteria
  • Output format instruction (see below)
  • "Do NOT post findings to any external system. Report in chat only."

• Required deep agent output format:

  Approach: <name>
  
  Summary: <2-3 sentences>
  
  Pros:
  - <point>
  
  Cons:
  - <point>
  
  Key Evidence / Citations:
  - <source or concrete reference>
  
  Fit Rating: <High / Medium / Low> — <one sentence rationale>
  

• Cap each agent response under 500 words

• After all deep agents return, produce consolidated report
• If the workflow returns stats.researched < stats.requested, MUST name the approaches that failed and ask whether to re-run them before presenting
• Comparison table:

  | Approach | Pros | Cons | Fit |
  |---|---|---|---|
  

• Conflicts section — where agents cite contradicting evidence; state each side
• Recommended approach — prioritized pick with rationale; cite supporting evidence
• MUST cite sources where agents returned citations
• MUST present report then stop; ask user which direction to pursue
• MUST NOT begin implementation

• MUST gather and confirm problem context before any agent dispatch
• MUST clarify ambiguity before shallow scan
• MUST confirm shallow results with user before deep dispatch
• MUST dispatch deep research via the explore-deepdive workflow when the Workflow tool is available; else dispatch deep agents in parallel (single message, multiple tool uses)
• MUST keep all findings local in chat only
• MUST NOT post to Slack, GitHub, Jira, or any external system unless user explicitly authorizes
• MUST NOT implement chosen approach — recommendation only
• MUST NOT proceed through any gate without explicit user confirmation
• If zero suitable research-synthesis agents exist in environment → MUST stop and tell user; never inline-impersonate a researcher