explore | empire-research | ClaudePluginHub

Skill

explore

From empire-research

Enumerates 3-5 candidate approaches via shallow research scan for open-ended problems, enables user-selected deep dives, parallel research, and consolidated comparisons with recommendation.

developer-tools

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npx claudepluginhub marcoskichel/empire --plugin empire-research

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This skill uses the workspace's default tool permissions.

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<section id="purpose-vs-compare">

SKILL.md

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Parent Repo Stars8

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Last CommitMay 5, 2026

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Tags

explore-options

research-approaches

option-analysis

problem-exploration

Help us improve

Share bugs, ideas, or general feedback.

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

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

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

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 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