# Explore

> >

- **Type:** Skill
- **Install:** `agentstack add skill-marcoskichel-empire-explore`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [marcoskichel](https://agentstack.voostack.com/s/marcoskichel)
- **Installs:** 0
- **Category:** [Agent Skills](https://agentstack.voostack.com/c/agent-skills)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [marcoskichel](https://github.com/marcoskichel)
- **Source:** https://github.com/marcoskichel/empire/tree/main/plugins/empire-research/skills/explore

## Install

```sh
agentstack add skill-marcoskichel-empire-explore
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

- 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. 
       

    2. 
       ...
    ```

  - 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: 

  Summary: 

  Pros:
  - 

  Cons:
  - 

  Key Evidence / Citations:
  - 

  Fit Rating:  — 
  ```

- Cap each agent response under 500 words

- After all deep agents return, produce consolidated report
- If the workflow returns `stats.researched 

- 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

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [marcoskichel](https://github.com/marcoskichel)
- **Source:** [marcoskichel/empire](https://github.com/marcoskichel/empire)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-marcoskichel-empire-explore
- Seller: https://agentstack.voostack.com/s/marcoskichel
- Browse the marketplace: https://agentstack.voostack.com/browse

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Listed on AgentStack — the marketplace for AI agent skills and MCP servers. Every listing is security-reviewed. Creators keep 70%.