# Agent Use

> Classifies user questions and routes to the right Red Hat agentic interface — Sales Assistant (shadowbot.io), Dataverse Agent (agent.dataverse.redhat.com), or Dataverse MCP skills in Cursor. Pulls capability context from The Source. Use when the user asks which agent to use, wants to query Sales Assistant or Dataverse Agent, or needs help routing sales vs data questions.

- Skill: `cgray-redhat/agent-use` (Agent Skill, multi-file: 4 files)
- Install (CLI): `npx skillmds@latest add cgray-redhat/agent-use`
- Raw SKILL.md: https://api.skillmd.com/api/skills/cgray-redhat/agent-use/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: cgray-redhat (https://skillmd.com/u/cgray-redhat)
- Updated: 2026-09-22
- Page: https://skillmd.com/skills/cgray-redhat/agent-use

---


# Agent Use

Classify the user's question, route to the correct agentic interface, and hand off with a crafted prompt — or delegate to an existing Cursor skill when MCP is the better interface.

**Interaction model:** Route + hand off only. Do not automate web agent UIs with Playwright unless the user explicitly requests browser automation.

## Prerequisites

1. Run **refresh-connections** if Dataverse MCP delegation is likely.
2. Read project `dataaccess.md` (if present) when routing data questions to MCP.
3. When routing is ambiguous or docs may be stale, refresh Source context:

```bash
python3 ~/.cursor/skills/agent-use/scripts/fetch_agent_docs.py --format json
```

If the script fails with auth errors, run the **thesource** skill authentication flow first.

## Workflow

```
Agent Use:
- [ ] Step 0: Intake — clarify intent and deliverable
- [ ] Step 1: Classify — sales | enterprise_data | people | access | docs
- [ ] Step 2: Route — hand off to web agent OR delegate to Cursor skill
- [ ] Step 3: Report — rationale, URL, prompt, sources
```

---

## Step 0 — Intake

Use **AskQuestion** when available; otherwise ask in one message.

| Question | Purpose |
|----------|---------|
| What are you trying to accomplish? | Core routing signal |
| Is this about selling/deals/RHSC or enterprise data/analytics? | Primary fork |
| Do you need the answer here in Cursor, or is a web agent chat OK? | MCP vs web agent |
| Any scope filters? (account, geo, time period, person) | Crafted prompt |

**Gate:** Do not route until intent is at least `sales`, `enterprise_data`, `people`, `access`, or `docs`.

---

## Step 1 — Classify

```
people lookup (who is X, title, manager)?
  → dataverse-people-lookup

sales / RHSC / quote / TDP / account plan / competitive / meeting prep?
  → Sales Assistant handoff

enterprise data / pipeline / renewals / bookings / revenue / analytics?
  ├─ user wants answer in Cursor OR needs access check?
  │    → guided-data-analysis
  └─ user wants conversational exploration with charts?
       → Dataverse Agent handoff

access / permissions / "do we have data for X"?
  → guided-data-analysis (Steps 1–3)

"what can agent X do?" / capability question?
  → fetch_agent_docs.py or thesource + explain + handoff
```

See [reference.md](reference.md) for the full routing matrix and example prompts.

---

## Step 2 — Execute or hand off

### Web agent handoff (Sales Assistant or Dataverse Agent)

Present using this template:

```markdown
## Recommended agent: [Sales Assistant | Dataverse Agent]

**Why:** [1–2 sentences tied to Source capabilities]

**Open:** [URL]

**Suggested prompt:**
> [Crafted question with scope, account/geo/time if known]

**Tips:** [workflow library, Memories, Favorites, or starter question]

**Support:** [Slack channel]
```

| Agent | URL |
|-------|-----|
| Sales Assistant | https://shadowbot.io.redhat.com/agents/sales-assistant |
| Dataverse Agent | https://agent.dataverse.redhat.com/ |

### MCP delegation (stay in Cursor)

| Intent | Delegate to |
|--------|-------------|
| Employee title, location, manager | **dataverse-people-lookup** |
| Enterprise data analysis, access checks, SQL | **guided-data-analysis** |
| Auth / connection issues | **refresh-connections** |

Invoke the target skill; do not duplicate its pipeline.

---

## Step 3 — Report

Always include:

- Which interface was chosen and why
- What was executed in Cursor vs handed off
- Source pages consulted (or `reference.md` if Source fetch was skipped)
- Next step if access denied or the wrong agent was chosen

## Quality rules

- Never fabricate agent capabilities; trace to Source excerpts or [reference.md](reference.md)
- Prefer MCP in Cursor for governed data when permission probing matters
- Prefer Sales Assistant for seller workflows; Dataverse Agent for conversational data exploration
- Ask a clarifying question before handoff when intent is ambiguous
- Do not use Playwright for web agents unless the user explicitly requests it

## Example prompts

1. "Help me build a quote for Acme Corp with RHEL and a 15% discount"
2. "What's our renewal pipeline risk in Germany for Q3?"
3. "Which agent should I use to prep for a customer meeting?"
4. "What can Sales Assistant do vs Dataverse Agent?"

## Additional resources

- [reference.md](reference.md) — routing matrix, URLs, example prompts, MCP delegation map