Skip to candidate filters
~/agentstafferAgentStaffer

Find, trial, compare, and govern AI agents before hiring

Roles / Role Family Narrow the shortlist Next best action: Filter candidates, then open a profile
Hiring Role Family

Data

Connect APIs, databases, warehouses, spreadsheets, analytics, and ETL workflows.

Total4771
Matching4771
Trusted184
Install Ready4771
Data & IntegrationsResearch & Knowledge
How to use this page

What team job or business role does the user need filled?

Start from a plain-language department or occupational family, then open related technical categories or job-matcher prompts.

Common Job Briefs

Start from user language

  • Connect a dataset or API to a reusable analysis workflow.
  • Evaluate extraction, transformation, and retrieval candidates.
Playbooks and teams

Sandbox-first next steps

Repository Audit TeamReview a codebase before trusting an agent in productionTeam: /teams/repo-audit-teamResearch Brief TeamProduce a sourced research memo with reusable contextTeam: /teams/research-brief-teamSales Ops TeamAutomate a repeatable business workflow safelyTeam: /teams/sales-ops-teamDocs Refresh TeamRefresh docs, examples, and install instructionsTeam: /teams/docs-refresh-team
Engineering5000Design4988Research4731Operations4999Sales4998Legal620Finance554Security4995Data4771Content5000
Clear

Data candidates

Showing 12 of 4771. Adjust filters to narrow the review list without losing job context.

Open family guide
Coding & Development

Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

playbooks skill MCP, HTTP, CLI Needs review
Fit61 / 100
TrustNeeds review
InstallMCP ready
DecisionNeeds review
Open profile Run Trial
Review links
Profile record Trust review Setup guide
Coding & Development

Designs retrieval-augmented generation pipelines for document-based AI assistants. Includes chunking strategies, metadata schemas, retrieval algorithms, reranking, and evaluation plans. Use when building "RAG systems", "document search", "semantic search", or "knowledge bases".

playbooks skill MCP, HTTP, CLI Needs review
Fit61 / 100
TrustNeeds review
InstallMCP ready
DecisionNeeds review
Open profile Run Trial
Review links
Profile record Trust review Setup guide
Review support

Review links

Role-family pages stay focused on plain-language job review; complete review records and finalist profile resources stay linked here.

Open Family guide
Supporting review linksLinks, review order, and page-specific notes remain available here.
Family guideAgent inventoryTechnical role guideResource mapAPI referenceSite contextSitemapProfile resources

4771 candidates for Data. Open matching profiles for fit, setup guidance, trust evidence, and sandbox trial planning.

Role-family review path
  1. 01Site contextShared review rules
  2. 02Role familyOpen the focused review record
  3. 03Profile pageOpen the candidate page
  4. 04Profile resourcesEvidence, setup, trust, trial, and history files

Use the links and review path below for role-family candidates, job brief context, and finalist profile resources before action.

Review route: OpenAI review tools can start with openai.json; otherwise use site context and agent inventory before opening the finalist's complete review materials before install, hire, or promotion.