Skip to trial filters
~/agentstafferAgentStaffer

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

Trials Trial safely Next best action: Run a sandbox trial
Trials

Trial work queue

A compact sandbox trial queue for choosing a safe first task, checking fit and trust, and opening the full review package before any local run. Use this before hiring community-sourced agents into production workflows.

13available profiles
Filter optionsRole, protocol, trust, setup, and sorting stay available here.
First queue itemiflow-mcp-sixhq-overtureReview trust and setup, then run a small code review trial.Review preflight
Setup coverage12 guided / 0 manualAcross 12 candidates on this page. A setup path is not safety approval; trust review and a sandbox trial are still required.Setup guide
Recent evidence0 runsNew sandbox evidence appears in the run ledger.Evidence queue
#1 trial priority · npm / Coding & Development
Fit
66 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

MCP Server for Claude Code, Cursor, Cline, Copilot, Github Copilot, Windsurf - Visual AI Agent Plan Execution, Approval Workflow, Plan Visualization, Agent Orchestration. See what your AI is thinking before it writes code. Works with Claude, GPT, Gemini,

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • Node.js and npm: Required to run iflow-mcp-sixhq-overture.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#2 trial priority · npm / Coding & Development
Fit
66 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

MCP Server for Claude Code, Cursor, Cline, Copilot, Github Copilot, Windsurf - Visual AI Agent Plan Execution, Approval Workflow, Plan Visualization, Agent Orchestration. See what your AI is thinking before it writes code. Works with Claude, GPT, Gemini,

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • Node.js and npm: Required to run overture-mcp.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#3 trial priority · explainx / Coding & Development
Fit
61 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

Weave is a lightweight toolkit for tracking and evaluating LLM applications.

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • MCP-capable client: Use Claude Desktop, Cursor, VS Code, or another client that can load MCP server configs.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#4 trial priority · playbooks / Coding & Development
Fit
61 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

This skill helps you track ML experiments and artifacts across platforms like MLflow and Weights & Biases to improve reproducibility and comparison.

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • MCP-capable client: Use Claude Desktop, Cursor, VS Code, or another client that can load MCP server configs.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#5 trial priority · playbooks / Coding & Development
Fit
61 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

This skill helps you track ML experiments, visualize training, sweep hyperparameters, and manage models using Weights & Biases for streamlined MLOps.

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • MCP-capable client: Use Claude Desktop, Cursor, VS Code, or another client that can load MCP server configs.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#6 trial priority · playbooks / Coding & Development
Fit
61 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

This skill helps you monitor, analyze, and compare Weights & Biases training runs to detect failures and optimize performance.

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • MCP-capable client: Use Claude Desktop, Cursor, VS Code, or another client that can load MCP server configs.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
Show 6 more trial candidatesAdditional candidates stay folded for scanning; trials.json and card resource links keep the full review path.
#7 trial priority · mcpservers / Coding & Development
Fit
65 / 100
Trust
Needs review
Setup
MCP ready
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

Azure Resource Manager SDK for deploying and managing Weights & Biases ML experiment tracking instances via Azure Marketplace.

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • MCP-capable client: Use Claude Desktop, Cursor, VS Code, or another client that can load MCP server configs.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#8 trial priority · remoteopenclaw / Coding & Development
Fit
52 / 100
Trust
Needs review
Setup
Source guide
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

Primary W&B skill for broad or mixed Weights & Biases work: project overviews, W&B runs and artifacts, Weave traces and evaluations, Reports, Signal Builder, and Launch workflows. Use when the task spans multiple W&B surfaces or the user asks generally what is happening in a W&B project.

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • License review: License metadata is missing from the current public profile.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#9 trial priority · claudemarketplaces / Coding & Development
Fit
45 / 100
Trust
Needs review
Setup
Source guide
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

This lets Claude integrate Weights & Biases experiment tracking into your ML workflows without you having to look up the API docs every time. You get automatic metric logging, real-time training dashboards, hyperparameter sweeps, and model registry management. The skill comes from a template collection that's been installed 365 times and covers the core W&B...

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • License review: License metadata is missing from the current public profile.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#10 trial priority · claudemarketplaces / Coding & Development
Fit
39 / 100
Trust
Needs review
Setup
Source guide
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

ml-pipeline enables ML engineers and data scientists to design and implement production-grade machine learning infrastructure by configuring experiment tracking systems (MLflow, Weights & Biases), orchestrating training workflows with tools like Kubeflow and Airflow, building feature stores using Feast, and automating model validation and retraining process...

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • License review: License metadata is missing from the current public profile.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#11 trial priority · claudemarketplaces / Coding & Development
Fit
31 / 100
Trust
Needs review
Setup
Source guide
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

This is your go-to when you need to build production ML infrastructure that actually scales. It covers the full MLOps stack: Kubeflow and Airflow for orchestration, MLflow and Weights & Biases for experiment tracking, plus cloud-specific tooling across AWS SageMaker, Azure ML, and GCP Vertex AI. You also get Kubernetes deployment patterns, feature stores li...

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a small code review trial
Input
Give the agent one low-risk issue or one small pull request and ask for defects, tests, and a patch plan.
Expected output
A concise review with reproducible findings, changed-file scope, and test suggestions.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • License review: License metadata is missing from the current public profile.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON
#12 trial priority · awesomeagents / Workflow Automation
Fit
27 / 100
Trust
Needs review
Setup
Source guide
Before run
1 check
Top risk

No community evaluationAgentStaffer users have not submitted a review yet.

Full readiness snapshotCandidate summary and risk context stay available here.

W&B Traces enhances AI agent observability by providing intuitive visualizations for debugging LLMs, allowing practitioners to review past results, debug errors, and gain insights into model behavior website

Top risk

No community evaluation: AgentStaffer users have not submitted a review yet.

Trial task Run a dry-run workflow trial
Input
Connect the workflow to mock inputs and ask it to produce a trace without touching production systems.
Expected output
A step-by-step run log with inputs, outputs, failures, and rollback notes.
Permissions & setup
  • Sandbox workspace: Run the first trial in a throwaway directory or isolated environment.
  • License review: License metadata is missing from the current public profile.
Promotion gates
  • Clear Install path reviewed
  • Hold Trust evidence reviewed
Guardrails
  • Install asks for production credentials or broad write access.
  • Source or package cannot be verified from profile evidence.

Create evidence with POST /api/actions/run-trial. Keep the card short; open the full checklist in the trial JSON.

Review & run trial Open profile
Review links
Profile JSON Trial JSON Setup JSON Trust JSON

Recent Trial Runs

RunResultScoreRolePromotionDetails
No public trial runs have been created yet. Pick a candidate above, open its profile, then use Run Trial or POST /api/actions/run-trial with the candidate listingId. New evidence appears here and in the trial guide.
Review support

Review links

Trial candidates and recent runs stay first; complete review records and finalist profile resources stay linked here.

Open Trial queue JSON
Supporting review linksLinks, review order, and page-specific notes remain available here.
Trial queue JSONEvidence requests JSONPromotion queue JSONGEO exposure JSONAgent visibility JSONResource catalog JSONSite context TXTSitemap XMLProfile package JSON
Trial review paths
Trial review path
  1. 01Site contextShared review rules
  2. 02Trial queueOpen 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 trial queue context, evidence requests, 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.