agentsclimarketplace

Agent deployment

Skill agent-blueprint/agent-blueprint-skills/plugins/agent-blueprint-skills/skills/agent-deployment

Open-source AI agent skills for enterprise deployment. ServiceNow AI Agent Studio, deployment methodology, research patterns. Claude Code plugin + Codex + any agent.

Install
npx -y skills add agent-blueprint/agent-blueprint-skills --skill agent-deployment

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 2 stars2 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Vendor-agnostic AI agent deployment methodology for implementation plans, rollout strategy, and pilot planning. Phased rollout, pilot-first approach, test gates, access tier adaptation, and progress tracking. Works with any enterprise platform (ServiceNow, Salesforce, custom). Use when deploying AI agents, planning agent rollouts, or building implementation plans. Triggers: agent deployment, deploy agents, implementation plan, rollout strategy, pilot plan, agent testing, phased deployment.

The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

6.1 KB, as published. Nobody here has run it

AI Agent Deployment Methodology

A vendor-agnostic methodology for deploying AI agents in enterprise environments. Designed to work with any platform. Focused on reducing risk through phased rollout, pilot-first validation, and structured test gates.

This skill defines the methodology. For platform-specific knowledge, load a companion skill (e.g., servicenow-ai-agents for ServiceNow).

Before You Start

  1. Read the blueprint or spec. Understand the agent architecture: how many agents, what tools they need, how they interact, what business problem they solve.

  2. Check for a platform-specific skill. If one exists, load it for platform-specific setup, data model, and configuration patterns.

  3. Check for prior work. Look for deployment history, progress files, or implementation state from previous sessions. Don't repeat work.

Access Tier Adaptation

Adapt your approach based on what access the user provides. Don't present access tiers as a menu. Respond naturally to what the user gives you.

  • Full access (read + write): Execute steps directly on the target platform. Create records, write scripts, configure integrations, test. Act first, report after.

  • Read-only access: Connect to discover schemas and read existing config. For steps that create or modify records, generate all records and scripts as local files and walk the user through creating them. After the user acts, verify by reading back from the platform.

  • No access: Work from the spec and skill knowledge only. Generate everything as local files with step-by-step instructions. Ask the user to share results (screenshots, IDs, error messages) after each action.

Shift fluidly within any access level. Even with full access, some tasks require the user to act (UI-only config, credential vaults, approval workflows).

Deployment Sequence

Start with the pilot. Deploy only Phase 1 agents first. Validate end-to-end before expanding.

Step 1: Read the implementation roadmap

The blueprint's implementation plan defines what gets built in each phase. Identify the Phase 1 agents exactly as specified — typically one orchestrator and one worker. Do not add agents from later phases. If the blueprint has no phased roadmap, pick the single agent with the clearest business value and fewest dependencies.

Step 2: Set up platform access

Follow the platform skill's setup instructions if one is loaded. Otherwise, configure access using your own knowledge of the target platform.

Step 3: Discover the target environment

Explore schemas, existing config, and platform version before creating anything. Never assume you know the schema. Query it.

Step 4: Create Phase 1 records

Agents, teams, workflows, tools, and linking records. Create only the agents specified for Phase 1 in the implementation plan. If you are about to create more worker agents than the plan specifies for this phase, stop and confirm with the user before proceeding.

Note: the blueprint may define a Manager/Orchestrator agent alongside Workers. On most platforms, the Manager maps to the native orchestration layer (workflow, pipeline, coordinator), not a separate agent record. Check the platform skill for the exact entity mapping.

Step 5: Organize records

Move records into the appropriate scope or project structure.

Step 6: Verify deployment

Confirm all records exist and are correctly configured.

Step 7: Implement tool scripts

Start with simulation scripts to validate the LLM orchestration (tool chain, parameter passing, sequencing). This is step 1 of 2. See references/DEPLOYMENT_PATTERNS.md for why simulation-first matters.

Step 8: Configure security

Set up access controls appropriate for the platform. VERIFY security is actually configured. Do not assume success. Query the platform to confirm.

Step 9: TEST BEFORE EXPANDING (gate, not optional)

Invoke the pilot agent programmatically. Read the full execution trace. Verify each tool produced output. If any tool returns null, the script format is wrong. Fix before proceeding. Re-test until the full tool chain completes. Do NOT proceed to step 10 until the pilot passes.

Step 10: Make the pilot real

Replace simulation scripts with real implementations that connect to actual data sources, APIs, and platform tables. See references/DEPLOYMENT_PATTERNS.md for the progression pattern.

Step 11: Expand to next phase

Do not build Phase 2 agents before Phase 1 is validated with real data. After Phase 1 passes end-to-end with real integrations, proceed to Phase 2 agents as defined in the implementation plan. Each new agent follows the same simulation-to-real progression (Steps 7-10).

Maintaining Progress

Keep a progress file updated throughout the engagement. Update after each milestone:

  • Environment details: platform, version, environment, project/scope ID
  • What's deployed: agents, tools, workflows created (with IDs)
  • What's left: remaining phases, pending work
  • Lessons learned: platform-specific behavior, things that failed and why
  • User decisions: choices that override blueprint defaults

References

  • DEPLOYMENT_PATTERNS.md -- Why simulation-only pilots fail, the pilot progression pattern, and test strategy guidance.

Built by Agent Blueprint -- AI advisory for enterprise agent deployment.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.