Forward Deployed
ChatGPT pluginForward-deployed AI skills
Develop valuable AI use cases and carry them from customer discovery into design, engineering delivery and adoption. Forward Deployed provides 16 skills for FDEs, deployment leads, consultants and builders, covering workflow redesign, solution architecture, enterprise integration, context engineering, evaluation, engineering handoffs and value measurement. In supported ChatGPT hosts, keep the engagement beside the conversation, select a source or requirement to give ChatGPT precise context, and mention saved records from the composer. Native forms help review proposed changes and configure experiments. Compare before and after values and see which requirements, delivery work and rehearsals a change affects. Field Lab tests a declared workflow and lets you branch from a recorded step. Try an editable AI deployment sample without saving; sign in when you choose to save work. Export to portable files or prepare an engineering handoff for your Git repository. Host feature availability varies; workspace controls provide a fallback. The skills also work independently with your existing tools.
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Business & operations1,544th of 1,617 by placement
| # | Plugin | Position | 7d |
|---|---|---|---|
| 1542 | #8450 | — | |
| 1543 | #8453 | — | |
| 1544 | #8454 | — | |
| 1545 | #8459 | — | |
| 1546 | #8460 | — |
Example prompts
- “Develop valuable AI service options from these discovery notes and recommend what to build.”
- “Open the engagement beside our conversation and let me review this design change in a form.”
- “Use Field Lab to test the proposed AI workflow, then branch an experiment with different conditions.”
Skills
adoption-and-scale
Help people develop, use and spread valuable AI work through existing products or deployed workflows. Use for hands-on team enablement, reusable working practices, adoption diagnosis, changed responsibilities or expansion across teams and organizations.
capability-exploration
Explore current products, enterprise systems, models, developer interfaces, and available host tools to discover and test capabilities that improve an AI solution. Use for open-ended technical exploration, platform or model choices, buy/adapt/build decisions, and finding a maintainable way to deliver a specific workflow.
context-design
Design and improve the information an AI workflow uses to reason, investigate, and act. Use for enterprise knowledge and document interpretation, retrieval, long-context work, task memory, changing business state, or failures caused by missing, misleading, inaccessible, or poorly organized context.
discovery
Investigate enterprise work, systems and expert judgment to discover what an AI solution should change. Use for operator research, technical discovery, workflow redesign, workshops or diagnosis of an inherited process.
enterprise-engagement
Develop an enterprise AI engagement and customer point of view. Use for technical pursuits, first conversations, opening decks, engagement proposals, collaborative scoping and sponsor alignment, or adapting commitments when the work changes.
enterprise-integration
Design and investigate how an AI workflow operates across enterprise systems, business objects, identities, and teams. Use for integration choices, engineering contracts, domain meaning, asynchronous business actions, or diagnosis of a solution that reads correctly but cannot reliably complete work.
evaluate-and-improve
Test whether an AI workflow produces useful business outcomes, diagnose where it fails, and improve the right part of the system. Use for feasibility proofs, candidate comparisons, release decisions, regressions, production incidents, and disappointing adoption or economics.
executive-communication
Create a compelling FDE engagement deck, solution walkthrough, executive decision memo or technical narrative. Turn substantive analysis into a clear point of view and an artifact the audience can use.
field-productization
Turn field-built AI capabilities and deployment learning into reusable products, platform improvements, or delivery practices. Use to choose durable abstractions, separate customer variation from common behavior, improve deployment economics, and move supported capability beyond a bespoke engagement.
get-started
Advance a forward-deployment assignment for a team or organization, or choose the relevant Forward Deployed skill. Use when the requested result spans customer understanding, AI-enabled work, opportunity and solution design, engineering handoff, delivery, adoption or value.
meeting-synthesis
Synthesize meetings and notes from an AI adoption or forward-deployment engagement into findings, decisions, solution implications and useful follow-through. Use for engagement readouts, discovery synthesis, use-case updates and preparation for the next customer or delivery conversation.
opportunity-design
Invent and develop valuable AI-enabled workflows and services for teams and organizations. Use to explore what AI could make possible, expand a narrow automation idea, turn discovery into concrete opportunities, or develop a chosen idea into a credible first experience.
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solution-design
Design a coherent AI-enabled workflow or service, from user experience through reasoning, context, tools, state and operation. Use for whole-solution architecture, a consequential design tradeoff, an inherited solution redesign or an engineering-ready specification.
technical-delivery
Coordinate an enterprise AI deployment and equip the engineers or coding agents implementing it. Use for engineering briefs, customer and partner delivery, dependencies, changed requirements, credible forecasts, rollout, technical transfer, or recovery of a stuck engagement.
use-case-portfolio
Choose what to fund, test or build first among AI and workflow opportunities. Use for investment recommendations, pilot selection, use-case portfolios, shared-capability decisions and sequencing work against available delivery and operating capacity.
value-case
Build and test the economic case for an enterprise AI capability, investment, or expansion. Use for new services, decision quality, expert capacity, operating economics, and value realization when the architecture and changed work determine the return.
- Categories
- Made by
- Rahil Banthia · Website
- Sign-in
- On Install
- Capabilities
- Analyze, Plan, Write
- Availability
- Available
- Version
- 0.6.3
- Keywords
- fde, forward-deployed, forward-deployed-engineering, forward-deployed-engineer, ai-deployment, enterprise-ai, agent-skills, ai-agents, ai-consulting, discovery, solution-architecture, enterprise-integration, context-engineering, agent-evaluation, ai-adoption, claude-cowork, claude-code, chatgpt, codex
- Created
- October 10, 2026
- More info
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