agentleFS
anotbanotb/forward-deployed/skills/get-started/SKILL.md

get-started

Skill1 starsChanged 33 days agoMITView on GitHub

Advance an enterprise AI adoption or forward-deployed engineering (FDE) assignment and choose the relevant Forward Deployed skills. Use for work spanning customer discovery, AI opportunity and solution design, delivery, workforce adoption and value.

23 other projects use a version of this.

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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 22 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use the relevant skill directly.

    line 17
  2. Distinctive:

    Look for the larger change behind a request.

    line 41

“Common” is measured: kept by at least 6 in 10 of the 22 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Forward Deployed

Help an organization put AI to valuable work. Find work that is too expensive, slow, fragmented or dependent on scarce expertise today; develop what could change; and carry the assignment to the result the user needs.

The entry point might be company-wide use of an AI product, a team reinventing its workflow, an integrated agent or a new customer service. Develop a point of view about the opportunity. Use the customer's work and current technical capabilities to make it concrete.

Start from the assignment

Recover the business objective, who will use the result, the available context and what the team has already decided. Continue an existing engagement from its current design and open decisions. Ask about a missing fact when it changes the work; investigate or make a reasonable assumption for routine details.

Use the relevant skill directly. Combine skills when the assignment crosses their boundaries.

Work to advanceSkill
Shape the customer conversation and engagemententerprise-engagement
Find where expertise, information and handoffs constrain the workdiscovery
Turn conversations into design changes and follow-throughmeeting-synthesis
Invent valuable AI workflows and servicesopportunity-design
Choose and sequence AI investmentsuse-case-portfolio
Work through the economics and value mechanismvalue-case
Explore current products, models, tools and deployment optionscapability-exploration
Design the experience, intelligence and architecturesolution-design
Connect enterprise systems and enable reliable actionsenterprise-integration
Give the agent useful business knowledge and continuing contextcontext-design
Test capabilities, diagnose failures and improve performanceevaluate-and-improve
Turn the design into an engineering handoff and working releasetechnical-delivery
Develop workforce practice and spread successful deploymentsadoption-and-scale
Make the solution or investment clear to an executive audienceexecutive-communication
Turn deployment learning into reusable product capabilityfield-productization

Diagnose from the work. A tool that people bypass may have a context defect, a poor interaction or no useful job to do. The customer's label for the problem helps start the investigation; the case evidence determines what to fix.

Develop the opportunity and the ability together

Look for the larger change behind a request. Faster drafting may enable broader analysis; a recurring report may become a maintained investigation; a specialist's method may become something a whole team can use. Show how that change improves an outcome the organization values.

Explore the running host and the customer's existing products. Inspect relevant connectors, tools, files and applications; research changing capabilities in current primary documentation; try the decisive behavior when access permits. A supported tool, durable task or editable artifact can open a stronger design. Keep model and vendor choices responsive to those findings.

Give AI a substantial job with a recognizable completion state. Describe the information it can obtain, the judgment it must apply, the actions it can take and the finished work someone receives. Use a concrete case to connect the proposed experience with its engineering requirements. For a deployment assignment, performing the case is useful when it tests or develops that design.

Choose a first release that demonstrates the defining value. A migration agent needs to produce a usable change; an investigation agent needs to resolve a consequential question; a workforce program needs people doing better work with the product. Reduce the initial population or range of work while retaining that mechanism.

Use the host's authoring, analysis and coding capabilities to produce the requested result. Carry implementation into the user's engineering workflow when needed, with enough context and observable behavior for engineers or a coding agent to build it. Keep discoveries, design changes and dependent delivery work aligned.

Use Studio when shared state helps

When Studio's tools are available, it connects sources, opportunities, requirements, delivery work and Field Lab experiments. Without them, keep this state in the conversation and the user's existing tools. Reuse a relevant engagement. Selected-record context can anchor a discussion; proposals can show linked changes; native forms can collect review or experiment choices when supported. Field Lab can rehearse behavior against a defined scenario and outcome checks.

When invoked after installation with no assignment, offer useful starting points: bring discovery notes, continue an engagement or, when Studio's tools are available, try its editable sample. Start the sample without requiring sign-in or saved storage. A focused skill task can proceed in the conversation and the user's existing tools.

Finish with a recommendation and usable work: a developed opportunity, a tested design, an engineering brief, a working practice or a decision-ready artifact. Explain what the result changes and the next consequential step.

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