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.
Adopted by
Most recently taken up by
- blovett80/synero-mcptheir own version, first seen Nov 8, 2026
- Protoduct-AI/ledgie-plugintheir own version, first seen Nov 8, 2026
- chatmaxima-com/chatmaxima-skillstheir own version, first seen Nov 8, 2026
- pschlieker/getbacklanetheir own version, first seen Nov 8, 2026
- LeadMagic/leadmagic-claude-plugintheir own version, first seen Nov 3, 2026
- freigent/freigent-plugintheir own version, first seen Nov 1, 2026
Other versions23 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 23
- glifxyz/glif-mcp-server★ 213same name
- different-ai/openwork-hub★ 35same name
- gomarble-ai/marketing-agent★ 15same name
- katalon-labs/true-skills★ 10same name
- PicsArt/gen-ai-skills★ 6same name
- Marky-Team/marky-skills★ 5same name
- multiples-hq/business-state-machine-mcp★ 2same name
- rams-design/rams-plugin★ 2same name
- asimokat/imzala-eklenti★ 1same name
- freigent/freigent-plugin★ 1same name
13 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository15 other files it gives its agents.
- adoption-and-scaleSkill
- capability-explorationSkill
- context-designSkill
- discoverySkill
- enterprise-engagementSkill
- enterprise-integrationSkill
- evaluate-and-improveSkill
- executive-communicationSkill
- field-productizationSkill
- meeting-synthesisSkill
- opportunity-designSkill
- solution-designSkill
- technical-deliverySkill
- use-case-portfolioSkill
- value-caseSkill
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
- Distinctive: line 17
Use the relevant skill directly.
- Distinctive: line 41
Look for the larger change behind a request.
“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.
Files
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 advance | Skill |
| Shape the customer conversation and engagement | enterprise-engagement |
| Find where expertise, information and handoffs constrain the work | discovery |
| Turn conversations into design changes and follow-through | meeting-synthesis |
| Invent valuable AI workflows and services | opportunity-design |
| Choose and sequence AI investments | use-case-portfolio |
| Work through the economics and value mechanism | value-case |
| Explore current products, models, tools and deployment options | capability-exploration |
| Design the experience, intelligence and architecture | solution-design |
| Connect enterprise systems and enable reliable actions | enterprise-integration |
| Give the agent useful business knowledge and continuing context | context-design |
| Test capabilities, diagnose failures and improve performance | evaluate-and-improve |
| Turn the design into an engineering handoff and working release | technical-delivery |
| Develop workforce practice and spread successful deployments | adoption-and-scale |
| Make the solution or investment clear to an executive audience | executive-communication |
| Turn deployment learning into reusable product capability | field-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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

