engineering-principles
Skill22 starsChanged 10 days agoMITView on GitHub
GPT-RAG MCP architecture and implementation principles. Use for design, review, meaningful refactoring, Azure integration, security, protocol, testing, or operational changes.
22 other projects use a version of this, 8 of them edited.
Adopted by
Most recently taken up by
- SwiftyJunnos/skillstheir own version, first seen Nov 8, 2026
- ericwang200608-beep/LANYANT-Skillstheir own version, first seen Nov 8, 2026
- paureis/pau-skillstheir own version, first seen Nov 8, 2026
- the-inconvenience-store/skillstheir own version, first seen Nov 6, 2026
- Azure/gpt-rag-mcpword for word, first seen Nov 2, 2026
- sreejithraman/SreeStacktheir own version, first seen Nov 1, 2026
Used with
Projects with it also use how, why and 8 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
jaydubya818Skillhow
Use for runtime, ownership, layering, code-walkthrough, and placement questions.
Also in 3 projects that use this
- jaydubya818Skill
why
Use when design rationale must be recovered from source history and available records.
Also in 3 projects that use this
MicrosoftSkillwriting-for-agents
Writing documents for agents. Use when creating or editing skills, or modifying AGENTS.md or CLAUDE.md.
Also in 4 projects that use this
GoogleSkillcodebase-design
Shared vocabulary for designing deep modules. Use when the user wants to design or improve a module's interface, find deepening opportunities, decide where a seam goes, make code more testable or AI-navigable, or when another skill needs the deep-module vocabulary.
Also in 4 projects that use this
- GoogleSkill
domain-modeling
Build and sharpen a project's domain model. Use when the user wants to pin down domain terminology or a ubiquitous language, record an architectural decision, or when another skill needs to maintain the domain model.
Also in 4 projects that use this
- +5 more from google
Other versions22 projects use a version of this.
Word for word · 1
- Azure/gpt-rag-mcp★ 22100% same
Edited · 8
- Azure/agent-landing-zone★ 1.2k36% same
- Azure/GPT-RAG★ 1.2k36% same
- Azure/agent-app-ingestion★ 18825% same
- Azure/gpt-rag-ingestion★ 18825% same
- Azure/agent-app-orchestrator★ 10730% same
- Azure/gpt-rag-orchestrator★ 10730% same
- Azure/agent-app-ui★ 1017% same
- Azure/gpt-rag-ui★ 1017% same
Wrote their own version · 13
- Azure/bicep-ptn-aiml-landing-zone★ 19same name
- EnsoDynamics/enso-method★ 6same name
- M4n5ter/skills★ 4same name
- cypher0n3/dotagents★ 3same name
- ericwang200608-beep/LANYANT-Skills★ 2same name
- paureis/pau-skills★ 2same name
- andrewclark88/skills★ 1same name
- nb-99/skillektion★ 1same name
- sreejithraman/SreeStack★ 1same name
- SwiftyJunnos/skills★ 1same name
3 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 repository7 other files it gives its agents.
- AGENTS.mdAGENTS.md
- .github/copilot-instructions.mdCopilot instructions
- architecture-decisionSkill
- component-releaseSkill
- documentation-consistencySkill
- mcp-capability-developmentSkill
- releaseSkill
The file
We split this skill into the rules it gives an agent
1 rule. 0 are common, 1 is distinctive, and 0 are very specific to this project.
0
Common
Most of the 20 edited versions we read keep these.
1
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 18
Use these principles as design questions rather than dogma.
“Common” is measured: kept by at least 6 in 10 of the 20 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
GPT-RAG MCP engineering principles
Load only the references needed:
| When the task involves | Read |
| Repository purpose, ownership, or runtime boundaries | MCP server architecture |
| Tools, resources, prompts, schemas, or protocol compatibility | MCP contracts and safety |
| Python design, async behavior, or maintainability | Python and async |
| Identity, secrets, deployment, networking, or operations | Security and operations |
| Tests, validation, compatibility, or evidence | Testing and evidence |
Use these principles as design questions rather than dogma. Task requirements, the current implementation, executable configuration, and protocol contracts remain the sources of truth.
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Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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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.