How to Create the Repogenic Loop
How to Create the Repogenic Loop
Software teams already have a starting point for most features.
Usually, it is a Jira story, Azure DevOps work item, ServiceNow request, backlog item, or some other form of business requirement.
The problem is that those stories rarely contain enough engineering context to safely drive implementation on their own.
That is where the Repogenic Loop begins.
The basic idea is simple:
Put the work request into the repo, let the repo turn it into engineering intent, and let that intent drive implementation, validation, evidence, and delivery.
Instead of treating the repository as a place where code is stored after decisions are made somewhere else, the Repogenic Loop makes the repository the active engineering environment where the feature is interpreted and executed.
Step 1: Bring the Story Into the Repo
Start with the source requirement.
That might be a Jira story with:
a description
acceptance criteria
business rules
screenshots
API references
links to supporting documentation
Store that request in the feature branch as something simple such as:
/features/ABC-123/feature.md
The feature.md file does not need to be perfect.
It is the raw source of truth for what the business is asking the team to do.
The important shift is that the feature request is now inside the same environment as the code that must satisfy it.
Step 2: Use a Skill to Create the Engineering Intent
The next step is not to immediately generate code.
The first automation should be a repo-native skill that reads the feature request and turns it into a usable engineering intent.
For example:
/skills/create-intent-from-feature.md
That skill can read much more than the Jira story.
It can also inspect:
architecture documentation
existing code
existing contracts
tests
repo conventions
security requirements
hooks
templates
similar implementations already in the codebase
This is where the Repogenic Loop becomes significantly more powerful than a generic prompt or external specification generator.
A generic generator understands the request.
A repo-native skill understands the request in the context of the actual system.
It might discover that the requested feature already partially exists.
It might discover that a new microservice is unnecessary.
It might identify an existing contract that should be extended rather than replaced.
It might discover missing acceptance criteria or a conflict with an architectural standard.
The goal is not merely to rewrite the story.
The goal is to create the smallest valid engineering intent for this repository.
Step 3: Validate Before Implementation
The skill should not automatically assume that every story is ready to build.
A useful Repogenic Loop includes a simple decision:
↓
repo analysis
↓
Is the request clear enough?
If the answer is no, the process stops and surfaces:
missing information
conflicting requirements
architectural concerns
unclear success criteria
assumptions that need confirmation
That matters because one of the worst things automation can do is turn ambiguity into code faster.
The loop should reduce ambiguity before implementation begins.
Step 4: Produce One Intent or Progressive Intents
Once the work is sufficiently clear, the skill creates the appropriate intent structure.
A small feature may only need:
A larger feature might generate progressive intents:
/intents
The complexity of the intent should match the complexity of the work.
The team should not be forced through unnecessary ceremony.
The intent exists to create a clear engineering boundary, not documentation for documentation’s sake.
A good intent defines things such as:
desired outcome
relevant inputs
expected outputs
constraints
success criteria
required evidence
That becomes the control surface for the implementation.
Step 5: Execute Inside the Repo
Once the intent exists, the implementation loop begins.
The AI runner can now work with far richer context than it had from the original Jira story.
It can:
implement the change
run tests
compare the implementation against the intent
call repo skills
trigger hooks
inspect logs or other evidence
refine the implementation
stop when success criteria are satisfied
The flow becomes:
Implement
↓
Validate
↓
Test
↓
Observe
↓
Refine
↓
Validate again
This is the active portion of the Repogenic Loop.
The repository is no longer passive storage.
It contains the rules, context, automation, evidence, and intent required to perform the work.
Step 6: Create Evidence and Move to PR
The output of the loop should not simply be code.
It should also provide evidence that the requested outcome was achieved.
That may include:
passing tests
coverage results
security validation
API contract verification
screenshots
logs
generated reports
links back to the original feature
confirmation that success criteria were met
By the time the pull request is created, reviewers have more than a diff.
They have traceability:
Story
↓
Feature
↓
Intent
↓
Implementation
↓
Evidence
↓
Pull Request
That makes the change easier to understand, easier to review, and easier to govern.
Why Convention Over Configuration Matters
The Repogenic Loop favors convention over configuration.
The team should not decide how the engineering process works every time a new story arrives.
The repository should already establish conventions such as:
/features
/intents
/skills
/hooks
/templates
/evidence
A developer entering the repository should be able to understand:
This is how work is done here.
That consistency matters even more when different developers are using different AI runners.
One developer might use Claude.
Another might use Copilot.
Another might use Gemini or Cursor.
The model may change.
The operating convention should not.
The repo provides the consistent engineering boundary.
This is also why the Repogenic Loop is different from maintaining a large library of “perfect prompts.”
Instead of configuring every AI conversation individually, the team establishes the workflow once and lets every feature inherit it.
Configure Only What Should Vary
Convention does not mean that everything should be hard-coded.
Some things genuinely vary between organizations and repositories.
Those may include:
required test coverage
security policies
approved LLM endpoints
naming conventions
architecture rules
evidence requirements
deployment environments
MCP services
enterprise shared services
Those should be explicit configuration.
A useful way to describe the model is:
Convention defines the workflow. Configuration defines the local rules. AI handles the reasoning.
That is a much more scalable engineering model than configuring each interaction independently.
The Repo Becomes the Engineering System
The real significance of the Repogenic Loop is not the Markdown files.
It is the shift in where engineering intelligence lives.
Instead of spreading context across:
Jira
Confluence
prompt libraries
developer memory
chat sessions
architecture diagrams
disconnected automation systems
the most important working context begins to converge inside the repository.
The repo contains the feature request.
The repo contains the standards.
The repo contains the skills.
The repo contains the hooks.
The repo contains the intent.
The repo contains the implementation.
The repo contains the evidence.
That creates continuity from the original request all the way through delivery.
The Repogenic Loop
At a high level, the entire process looks like this:
Jira Story
↓
↓
Repo-aware Intent Skill
↓
Clarify if Necessary
↓
intent.md or Progressive Intents
↓
Implement
↓
Validate
↓
Test
↓
Observe
↓
Refine
↓
Evidence
↓
Pull Request
↓
Merge
And then the next feature enters a repository that is slightly richer than before.
More patterns exist.
More evidence exists.
More reusable skills exist.
More context exists.
That is why I call it the Repogenic Loop.
The repository becomes better at producing the next generation of software work.
The guiding principle remains simple:
Structure the boundary, not the reasoning.
Give the AI a strong engineering environment.
Give it the feature.
Give it the standards.
Give it the success criteria.
Then let it reason inside those boundaries.
That is where repo-native Intent-Driven Engineering becomes practical.
This one is strong enough to stand as the core explanatory article for the Repogenic Loop. I can also tighten it into a shorter LinkedIn version without losing the process.
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