v8.0.0-rc.1 · Release candidate

Your code evolves.
Your AI context should too.

Repository AIOps: audit → reviewable proposal → apply → rescan. Start with the files coding agents already read, such as AGENTS.md.

No account for public previews. Usage and repository-size limits apply. Private repositories require an authorized GitHub connection.

How your code is handled

See how it worksExplore the products

example / checkout-serviceIllustrative example

One convention has drifted.

The example instructions still reference the old API client.

CLAUDE.md
  Use src/lib/legacyClient.ts

Repository evidence
  src/lib/api.ts is the shared client
  legacyClient.ts is no longer present
Your report uses evidence from your repository.
Grounded in repository evidenceChanges you can reviewYour existing coding tools

Repository AIOps

Audit, propose, rescan — against the repo you actually have.

01

Audit the setup.

Scan the repository for context gaps, stale instructions and configuration risks.

02

Review a proposal.

Get a reviewable AGENTS.md or client-setup change. Inspect it before anything is applied.

03

Rescan after apply.

Confirm the finding is gone, then keep context files aligned with the repository.

Connected products

One workflow. The right tools.

Explore all products

Repository context

Inspect repository context, identify gaps, and prepare CLAUDE.md and AGENTS.md files grounded in your actual stack and conventions.

Fleet maintenance

Review repository health, drift alerts and GitHub checks across your fleet, then inspect proposed context updates before they land.

Agent design

Design agent responsibilities, routing and handoffs, then export implementation scaffolds for the frameworks supported by Agent Architect.

Security review

Inspect prompts, MCP manifests, agent architectures and vendor answers with focused security tools and explicit review boundaries.

A prompt toolkit, too.

Craft, rate and edit instructions for your own workflow. Start with a template, then test it against real examples.

Craft a prompt →Review a prompt →Browse templates →

Trust starts with clear boundaries.

Understand what is processed, what is retained and what an automated check can tell you.

Read about data handling

Public preview ≠ saved analysis

A public preview produces a limited summary. Signed-in analysis retains files and a profile to support your workflow.

Access should fit the task

Connect only the repositories you need. Inspect integration permissions and proposed changes.

A score is a starting point

Review evidence, run your tests and verify the result. Automated checks do not certify a system.

Before you start

Useful answers.
A clear next step.

How to write CLAUDE.mdWhat belongs in AGENTS.mdHow to spot context drift

What does PrompterJack do?

PrompterJack is repository AIOps: audit a repo, review a setup proposal, apply it, then rescan. It maintains AI coding setup files such as AGENTS.md — it is not a chat product. Prompt, agent-design and security tools stay secondary.

Can I try it without an account?

Yes. Paste a public GitHub repository above for a limited preview. Saved profiles and authorized private-repository analysis require sign-in and an eligible plan.

Does it replace Claude Code, Cursor or Copilot?

No. It helps prepare and maintain context for your existing tools. Verify which instruction files and configuration your chosen tool supports.

Will generated files make every AI response correct?

No. Context can improve the information a tool works with, but generated changes still need review and tests. Health and quality scores are review aids, not guarantees.

Start with one repo.
Make the next change clearer.

See your context gaps before choosing a plan.

Scan a public repoCompare plans