AI app for it and development · no coding needed
Source-linked coding agent context and orchestration console
Reduce repeated context briefing and manual agent checking while keeping engineers in control.
Made for: Engineering teams running AI coding agents across repositories, issue trackers and documentation
What it does for you
The problem
Coding agents lack project context and their work is not verified, so engineers re-state conventions and re-check results by hand.
What it gives you
Reviewed, source-linked task context and verified agent results
What you give it
Permitted codeissuesdocumentationagent output
Build your own version of coolplugz, Stash MCP Server and more
One app with what these 4 AI tools do, yours to keep and change: coolplugz, Stash MCP Server, Knowns CLI, bb.
Everything these tools do, in one app
- Context aggregation Collects code, issues, and documentation into one searchable source for AI tools.Found in Stash MCP Server
- Context fetching Automatically gathers relevant information from tools like Jira, GitHub, Notion, and Slack.Found in coolplugz
- Automatic context ingestion Consumes project context and linked documentation directly from tasks to reduce re-stating conventions.Found in Knowns CLI
- Issue-aware context Links related code files, past similar issues, and relevant documents for each ticket.Found in Stash MCP Server
- Task linking Attaches relevant docs to tasks for straightforward context retrieval from the terminal.Found in Knowns CLI
- Automated prompt writing Constructs prompts for Claude Code based on fetched context so you don't spell out every instruction.Found in coolplugz
- Simple command flow Enables concise requests to an assistant, like resolving an issue by ID, with minimal prompting.Found in Stash MCP Server
- Agent orchestration Manages multiple coding agents like Claude Code, Codex, OpenCode, and Cursor using existing subscriptions.Found in bb
- Task verification Checks that the coding agent actually completes tasks correctly rather than assuming success.Found in coolplugz
- CRISPE prompt structuring Applies a predefined structure to prompts with constraints and formatting rules.Found in coolplugz
- Custom models for Jira parsing Uses Hugging Face models to extract repo names and acceptance criteria from messy ticket comments.Found in coolplugz
- Write-back capability Allows the AI to update or create documentation as it learns while working.Found in Knowns CLI
- Prompt-driven self-modification Lets you ask for features like a task tracker, and the tool builds the UI and creates skills for agents.Found in bb
- Plugin architecture Implements workflows, side chat, crons, inline previews, and remote access as plugins.Found in bb
- Access controls and audit logs Respects existing permissions in connected systems and records agent actions for transparency.Found in Stash MCP Server
- Integration helpers Provides connectors for common development platforms to scope which projects and spaces are processed.Found in Stash MCP Server
- Server mode Runs cross-platform in a browser via npx bb-app@latest.Found in bb
- Open source and MIT licensed Allows cloning and running the software locally with full customization.Found in bb
How it works, step by step
- Aggregate code, issues and documentation into one searchable source
- Fetch relevant context from Jira, GitHub, Notion and Slack
- Ingest project context and linked documentation from tasks automatically
- Link related code files, past similar issues and relevant documents per ticket
- Attach relevant docs to tasks for terminal retrieval
- Construct prompts for coding agents from fetched context
- Support concise requests such as resolving an issue by ID
- Orchestrate multiple coding agents using existing subscriptions
- Verify that agents complete tasks correctly
- Apply CRISPE prompt structure with constraints and formatting rules
- Parse messy ticket comments for repo names and acceptance criteria
- Write back documentation updates as agents work
- Build requested features such as task trackers from prompts
- Run workflows, side chat, crons, inline previews and remote access as plugins
- Respect existing permissions and record agent actions in audit logs
- Provide connectors to scope which projects and spaces are processed
- Run cross-platform in a browser via server mode
- Allow local cloning and customization under an open license
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Source-linked coding agent context and orchestration console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Source-linked coding agent context and orchestration console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce repeated context briefing and manual agent checking while keeping engineers in control. For engineering teams running AI coding agents across repositories, issue trackers and documentation, convert permitted code, issues, documentation and agent output into reviewed, source-linked task context and verified agent results. The benefit is a testable hypothesis, measured through verified agent task completions per engineer hour and rework after agent handoff; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect permitted code, issues, documentation and agent output, then follow this sequence: 1. Aggregate code, issues and documentation into one searchable source. 2. Fetch relevant context from Jira, GitHub, Notion and Slack. 3. Construct prompts for coding agents from fetched context. 4. Orchestrate multiple coding agents using existing subscriptions. 5. Verify that agents complete tasks correctly. Resolve uncertain cases with qualified reviewers, approve reviewed, source-linked task context and verified agent results, and measure verified agent task completions per engineer hour and rework after agent handoff against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed repository set and connected workspace; final code review and merge decisions remain with engineers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, code accuracy and usage permissions. Engineers approve substantive changes and merge scope. One fixed repository set and connected workspace; final code review and merge decisions remain with engineers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed repository set and connected workspace; final code review and merge decisions remain with engineers. Implement one approved input format, a bounded representative case set and the first two task modules: aggregate code, issues and documentation into one searchable source; fetch relevant context from Jira, GitHub, Notion and Slack. Support the third module with operator review: construct prompts for coding agents from fetched context. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Customer-owned repositories, issue trackers, documentation spaces and agent subscriptions. Cloud code storage, tracker import/export and CI destinations. Start with file exchange and validate destination specifications before promising direct merge or deployment. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Source connection and scope, Task context and agent run, Verification and audit. Use a project list with connection status, a central task view showing fetched context, generated prompt and agent output, and a right-hand panel for sources, permissions and review state. Let users compare agent runs side by side. Display draft, changes requested and approved states. Provide an audit view with each agent action linked to its source. Make the task-specific outcome reviewed, source-linked task context and verified agent results visible beside its evidence, review state and value baseline.
