Free Course: An AI Task Pipeline
Jira is a task board — like a to-do list for a team. In 3 lessons you will teach the AI to work with it on its own: you type "pick up KAN-12" into the chat — it opens the task, writes the code, checks itself and closes the ticket with a report. A separate lesson shows how to add a second AI that reviews the first one's code.
Know git, or don't have a web project of your own? Deploy a ready-made project with the pipeline in a minute. Have your own project or want to build it yourself — scroll down for the step-by-step course.
Course program
A task by its number
You type "pick up KAN-12" — the AI finds the task in Jira itself, reads the description, writes the code and moves the card across the board on its own: To Do → In Progress → Done. The board updates without you.
A check before handover
A second AI re-reads the code before the task is closed: is it what was asked, are there bugs, did a password end up right in the code. Lesson 3 shows two ways to add a reviewer — one of them free.
Set up once — works forever
The rules are written into a CLAUDE.md file — the program's memory. Close your laptop, come back tomorrow, start a new chat — the AI remembers everything and works the same way.
Lesson 1. Manual prep — 4 steps
Register Jira and create a project
Jira is a task board, free for teams of up to 10 people. Go to atlassian.com, sign up, create a project and pick a regular Kanban board. Add a couple of real tasks to it — that is what we will test on. Already have Jira — skip this step.
Get an access key (API token)
It is a long password the AI will use to enter your Jira. Open id.atlassian.com → Security → Create API token. The token is shown once — copy it to your notes right away.
Install Claude Code
It is the program you use to talk to the AI right inside your project folder. In the terminal run npm install -g @anthropic-ai/claude-code and sign in to your Anthropic account. Then open the project folder in the terminal, type claude and pick the Fable model with the /model command. That is the last command you ever type by hand.
The AI creates the keys file itself
.env is a plain text file where passwords are kept. No need to create it by hand: the very first prompt of lesson 2 asks the AI to create the file, hide it from publishing and tell you what goes where. All that is left is to paste four values from steps 1–2 — into the file, not into the chat.
Lessons 2–3. The pipeline prompts
A prompt is just text: copy it and paste it into the chat with the AI. Send them one by one, waiting for the AI to finish each time. Prompts 1–3 are lesson 2, prompts 4–5 are lesson 3.
1 Lesson 2. Prompt #1 — show the AI your board
First the AI creates the .env file (you fill in the values), then it simply looks around your Jira: checks that the login works and sees what tasks and columns exist. It will not change anything.
If the AI showed your Jira account name — the connection works. A 401 error means "not allowed in": check the email and the token in the .env file.
2 Lesson 2. Prompt #2 — so the AI never forgets the rules
The AI writes the Jira workflow into the CLAUDE.md file — its memory. The rules will keep working even if you close the program and come back a week later.
Check: a CLAUDE.md file with a "Working with Jira" section appeared in the project folder, and .env is mentioned in .gitignore — meaning your keys will never reach the internet.
3 Lesson 2. Prompt #3 — daily work: one line per task
That is it, setup is over. From now on you type one line with a ticket number every day — the AI does the rest by the rules in CLAUDE.md.
Replace KAN-12 with your own issue key. Tomorrow and a month from now it works the same: the rules are already written down.
4 Lesson 3. Prompt #4 — add a reviewer
Why it matters: whoever wrote the code is bad at spotting their own mistakes — even if it is an AI. A second AI comes in with a fresh head and catches what the first one missed: did something other than what was asked; forgot a rare case; left a password right in the code. On CHATBOSS.PRO every task is checked this way — defects never reach the board.
From now on every task passes a review before closing. Findings are ranked by severity: P0 — stop, the task cannot be closed; P1 — fix now; P2 — can wait; P3 — a matter of taste. The AI posts the summary as a Jira comment.
5 Lesson 3. Script #5 — the same reviewer, as a standalone command
The reviewer does not have to live inside Claude Code. This small script gathers everything you changed in the code and sends it straight to the AI — handy to run before handing work over, or to put on automation. Routine checks run on a cheap model: one review costs cents.
Requires curl and jq (usually already installed) and an Anthropic key in the ANTHROPIC_API_KEY variable. Run: ./review.sh — it compares your branch against the main one.
Lesson 3, a lifehack — the free reviewer from GitHub
If your code lives on GitHub, you can get a second pair of eyes with no keys and no payments at all: it has a built-in AI reviewer, Copilot.
Enable Copilot on github.com — there is a free Copilot Free plan, no card required. The monthly quota covers several reviews a day.
Push the task branch to GitHub and open a Pull Request — that is a "please accept my work" request. In the Reviewers list pick Copilot.
In a minute or two Copilot leaves comments right next to the relevant lines of code, like a human would. Then just tell your AI: "fix the findings from the PR review".
In the repository settings you can make the review automatic — then Copilot checks every new pull request by itself, no need to ask.
Honestly about the quality. Inside Copilot's review runs GPT-4.1. On our CHATBOSS.PRO tasks it reliably catches bugs in the code itself: mixed-up data types, rare cases, forgotten checks, obvious errors. But it sees "how the project works as a whole" noticeably worse — which is why our big pipeline runs a second, separate reviewer. Still, as a free first line of defence it does the job.
Did it work?
If the AI closed your first task and left a comment in Jira — it worked, the pipeline is running. That was one task at a time — next comes the system.
The course is about one task at a time. The full pipeline is covered in the manual — and if you would rather not do it yourself, we can set it up turnkey.
This pipeline is not theory
With the full version of the pipeline we build CHATBOSS.PRO — a service where Telegram bots are assembled with a mouse from ready-made blocks. Every new feature travels the whole route on its own: from task decomposition to the rollout on the live site.
💬 The AI consultant itself runs on the CHATBOSS.PRO platform — ask it about the pipeline, timelines and pricing right in Telegram.
Honestly about the course
Completely free
Three lessons, all the prompts and the reviewer script — take them and use them.
Your keys stay safe
The AI puts the Jira key into a file on your computer and hides it from publishing. It never prints it to the chat.
Everything is in the open
The prompts and the script are on GitHub: you can see exactly what you are running. Hit Watch — you will hear about new chapters.
Anyone can do it
The AI types the terminal commands itself. Your part — paste text into the chat and a couple of keys into a file.
Frequently Asked Questions
Is the course really free?
Yes: no payment, no sign-up, no fine print. It is a simplified version of the same approach we use ourselves and set up for clients.
Is it safe to give the AI a Jira key?
The key lives in a file on your computer and is hidden from publishing — the AI simply reads it from there, and the rules explicitly forbid it from showing the key in the chat, logs or code. And at any moment you can revoke the key at id.atlassian.com with one button — access disappears instantly.
What do I need to make it all work?
Jira (the free plan for up to 10 people is enough), the Claude Code program and an Anthropic subscription with the Fable model. The Copilot lesson also needs a GitHub account. The whole setup takes one evening.
Is the Copilot reviewer really free?
The Copilot Free plan connects without a card, but the monthly request quota is limited, and reviews spend it. It is enough for several reviews a day. If you have more tasks — either the paid plan from $10 a month, or your own reviewer from lesson 3.
What comes after the course?
The course is one task at a time. Next comes the system: many tasks at once, in waves, two reviewers and deploys that go to a staging site first. All of that is in the manual "An AI Pipeline With Your Own Hands" (preorder, the price rises daily until the September 20 release). Or skip the DIY — we can set it up turnkey.
Questions about the course? Ask us — we will help
Stuck on a lesson, or the AI produced something odd — message us on Telegram, we will sort it out
Contact us