HTML5 Studio

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

1
Manual prep · ~15 minutes
Set up a task board, get its key for the AI and install Claude Code. Four click-through steps.
2
Three texts into the chat · ~30 minutes
Copy three ready-made texts one by one: the AI meets your board, memorises the rules and starts picking up tasks by number.
3
The reviewing AI · ~20 minutes
Why code needs a second pair of eyes, a ready-made text for the reviewer, a standalone-command variant — and the free reviewer from GitHub.

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

1

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.

2

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.

3

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.

4

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.

prompt-1-recon.md
We work with Jira through REST API v3 with Basic auth (email:token). Credentials live in a .env file in the project root: JIRA_BASE_URL, JIRA_EMAIL, JIRA_API_TOKEN, JIRA_PROJECT. Security rule: never print the API token to the chat, logs, code or commits — and never ask me to paste it into a message.

Step 0. If there is no .env file — create it with these four placeholder fields, check that .env is listed in .gitignore (add it if not), and tell me what to fill in where: the Jira address, the account email, the API token from id.atlassian.com and the project key. I will fill in the values myself. After that, stop and wait for my "done".

Once .env is filled in — verify the connection and scout my project:
1. GET {JIRA_BASE_URL}/rest/api/3/myself — show the account name from the response: this confirms the connection works.
2. GET /rest/api/3/project/{JIRA_PROJECT}/statuses — what issue types and statuses my board has.
3. Search issues by JQL (project = {JIRA_PROJECT} ORDER BY created DESC) — show the latest issues: key, title, status.
4. Pick any issue and request GET /rest/api/3/issue/{key}/transitions — which status transitions are available.

Finish with a summary: account, board statuses, available transitions and the list of issues in To Do. Do not change anything in Jira yet — this is recon only.

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.

prompt-2-workflow.md
The Jira connection works — now lock the skill in so it applies in every new session. Create (or extend) the CLAUDE.md file in the project root and write this workflow into it:

## Working with Jira
- Credentials live in .env: JIRA_BASE_URL, JIRA_EMAIL, JIRA_API_TOKEN, JIRA_PROJECT. Never print the token to the chat, logs or code. Check that .env is in .gitignore — if not, add it.
- The command "pick up {issue key}" runs the full task cycle:
  1. GET /rest/api/3/issue/{key} — read the title, description and comments. If there is no acceptance criterion, formulate one and show it to me before starting.
  2. Move the issue to In Progress (POST /rest/api/3/issue/{key}/transitions).
  3. Do the work in the project code.
  4. Hand the result to a separate reviewer subagent: acceptance criterion match, bugs, edge cases, code style. Fix the findings immediately, before closing the task.
  5. Move the issue to Done (if the board has a Review or In Testing column — use it) and leave a comment: what was done, which files were touched.
- If the task is too big for one cycle — first propose splitting it into subtasks in Jira and wait for my confirmation.
- On the command "stop" — finish the current step cleanly and record the honest task status in Jira.

Write the workflow, show the final CLAUDE.md and confirm you are ready to work.

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.

prompt-3-work.md
Pick up KAN-12.

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.

prompt-4-reviewer.md
Extend CLAUDE.md with an "AI review" section and from now on follow it when closing every task:

## AI review
- Before moving a task to Done, run a separate reviewer subagent. Hand it: the task's acceptance criterion and the full diff of the changes (git diff over the touched files).
- The reviewer's checklist: 1) acceptance criterion match; 2) bugs and edge cases — empty data, null, concurrent access; 3) security — secrets in the code, SQL injections, unprotected endpoints; 4) readability and project style.
- Every finding gets a priority: P0 — blocks closing the task, P1 — fix now, P2 — can be deferred (create a Jira task for it), P3 — a matter of taste, my call.
- Fix P0 and P1 immediately and re-run the review. A task may only be closed with an empty P0/P1 list.
- Post the review summary as a comment on the Jira issue: how many findings there were and which were fixed.

Write the section, show the final CLAUDE.md and confirm you are ready.

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.

review.sh
#!/bin/bash
# review.sh — AI review of a diff via the Anthropic API.
# Requires: curl, jq and a key:  export ANTHROPIC_API_KEY=sk-ant-...
# Usage:  ./review.sh [base_branch]

set -euo pipefail

BASE_BRANCH="${1:-main}"
DIFF=$(git diff "$BASE_BRANCH"...HEAD)

if [ -z "$DIFF" ]; then
    echo "No changes against $BASE_BRANCH — nothing to review."
    exit 0
fi

SYSTEM='You are a strict code reviewer. Check the diff against this checklist: 1) bugs and edge cases (empty data, null, concurrent access); 2) security — secrets in the code, SQL injections, unprotected endpoints; 3) readability and style. Every finding gets a priority: P0 (blocker), P1 (fix now), P2 (can be deferred), P3 (a matter of taste) — with the file and line. If there are no findings — reply APPROVED.'

curl -s https://api.anthropic.com/v1/messages \
    -H "x-api-key: $ANTHROPIC_API_KEY" \
    -H "anthropic-version: 2023-06-01" \
    -H "content-type: application/json" \
    -d "$(jq -n --arg diff "$DIFF" --arg system "$SYSTEM" '{
        model: "claude-sonnet-5",
        max_tokens: 3000,
        system: $system,
        messages: [{role: "user", content: ("Review this diff:\n\n" + $diff)}]
    }')" | jq -r '.content[0].text'

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.

1

Enable Copilot on github.com — there is a free Copilot Free plan, no card required. The monthly quota covers several reviews a day.

2

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.

3

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".

4

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 next level — the manual

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.

500+
users have already built their bots
Visit CHATBOSS.PRO → Ask the AI consultant about the pipeline

💬 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