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Jira Setup

Organization
rejot-dev
jira-setup

Set up, verify, or use Jira Cloud in Backoffice with an Atlassian account email and API token. Use when connecting a Jira site, creating or searching issues, updating fields, adding comments, transitioning tickets, or discovering Jira projects and issue types.

Overview

Publisherrejot-dev
Repositoryfragno
Skill namejira-setup
Stars
62
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by rejot-dev on GitHub. Read the source before you install it.

Installation

Install the Jira Setup AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/rejot-dev/fragno.git /tmp/fragno
mkdir -p .claude/skills
cp -r /tmp/fragno/apps/backoffice/content/marketplace/jira-setup/versions/1.0.0/skills/jira-setup .claude/skills/jira-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Jira Setup in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Jira Setup on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Jira Setup is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Jira setup

Set Jira Cloud up as one handshake: connect → verify → discover → act.

1. Inspect existing API connections

The API capability is available automatically for the current scope. Inspect the existing outbound API connections before creating or replacing Jira. Reuse an active connection whose slug is jira and whose base URL matches the requested Jira site.

Complete when the existing Jira connection and its base URL are known.

2. Collect missing credentials

Jira Cloud API-token authentication requires:

  • the Jira site URL, such as https://example.atlassian.net;
  • the Atlassian account email;
  • an Atlassian API token, not the account password.

Normalize a bare site name or subdomain into https://<site>.atlassian.net. Confirm an ambiguous custom URL with the user rather than guessing it.

Collect the email and API token through a durable Backoffice UI rather than chat. Define a workflow and return this exact shape from a completed step.do, including only fields still missing:

js
await step.do("request Jira credentials", async () => ({
  $ui: {
    version: 1,
    state: { response: { siteUrl: "", email: "", apiToken: "" } },
    spec: {
      root: "form",
      elements: {
        form: {
          type: "Stack",
          props: { gap: "md" },
          children: ["site-url", "email", "api-token", "submit"],
        },
        "site-url": {
          type: "TextInput",
          props: {
            label: "Jira site URL",
            description: "For example, https://example.atlassian.net.",
            value: { $bindState: "/response/siteUrl" },
            required: true,
          },
          children: [],
        },
        email: {
          type: "TextInput",
          props: {
            label: "Atlassian account email",
            value: { $bindState: "/response/email" },
            required: true,
          },
          children: [],
        },
        "api-token": {
          type: "TextInput",
          props: {
            label: "Atlassian API token",
            description: "Use an API token, not your Atlassian password.",
            value: { $bindState: "/response/apiToken" },
            required: true,
            secret: true,
          },
          children: [],
        },
        submit: {
          type: "WorkflowEventButton",
          props: {
            label: "Connect Jira",
            eventType: "jira.credentials-submitted",
            payload: { $state: "/response" },
          },
          children: [],
        },
      },
    },
  },
}));

const credentials = await step.waitForEvent("Jira credentials", {
  type: "jira.credentials-submitted",
});

Use credentials.payload.apiToken only inside later step.do calls. Keep it out of workflow output, tool summaries, issue content, and prose.

Complete when the workflow has received the site URL, email, and API token through jira.credentials-submitted.

3. Connect Jira Cloud

Create or replace the connection with Basic authentication. The username is the Atlassian account email and the password is the API token:

js
await api.createConnection({
  slug: "jira",
  name: "Jira",
  baseUrl: siteUrl,
  auth: {
    type: "basic",
    username: email,
    password: apiToken,
  },
});

Treat the API token as a secret: use the exact submitted value only in the tool call. Never echo it or substitute a placeholder credential.

Verify stored authentication first with api.getAuthStatus({ slug: "jira" }). Then prove Jira API access with:

js
await api.request({
  slug: "jira",
  method: "GET",
  path: "/rest/api/3/myself",
  headers: { Accept: "application/json" },
  body: { type: "empty" },
  timeoutMs: 30000,
});

Require HTTP 200 and confirm the returned account email or display name with the user. On HTTP 401, ask for a fresh API token and confirm that the email belongs to the token owner. On HTTP 404, recheck the Jira site URL.

Complete when auth is active and /rest/api/3/myself returns the intended account.

4. Discover projects and issue types

Inspect Jira before choosing where or what to create. List accessible projects with:

js
await api.request({
  slug: "jira",
  method: "GET",
  path: "/rest/api/3/project/search",
  query: { maxResults: "50", startAt: "0" },
  headers: { Accept: "application/json" },
  body: { type: "empty" },
  timeoutMs: 30000,
});

Use the user's named project when it resolves uniquely. If multiple projects remain plausible, show their keys and names and ask the user to choose. Do not silently choose an example project.

Discover the selected project's issue types before creating an issue. Prefer a non-subtask type whose name matches the request; use Task for an ordinary actionable ticket when available. Ask when the choice changes the meaning materially, such as Bug versus Feature.

Complete when one project key and one valid issue type are selected.

5. Create issues

Create an issue with POST /rest/api/3/issue. Jira Cloud descriptions use Atlassian Document Format rather than a plain string:

js
await api.request({
  slug: "jira",
  method: "POST",
  path: "/rest/api/3/issue",
  headers: { Accept: "application/json", "Content-Type": "application/json" },
  body: {
    type: "json",
    value: {
      fields: {
        project: { key: projectKey },
        issuetype: { id: issueTypeId },
        summary,
        description: {
          type: "doc",
          version: 1,
          content: [
            {
              type: "paragraph",
              content: [{ type: "text", text: description }],
            },
          ],
        },
      },
    },
  },
  timeoutMs: 30000,
});

Use a concise imperative summary and preserve the user's intent in the description. Include only fields known to be valid for the selected project and issue type. Treat HTTP 201 as success, then report the returned issue key, summary, project, and type. Build the browse URL from the configured base URL and returned key; never expose the API token.

Complete when Jira returns HTTP 201 and an issue key.

6. Operate on existing issues

Use the Jira issue key exactly as supplied or discovered.

  • Read: GET /rest/api/3/issue/{issueKey}.
  • Search: use Jira's current issue-search endpoint with explicit fields and bounded pagination.
  • Update fields: PUT /rest/api/3/issue/{issueKey} with a JSON fields object.
  • Add comments: POST /rest/api/3/issue/{issueKey}/comment with an Atlassian Document Format body.
  • Discover transitions: GET /rest/api/3/issue/{issueKey}/transitions.
  • Transition: POST /rest/api/3/issue/{issueKey}/transitions with { "transition": { "id": transitionId } }.

Discover valid transition IDs immediately before transitioning. Ask the user to choose when several transitions plausibly match. For every mutation, require the endpoint's documented success status and summarize only the fields that actually changed.

Complete when Jira confirms the requested operation or the actionable upstream error is reported.

Request rules

Every api.request call uses a path relative to the Jira connection's base URL and includes a body, including body: { type: "empty" } for GET requests. Treat Jira 4xx and 5xx responses as upstream response data: report Jira's error messages without credentials, response headers, or unrelated account data.

Frequently asked questions

What does the Jira Setup AI skill do?

Set up, verify, or use Jira Cloud in Backoffice with an Atlassian account email and API token. Use when connecting a Jira site, creating or searching issues, updating fields, adding comments, transitioning tickets, or discovering Jira projects and issue types.

Why use Jira Setup on TypingMind?

Because you install it once and use it with any model. Jira Setup is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Jira Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rejot-dev/fragno/tree/main/apps/backoffice/content/marketplace/jira-setup/versions/1.0.0/skills/jira-setup. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Jira Setup?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Jira Setup?

As many as you like. As long as a model supports skills, you can use Jira Setup with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Jira Setup AI skill free?

Yes. It is published on GitHub by rejot-dev under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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