jira-issues

Create, update, and manage Jira issues from natural language. Use when the user wants to log bugs, create tickets, update issue status, or manage their Jira backlog.

16 stars

Best use case

jira-issues is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Create, update, and manage Jira issues from natural language. Use when the user wants to log bugs, create tickets, update issue status, or manage their Jira backlog.

Teams using jira-issues should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/jira-issues/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/jira-issues/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/jira-issues/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How jira-issues Compares

Feature / Agentjira-issuesStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Create, update, and manage Jira issues from natural language. Use when the user wants to log bugs, create tickets, update issue status, or manage their Jira backlog.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Jira Issue Management

Create and manage Jira issues using the Jira REST API or MCP.

## Setup

### Option 1: Jira MCP Server
Install the Jira MCP server for seamless integration:
```bash
npx @anthropic/create-mcp-server jira
```

### Option 2: Direct API
Set environment variables:
```bash
export JIRA_BASE_URL="https://yourcompany.atlassian.net"
export JIRA_EMAIL="your-email@company.com"
export JIRA_API_TOKEN="your-api-token"
```

Get your API token: https://id.atlassian.com/manage-profile/security/api-tokens

## Creating Issues

### Basic Issue
```python
import requests
from requests.auth import HTTPBasicAuth
import os

def create_issue(project_key, summary, description, issue_type="Task"):
    url = f"{os.environ['JIRA_BASE_URL']}/rest/api/3/issue"

    auth = HTTPBasicAuth(
        os.environ['JIRA_EMAIL'],
        os.environ['JIRA_API_TOKEN']
    )

    payload = {
        "fields": {
            "project": {"key": project_key},
            "summary": summary,
            "description": {
                "type": "doc",
                "version": 1,
                "content": [{
                    "type": "paragraph",
                    "content": [{"type": "text", "text": description}]
                }]
            },
            "issuetype": {"name": issue_type}
        }
    }

    response = requests.post(url, json=payload, auth=auth)
    return response.json()

# Example
issue = create_issue("PROJ", "Fix login bug", "Users can't login with SSO", "Bug")
print(f"Created: {issue['key']}")
```

### With Labels and Priority
```python
def create_detailed_issue(project_key, summary, description,
                          issue_type="Task", priority="Medium",
                          labels=None, assignee=None):
    payload = {
        "fields": {
            "project": {"key": project_key},
            "summary": summary,
            "description": {
                "type": "doc",
                "version": 1,
                "content": [{
                    "type": "paragraph",
                    "content": [{"type": "text", "text": description}]
                }]
            },
            "issuetype": {"name": issue_type},
            "priority": {"name": priority},
        }
    }

    if labels:
        payload["fields"]["labels"] = labels
    if assignee:
        payload["fields"]["assignee"] = {"accountId": assignee}

    # ... make request
```

## Common Issue Types

| Type | Use For |
|------|---------|
| Bug | Something broken |
| Task | Work item |
| Story | User-facing feature |
| Epic | Large initiative |
| Sub-task | Part of larger task |

## Updating Issues

### Change Status
```python
def transition_issue(issue_key, transition_name):
    # Get available transitions
    url = f"{JIRA_BASE_URL}/rest/api/3/issue/{issue_key}/transitions"
    transitions = requests.get(url, auth=auth).json()

    # Find matching transition
    transition_id = None
    for t in transitions['transitions']:
        if t['name'].lower() == transition_name.lower():
            transition_id = t['id']
            break

    # Execute transition
    requests.post(url, json={"transition": {"id": transition_id}}, auth=auth)
```

### Add Comment
```python
def add_comment(issue_key, comment_text):
    url = f"{JIRA_BASE_URL}/rest/api/3/issue/{issue_key}/comment"

    payload = {
        "body": {
            "type": "doc",
            "version": 1,
            "content": [{
                "type": "paragraph",
                "content": [{"type": "text", "text": comment_text}]
            }]
        }
    }

    requests.post(url, json=payload, auth=auth)
```

## Searching Issues

### JQL Queries
```python
def search_issues(jql):
    url = f"{JIRA_BASE_URL}/rest/api/3/search"
    params = {"jql": jql, "maxResults": 50}
    response = requests.get(url, params=params, auth=auth)
    return response.json()['issues']

# Examples
my_bugs = search_issues("project = PROJ AND type = Bug AND assignee = currentUser()")
open_items = search_issues("project = PROJ AND status != Done")
recent = search_issues("project = PROJ AND created >= -7d")
```

## Quick Commands

When user says... create this:

| Command | Action |
|---------|--------|
| "log bug about X" | Bug issue with description |
| "create task for X" | Task issue |
| "what's on my plate" | JQL: assignee = currentUser() AND status != Done |
| "move X to done" | Transition issue to Done |
| "add comment to X" | Add comment to issue |

## Best Practices

1. **Summary**: Keep under 80 chars, start with verb (Fix, Add, Update)
2. **Description**: Include steps to reproduce for bugs
3. **Labels**: Use for categorization (frontend, backend, urgent)
4. **Links**: Reference related issues when relevant



## Scientific Skill Interleaving

This skill connects to the K-Dense-AI/claude-scientific-skills ecosystem:

### Graph Theory
- **networkx** [○] via bicomodule
  - Universal graph hub

### Bibliography References

- `general`: 734 citations in bib.duckdb

## Cat# Integration

This skill maps to **Cat# = Comod(P)** as a bicomodule in the equipment structure:

```
Trit: 0 (ERGODIC)
Home: Prof
Poly Op: ⊗
Kan Role: Adj
Color: #26D826
```

### GF(3) Naturality

The skill participates in triads satisfying:
```
(-1) + (0) + (+1) ≡ 0 (mod 3)
```

This ensures compositional coherence in the Cat# equipment structure.

Related Skills

jepsen-testing

16
from plurigrid/asi

Jepsen-style correctness testing for distributed systems under faults (partitions, crashes, clock skew) using concurrent operation histories and formal checkers (linearizability/serializability and Elle-style anomalies). Use when designing, implementing, or running Jepsen tests, or interpreting histories/violations.

Deterministic Color Generation via Metadata Hashing

16
from plurigrid/asi

**Status**: ✅ Production Ready

cyton-dongle

16
from plurigrid/asi

Connect and stream from OpenBCI Cyton/Daisy via USB dongle, including first-time radio channel pairing

asi-transient-agenda

16
from plurigrid/asi

Org-agenda-like transient views for ASI skill orchestration via nbb/squint + Emacs hydra

Topological Superintelligence (TSI)

16
from plurigrid/asi

Compositional AI framework using GF(3) triadic balance and category-theoretic foundations.

zx-calculus

16
from plurigrid/asi

Coecke's ZX-calculus for quantum circuit reasoning via string diagrams with Z-spiders (green) and X-spiders (red)

zulip-cogen

16
from plurigrid/asi

Zulip Cogen Skill 🐸⚡

zls-integration

16
from plurigrid/asi

zls-integration skill

zig

16
from plurigrid/asi

zig skill

zig-syrup-bci

16
from plurigrid/asi

Multimodal BCI pipeline in Zig: DSI-24 EEG, fNIRS mBLL, eye tracking IVT, LSL sync, EDF read/write, GF(3) conservation

zig-programming

16
from plurigrid/asi

zig-programming skill

zeroth-bot

16
from plurigrid/asi

Zeroth Bot - 3D-printed open-source humanoid robot platform for sim-to-real and RL research. Affordable entry point for humanoid robotics.