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Jira Pm

  • 31 installs
  • 36 repo stars
  • Updated July 14, 2026
  • oimiragieo/agent-studio

Helps with ai & agent building tasks.

About

jira-pm is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • jira-pm
  • AI & Agent Building
  • AI-coding skill

Jira Pm by the numbers

  • 31 all-time installs (skills.sh)
  • Ranked #9,202 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs31
repo stars36
Last updatedJuly 14, 2026
Repositoryoimiragieo/agent-studio

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Mode: Cognitive/Prompt-Driven - No standalone utility script; use via agent context.

<identity> Jira PM (Project Management) - Provides integration with Atlassian Jira for issue tracking, project management, and workflow automation. Enables 90%+ context savings over direct MCP integration. </identity>

<capabilities>

  • Issue management: search, create, update, transition
  • Project discovery and metadata retrieval
  • Sprint management and issue tracking
  • Comment management on issues
  • JQL-based advanced queries

</capabilities>

<requirements>

Environment Variables

Required:

  • JIRA_URL - Base URL of your Jira instance (e.g., <https://yourcompany.atlassian.net>)
  • JIRA_API_TOKEN - API token for authentication (generate at Jira Account Settings -> Security -> API Tokens)
  • JIRA_USER_EMAIL - Email address associated with the API token

Optional:

  • JIRA_DEFAULT_PROJECT - Default project key for operations (e.g., PROJ)
  • JIRA_API_VERSION - API version (default: 3)

</requirements>

<instructions> <execution_process> 1. Authentication: All requests use Basic Auth with JIRA_USER_EMAIL and JIRA_API_TOKEN 2. Progressive Disclosure: Load only necessary issue fields to minimize API calls 3. Validation: Verify project keys and issue keys before operations 4. Error Handling: Gracefully handle rate limits, authentication errors, and invalid inputs </execution_process>

<tool_categories>

Issues

ToolDescriptionConfirmation Required
searchSearch issues using JQLNo
get-issueGet detailed issue informationNo
create-issueCreate a new issueYes
update-issueUpdate existing issue fieldsYes
transitionChange issue status/workflow stateYes

Projects

ToolDescription
list-projectsList all accessible projects
project-infoGet detailed project information

Sprints

ToolDescription
active-sprintGet currently active sprint for a board
sprint-issuesList all issues in a specific sprint

Comments

ToolDescription
get-commentsRetrieve all comments on an issue
add-commentAdd a comment to an issue

</tool_categories>

<usage_patterns>

Common Workflows

Issue Creation: List projects -> Get project info -> Create issue -> Add comment

Sprint Management: Get active sprint -> List sprint issues -> Update issue status -> Add comments

Issue Search: Use JQL for targeted searches -> Retrieve issue details -> Update issues

</usage_patterns>

<agent_integration>

Primary Agent: planner

  • Use Case: Project backlog management, sprint planning, requirement tracking
  • Common Operations: Create issues, update priorities, manage sprints

Secondary Agent: developer

  • Use Case: Issue tracking during development, status updates
  • Common Operations: Search assigned issues, transition issues, add comments

</agent_integration>

<error_handling>

Common Error Scenarios

  • Authentication Errors: Missing or invalid API token, expired credentials, insufficient permissions
  • Rate Limiting: Too many API requests in short period, implement exponential backoff
  • Invalid Inputs: Non-existent project keys, invalid issue types, invalid transition IDs
  • Network Errors: Connection timeouts, unreachable Jira instance

</error_handling>

<best_practices>

1. Use JQL Efficiently: Craft precise JQL queries to reduce result sets and API calls 2. Cache Metadata: Store project keys, issue types, and transitions locally 3. Verify Before Create: Always verify project and issue type before creating issues 4. Use Transitions: Respect workflow states when changing issue status 5. Batch Operations: Group related API calls when possible 6. Handle Errors Gracefully: Provide clear error messages and recovery suggestions 7. Respect Rate Limits: Implement backoff strategies for high-volume operations

</best_practices> </instructions>

<progressive_disclosure>

Context Optimization

1. Lazy Loading: Only load issue details when explicitly requested 2. Field Selection: Request only necessary fields from Jira API 3. Caching: Store frequently accessed metadata (projects, issue types) 4. Streaming: Not supported - all responses are complete payloads 5. Pagination: Automatically handle large result sets

Context Savings: 90%+ compared to loading full Jira MCP server </progressive_disclosure>

<api_reference>

Jira REST API Endpoints Used

  • /rest/api/3/search - JQL search
  • /rest/api/3/issue/{issueKey} - Get/update issue
  • /rest/api/3/issue - Create issue
  • /rest/api/3/issue/{issueKey}/transitions - Transition issue
  • /rest/api/3/project - List projects
  • /rest/api/3/project/{projectKey} - Get project details
  • /rest/agile/1.0/board/{boardId}/sprint - Get sprints
  • /rest/agile/1.0/sprint/{sprintId}/issue - Get sprint issues
  • /rest/api/3/issue/{issueKey}/comment - Get/add comments

See <https://developer.atlassian.com/cloud/jira/platform/rest/v3/> for full reference. </api_reference>

Iron Laws

1. ALWAYS verify the project key exists before creating an issue — Jira silently ignores invalid project keys in some API versions, creating orphaned issues or returning cryptic errors. 2. NEVER use PUT /issue to change status — Jira status changes must go through valid workflow transitions via POST /issue/{key}/transitions; direct field updates bypass workflow validators and automation rules. 3. ALWAYS use JQL for issue searches rather than fetching all issues and filtering client-side — returning all issues wastes quota and causes timeouts on projects with thousands of tickets. 4. NEVER create duplicate issues without first searching for existing ones — duplicate tickets fragment tracking, confuse assignees, and produce misleading velocity metrics. 5. ALWAYS include summary, issuetype, and project fields when creating an issue — these three fields are the minimum required by Jira Cloud REST API v3; missing any produces a 400 error.

Anti-Patterns

Anti-PatternWhy It FailsCorrect Approach
Updating status via field PUTBypasses workflow guards; invalid state transitions succeed silently; automation rules don't fireUse POST /issue/{key}/transitions with the correct transition ID
Fetching all issues and filtering locallyTimes out on large projects; wastes API quota; slow for paginated resultsAlways use JQL with specific project/sprint/status filters
Creating issues without duplication checkSplits work tracking; team sees multiple tickets for same taskSearch with JQL (project = X AND summary ~ "keyword") before creating
Hardcoding field IDs (e.g., customfield_10016)Field IDs differ between Jira instances and cloud/server; breaks across projectsDiscover field IDs dynamically via /rest/api/3/field endpoint
No error handling for rate limits (429)Jira Cloud rate limits at ~300 requests/minute; unhandled 429 crashes automationImplement exponential backoff; check Retry-After header on 429 responses

Memory Protocol (MANDATORY)

Before starting: Read .claude/context/memory/learnings.md

After completing:

  • New pattern -> .claude/context/memory/learnings.md
  • Issue found -> .claude/context/memory/issues.md
  • Decision made -> .claude/context/memory/decisions.md
ASSUME INTERRUPTION: If it is not in memory, it did not happen.

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