Agent skill

Jira Doc Generator

by openshift-eng in openshift-eng/ai-helpers

Detailed implementation guide for recursively analyzing Jira features and generating comprehensive documentation

Apache-2.0Auto-check passedDevelopment

Install Jira Doc Generator

skills CLI
$ npx skills add openshift-eng/ai-helpers --skill jira-doc-generator -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openshift-eng/ai-helpers jira-doc-generator --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openshift-eng/ai-helpers.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugins/jira/skills/jira-doc-generator .claude/skills/jira-doc-generator && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
jira-doc-generator
GitHub stars
120
Token cost
~2.4k tokens
SKILL.md length
1,018 words
Files
1
Skills in repo
118
Repo updated
First seen
Licence
Apache-2.0

At a glance

Detailed implementation guide for recursively analyzing Jira features and generating comprehensive documentation

  • Works in 5 steps: Initialize and Fetch Main Feature Issue → Analyze Each GitHub PR → Synthesize Documentation Structure → …
  • Development work in your project
  • SKILL.md covers When to Use This Skill, Prerequisites, Implementation Steps and Error Handling, plus 3 more sections
  • Calls gh; reaches github.com

What it does

Jira Doc Generator is an agent skill from openshift-eng/ai-helpers. Detailed implementation guide for recursively analyzing Jira features and generating comprehensive documentation

Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development. It works with Jira, GitHub and Model Context Protocol. The repository describes itself as: Developer productivity tools for Claude Code & other AI assistants. The licence is Apache-2.0.

When your agent uses it

  • Development work in your project

Example prompts

  • “/jira-doc-generator”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Initialize and Fetch Main Feature Issue
  2. Analyze Each GitHub PR
  3. Synthesize Documentation Structure
  4. Generate Documentation Content
  5. Output

What it can do on your machine

Read from SKILL.md and the folder at commit a627176. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • gh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Jira Doc Generator loads about 2.4k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 1,018 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~2.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openshift-eng/ai-helpers at commit a627176, republished under its Apache-2.0 licence (© openshift-eng). 1,018 words, ~2,352 tokens.

Download SKILL.mdSave it as .claude/skills/jira-doc-generator/SKILL.md (or your agent's skills folder).
name
jira-doc-generator
description
Detailed implementation guide for recursively analyzing Jira features and generating comprehensive documentation

Jira Feature Documentation Generator

This skill provides detailed step-by-step implementation guidance for the /jira:generate-feature-doc command, which generates comprehensive feature documentation by recursively analyzing a Jira feature and all its related issues and GitHub pull requests.

IMPORTANT FOR AI: This is a procedural skill - when invoked, you should directly execute the implementation steps defined in this document. Do NOT look for or execute external scripts. Follow the step-by-step instructions below, starting with Step 1.

When to Use This Skill

This skill is automatically invoked by the /jira:generate-feature-doc command and should not be called directly by users.

Prerequisites

  • MCP Jira server configured and running (required - see plugins/jira/README.md for setup)
  • GitHub CLI (gh) installed and authenticated (for analyzing PRs)
  • User has read access to Jira issues (including private issues via MCP authentication)
  • User has read access to linked GitHub repositories
  • Working directory has .work/jira/feature-doc/ for output (will be created if needed)

Implementation Steps

Step 1: Initialize and Fetch Main Feature Issue

Objective: Set up environment and fetch main feature issue.

Actions:

  1. Save initial directory: INITIAL_DIR=$(pwd) (save at start, before any cd commands)

  2. Check prerequisites: Verify jq and gh CLI are installed and authenticated

    • If missing, display error with installation instructions
  3. Create output directory: WORK_DIR=$INITIAL_DIR/.work/jira/feature-doc/<feature-key> (use mkdir -p)

  4. Fetch main feature via getJiraIssue with the feature key. If MCP is unavailable, display error pointing to plugins/jira/README.md.

  5. Parse response: Extract key, summary, description, issuetype, status

    • If fetch fails, display error and exit
  6. Display progress: Show feature summary and type

Step 2: Analyze Each GitHub PR

Important: This step expects PR data as input from the jira:extract-prs skill (invoked by the command file). The input is structured JSON containing all discovered PRs with their metadata.

Input Format:

json
{
  "pull_requests": [
    {
      "url": "https://github.com/org/repo/pull/123",
      "state": "MERGED",
      "title": "PR title",
      "isDraft": false,
      "sources": ["remote_link", "description"],
      "found_in_issues": ["ISSUE-123"]
    }
  ]
}

Objective: Fetch and analyze PR details to extract implementation information.

Important: This command is for documenting completed features. Only analyze PRs that have been MERGED. Skip OPEN, DRAFT, WIP, or CLOSED (but not merged) PRs.

Actions:

  1. Filter for MERGED PRs: Only analyze PRs with state == "MERGED" AND isDraft == false

    • Skip OPEN PRs (work in progress)
    • Skip PRs with isDraft == true (not ready for review)
    • Skip CLOSED PRs (not merged)
  2. For each MERGED PR, fetch detailed data:

    • Parse PR URL to extract org/repo/number
    • Fetch metadata: gh pr view {number} --repo {org}/{repo} --json title,body,mergedAt,author,commits,files
    • Fetch diff: gh pr diff {number} --repo {org}/{repo}
    • Fetch comments: gh pr view {number} --repo {org}/{repo} --json comments
  3. Extract key information:

    • From body: Purpose, approach, breaking changes
    • From commits: Implementation steps, testing
    • From diff: New APIs, architectural changes, config updates
    • From comments: Design decisions, rationale, considerations
  4. Handle errors: If PR inaccessible (403, 404), log warning and continue

  5. Save data: Store metadata, diff, and comments to ${WORK_DIR}/pr-{org}-{repo}-{number}.*

Step 3: Synthesize Documentation Structure

Objective: Organize information into structured outline.

Available sections (calling command specifies which to generate):

  1. Overview: Jira link, status, counts, dates, authors (from main issue + PR metadata)
  2. Background and Goals: Description from main issue (clean Jira formatting)
  3. Architecture and Design: High-level changes, components, design decisions (from PRs)
  4. Implementation Details: Core changes, API changes, configuration (from PR diffs, code snippets)
  5. Usage Guide: Prerequisites, basic/advanced usage (from README updates, PR descriptions)
  6. Testing: Test coverage, strategies, key test PRs
  7. Related Resources: Can include external links, issue tables, PR tables, dependency graphs
Step 4: Generate Documentation Content

Objective: Fill in the outline with actual content based on command requirements.

Section generation guidelines:

  1. Overview: Extract from main issue + PR metadata (Jira link, status, counts, dates, authors)
  2. Background: Clean Jira formatting from main issue description
  3. Architecture: Synthesize from PR descriptions and comments (high-level overview, components, decisions with PR links)
  4. Implementation: Group by core changes, API changes, configuration (include code snippets, link to PRs, list key files)
  5. Usage: Extract from README updates and PR descriptions (prerequisites, YAML/CLI examples)
  6. Testing: Summarize by category (unit, E2E, CI), list key test PRs
  7. Related Resources: Format depends on command requirements (external links, tables, graphs)

Note: Only generate sections specified by the calling command. Check the command file for exact requirements.

Show full SKILL.md (353 more words)Show less
Step 5: Output

Objective: Save documentation and display summary.

Actions:

  1. Write documentation: Save to ${WORK_DIR}/feature-doc.md with footer:

    markdown
    ---
    *Generated by `/jira:generate-feature-doc` on <timestamp>*
    *Source: <feature-key> and <count> related issues*
  2. Save metadata: Store analysis log with timestamps, counts, output files, errors/warnings

  3. Display summary:

    • Success message with file location
    • Statistics: issues analyzed, PRs analyzed, commits, files changed, doc lines
    • Warnings if any (inaccessible PRs, missing descriptions)
    • Additional files for debugging

Error Handling

Issue Not Found (404, 403, network error):

  • Display error with verification steps (issue key format, permissions, MCP config)
  • Display the error message, clean up any temporary state, and exit without creating files

No PRs Found:

  • Display warning (feature not implemented, PRs not linked, or small feature)
  • Generate documentation from main issue only

GitHub Rate Limit:

  • Display error with progress and reset time
  • Offer options: wait, generate from partial data, or cancel

Large Feature (>50 PRs):

  • Display warning with estimated time and API calls
  • Offer options: continue or cancel

Malformed Issue Data:

  • Log warning about missing/invalid fields
  • Continue with remaining issues (don't fail entire process)

Performance Optimization

Parallel PR Analysis:

  • For >10 PRs, use parallel processing (xargs -P 5)
  • Limit to ~5 concurrent requests to avoid rate limits

Smart Diff Analysis:

  • Use --stat to identify key files
  • Skip vendor/, generated files, test fixtures
  • Fetch full diff only for critical files

Best Practices for AI Implementation

  1. Progress feedback: Show progress after each major step (discovery, PR analysis, etc.)

  2. Error resilience: Don't fail the entire process if one PR is inaccessible

  3. Smart synthesis: Don't just concatenate PR descriptions - synthesize into coherent narrative

  4. Context awareness: Understand the codebase domain (e.g., Kubernetes, OpenShift) to better interpret changes

  5. Structured output: Use consistent markdown formatting with proper headers, code blocks, tables

  6. Link preservation: Always provide clickable links to Jira issues and GitHub PRs

  7. Timestamp tracking: Note when PRs were merged to understand timeline

  8. Author attribution: Credit authors of PRs and issues where relevant

  9. Code examples: Include actual code snippets from PRs to illustrate changes

  10. Visual hierarchy: Use tables, lists, and headers to make documentation scannable

Example Workflow

User runs: /jira:generate-feature-doc OCPSTRAT-1612

1. Initialize
   - Fetch main feature issue (OCPSTRAT-1612)
   - Create working directory (.work/jira/feature-doc/OCPSTRAT-1612/)
   - Verify prerequisites (jq, gh CLI)

2. Extract PRs (via extract-prs skill)
   - Discover descendants using `parent = KEY` BFS → 3 issues total
   - Extract PRs from remote links (primary) + text (backup) → 7 PRs
   - Fetch PR state from GitHub → 5 MERGED, 1 OPEN, 1 CLOSED

3. Analyze MERGED PRs
   - Filter for MERGED PRs → 5 PRs to analyze
   - For each: fetch metadata + diff + comments
   - Extract implementation details, design decisions

4. Generate Documentation
   - Synthesize sections: Overview, Architecture, Implementation, Usage, Testing
   - Create tables for issues and PRs
   - Write to feature-doc.md

5. Display Results
   ✅ Documentation generated successfully!
   📄 File: .work/jira/feature-doc/OCPSTRAT-1612/feature-doc.md
   📊 3 issues, 5 MERGED PRs, ~380 lines generated

© openshift-eng, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in plugins/jira/skills/jira-doc-generator of openshift-eng/ai-helpers.

Open the folder on GitHubat commit a627176

Compare with similar skills

Jira Doc Generator next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Jira Doc Generator compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Jira Doc Generator this skillopenshift-eng/ai-helpers120—~2.4kAutomated safety check: PassApache-2.0
Link Ticket To SessionJayantDevkar/claude-code-karma329—~1.8kAutomated safety check: NotesApache-2.0
Issue Triage Loopcobusgreyling/loop-engineering11k—~522Automated safety check: PassMIT
Create Epic RecapDataDog/datadog-agent3.8k—~5kAutomated safety check: NotesApache-2.0
Review Release Noteschef/chef-web-docs143—~5.2kAutomated safety check: PassCustom licence
Akb Ingestdnotitia/akb162—~2kAutomated safety check: PassCustom licence

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Categories

Questions about Jira Doc Generator

What does Jira Doc Generator do?

Detailed implementation guide for recursively analyzing Jira features and generating comprehensive documentation. Jira Doc Generator is an agent skill from openshift-eng/ai-helpers.

When should I use Jira Doc Generator?

Jira Doc Generator fits situations like: development work in your project.

How do I install Jira Doc Generator in Claude Code?

Run `npx skills add openshift-eng/ai-helpers --skill jira-doc-generator -a claude-code`. Or copy the skill folder (plugins/jira/skills/jira-doc-generator in openshift-eng/ai-helpers) into .claude/skills/jira-doc-generator in your project. Claude Code loads it when a task matches its description.

How do I install Jira Doc Generator in Codex?

Run `npx skills add openshift-eng/ai-helpers --skill jira-doc-generator -a codex`. Or copy the skill folder (plugins/jira/skills/jira-doc-generator in openshift-eng/ai-helpers) into .agents/skills/jira-doc-generator in your project. Codex loads it when a task matches its description.

Can I use Jira Doc Generator in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openshift-eng/ai-helpers --skill jira-doc-generator -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/jira-doc-generator, .gemini/skills/jira-doc-generator, .github/skills/jira-doc-generator and .opencode/skills/jira-doc-generator in your project.

What does Jira Doc Generator need to run?

Going by SKILL.md and its folder, Jira Doc Generator needs the command-line tools its instructions call (gh).

Does Jira Doc Generator access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Jira Doc Generator safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Jira Doc Generator use?

Jira Doc Generator is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Jira Doc Generator use?

About 2.4k tokens (SKILL.md is roughly 9.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Jira Doc Generator?

Skills that share tags, products or a category with Jira Doc Generator: Link Ticket To Session (JayantDevkar/claude-code-karma, 329 stars), Issue Triage Loop (cobusgreyling/loop-engineering, 11k stars), Create Epic Recap (DataDog/datadog-agent, 3.8k stars) and Review Release Notes (chef/chef-web-docs, 143 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Jira Doc Generator?

openshift-eng (a GitHub organization) maintains it in openshift-eng/ai-helpers, which has 120 GitHub stars. The repository holds 118 skills in this directory. The repository was last updated on October 6, 2026.

Source: openshift-eng/ai-helpers on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.