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Export Open Issues

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huytieu
export-open-issues

Audit and export open issues from any project tracker with summary analysis and vault archival

Overview

Publisherhuytieu
RepositoryCOG-second-brain
Skill nameexport-open-issues
Stars
1.2K
Forks
138
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 huytieu on GitHub. Read the source before you install it.

Installation

Install the Export Open Issues 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/huytieu/COG-second-brain.git /tmp/COG-second-brain
mkdir -p .claude/skills
cp -r /tmp/COG-second-brain/skills/export-open-issues .claude/skills/export-open-issues
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Export Open Issues 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 Export Open Issues 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 Export Open Issues 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.

COG Export Open Issues Skill

When to Invoke

  • User wants to audit open issues or backlog health
  • User says "export issues", "open issues", "backlog audit", "issue report", "what's open"
  • Sprint/cycle planning prep — need a clear picture of what's in flight
  • Stakeholder reporting — need a snapshot of current work status

Agent Mode Awareness

Check agent_mode in 00-inbox/MY-PROFILE.md frontmatter:

  • If agent_mode: team — use parallel agents to collect from all active trackers simultaneously and produce a unified report
  • If agent_mode: solo — collect from the primary tracker sequentially

Command: /export-open-issues

Pre-Flight Check

  1. Read 00-inbox/MY-INTEGRATIONS.md to determine which trackers are active

  2. Read 00-inbox/MY-PROFILE.md for active projects

  3. Get current timestamp: Run date '+%Y-%m-%d %H:%M' using Bash

  4. Ask the user (if not already provided):

    • Which project? (if multiple active)
    • Scope: all open issues, or filtered by label/milestone/assignee?
    • Include additional analysis? (aging, priority distribution, assignee load)

Execution Strategy

Phase 1: Collect Open Issues

Team Mode (parallel agents)

Launch collection agents using the Task tool with run_in_background: true:

Agent: "github-issues-collector" (if GitHub is active)

Export all open issues from GitHub.
Repository: [CUSTOMIZE: your-org/your-repo]

1. Get all open issues:
   gh issue list --repo [CUSTOMIZE: your-org/your-repo] --state open --json number,title,author,assignees,labels,createdAt,updatedAt,milestone,url --limit 500

2. Get all open PRs (separate from issues):
   gh pr list --repo [CUSTOMIZE: your-org/your-repo] --state open --json number,title,author,labels,createdAt,updatedAt,reviewDecision,url --limit 200

For each issue, calculate:
- Age in days (today - createdAt)
- Days since last update (today - updatedAt)
- Staleness flag: >30 days since last update = stale

Return: full issue list with calculated fields

Agent: "linear-issues-collector" (if Linear is active)

Export all open issues from Linear.

1. Use ToolSearch to load Linear tools
2. List all teams: mcp__claude_ai_Linear_2__list_teams
3. For each relevant team, list open issues: mcp__claude_ai_Linear_2__list_issues
4. Get current cycles: mcp__claude_ai_Linear_2__list_cycles
5. Get milestones: mcp__claude_ai_Linear_2__list_milestones

For each issue, collect:
- ID, title, status, priority, assignee, labels, project
- Created date, updated date
- Cycle membership
- Blocked status

Return: full issue list with metadata

Agent: "jira-issues-collector" (if Jira is active)

Export all open issues from Jira.
Project: [CUSTOMIZE: YOUR-PROJECT-KEY]

1. Search open issues:
   JQL: project = "[CUSTOMIZE: YOUR-PROJECT-KEY]" AND status NOT IN (Done, Closed, Resolved) ORDER BY priority DESC, created ASC

2. For each issue, collect:
   key, summary, issuetype, status, priority, assignee, reporter, labels, components, fixVersion, created, updated, duedate

Return: full issue list with metadata
Solo Mode

Run collection sequentially for the primary tracker.

Phase 2: Analyze and Categorize

Process the collected data to produce:

2.1 Summary Statistics
Total Open Issues: [N]
  - By Priority: Critical [N], High [N], Medium [N], Low [N], None [N]
  - By Type: Feature [N], Bug [N], Task [N], Other [N]
  - By Status: To Do [N], In Progress [N], In Review [N], Blocked [N]
  - By Assignee: [Name] ([N]), [Name] ([N]), Unassigned ([N])
2.2 Health Indicators
  • Stale issues (no update in >30 days): list with age
  • Unassigned issues: list with priority
  • Blocked issues: list with blocker details
  • Overdue issues (past due date): list with days overdue
  • Oldest open issues: top 10 by age
  • In-progress bottleneck: issues in "In Progress" for >7 days
2.3 Distribution Charts (text-based)
Priority Distribution:
  Critical  ████░░░░░░  12%
  High      ████████░░  38%
  Medium    ██████░░░░  28%
  Low       ████░░░░░░  22%

Age Distribution:
  <7 days   ██████████  45%
  7-30 days ██████░░░░  30%
  30-90 d   ███░░░░░░░  15%
  >90 days  ██░░░░░░░░  10%

Phase 3: Generate Report

markdown
---
type: open-issues-audit
project: [project-name]
date: [YYYY-MM-DD]
created: [YYYY-MM-DD HH:MM]
source: [github/linear/jira/multi]
tags: ["#issues-audit", "#[project-name]", "#backlog"]
summary:
  total_open: [N]
  critical: [N]
  high: [N]
  stale: [N]
  unassigned: [N]
  blocked: [N]
  oldest_days: [N]
---

# Open Issues Audit — [Project Name]

**Date:** [YYYY-MM-DD]
**Source:** [Tracker(s) used]
**Total Open Issues:** [N]

---

## Executive Summary

[2-3 sentences: overall backlog health, biggest concerns, and recommended actions]

---

## Summary Statistics

| Category | Count | % of Total |
|----------|-------|-----------|
| **By Priority** | | |
| Critical | [N] | [%] |
| High | [N] | [%] |
| Medium | [N] | [%] |
| Low | [N] | [%] |
| No Priority | [N] | [%] |
| **By Status** | | |
| To Do | [N] | [%] |
| In Progress | [N] | [%] |
| In Review | [N] | [%] |
| Blocked | [N] | [%] |
| **By Type** | | |
| Feature/Story | [N] | [%] |
| Bug | [N] | [%] |
| Task | [N] | [%] |
| Other | [N] | [%] |

---

## Assignee Load

| Assignee | Open Issues | Critical/High | In Progress | Oldest Issue (days) |
|----------|-------------|---------------|-------------|-------------------|
| [Name] | [N] | [N] | [N] | [N] |
| [Name] | [N] | [N] | [N] | [N] |
| Unassigned | [N] | [N] || [N] |

---

## Health Alerts

### Stale Issues (>30 days without update)
| # | Title | Assignee | Priority | Age (days) | Last Updated |
|---|-------|----------|----------|-----------|-------------|
| [#] | [Title] | [Name] | [Priority] | [N] | [Date] |

### Blocked Issues
| # | Title | Assignee | Blocked By | Days Blocked |
|---|-------|----------|-----------|-------------|
| [#] | [Title] | [Name] | [Reason] | [N] |

### Unassigned High-Priority Issues
| # | Title | Priority | Age (days) | Labels |
|---|-------|----------|-----------|--------|
| [#] | [Title] | [Priority] | [N] | [Labels] |

### Overdue Issues
| # | Title | Assignee | Due Date | Days Overdue |
|---|-------|----------|----------|-------------|
| [#] | [Title] | [Name] | [Date] | [N] |

---

## Full Issue List

### Critical Priority
| # | Title | Status | Assignee | Age | Labels | URL |
|---|-------|--------|----------|-----|--------|-----|
| [#] | [Title] | [Status] | [Name] | [N]d | [Labels] | [URL] |

### High Priority
[Same table format]

### Medium Priority
[Same table format]

### Low Priority
[Same table format]

---

## Recommendations

1. **[Recommendation 1]** — [Specific action with rationale]
2. **[Recommendation 2]** — [Specific action with rationale]
3. **[Recommendation 3]** — [Specific action with rationale]

---

*Generated by COG Open Issues Audit | [Date]*

Phase 4: Save to Vault

Save to: 04-projects/[project]/audits/open-issues-YYYY-MM-DD.md

bash
mkdir -p "04-projects/[project]/audits"

Phase 5: Present Results

Show the user:

  1. Executive summary
  2. Key health alerts (stale, blocked, unassigned)
  3. Top recommendations
  4. File location

Ask if they want:

  • Full detailed view
  • Export as CSV (generate a simple CSV file alongside the markdown)
  • Share to a specific channel or wiki

CSV Export (Optional)

If user requests CSV, generate alongside the markdown:

#,Title,Type,Status,Priority,Assignee,Labels,Created,Updated,Age(days),URL
[data rows]

Save to: 04-projects/[project]/audits/open-issues-YYYY-MM-DD.csv


Fallback Behavior

ScenarioBehavior
No tracker activeInform user that a project tracker integration is needed; offer to set one up
Tracker API failsRetry once, then report partial results with error note
Too many issues (>500)Paginate collection, warn user, offer to filter by label/milestone/assignee
Multiple trackers activeCollect from all and produce a unified report, noting the source for each item
No open issues foundReport clean backlog status (this is good news!)

Error Handling

  • Rate limits: Paginate requests and add delays if needed
  • Large payloads: Summarize rather than list all items if >200 issues
  • Missing fields: Mark as "N/A" rather than failing
  • Context overflow: Process in batches, summarize per batch

Frequently asked questions

What does the Export Open Issues AI skill do?

Audit and export open issues from any project tracker with summary analysis and vault archival

Why use Export Open Issues on TypingMind?

Because you install it once and use it with any model. Export Open Issues 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 Export Open Issues in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/huytieu/COG-second-brain/tree/main/skills/export-open-issues. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Export Open Issues?

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 Export Open Issues?

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

Is the Export Open Issues AI skill free?

Yes. It is published on GitHub by huytieu 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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