# Audit Jira

> Jira project health audit via the Atlassian MCP. Identifies velocity trends, backlog rot, bug clusters, assignee concentration risk, and firefighting patterns. Requires the Atlassian MCP to be connected. Use when asked to "audit jira", "project health", "backlog analysis", or "team velocity".

- Skill: `datashaman/audit-jira` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add datashaman/audit-jira`
- Raw SKILL.md: https://api.skillmd.com/api/skills/datashaman/audit-jira/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: datashaman (https://skillmd.com/u/datashaman)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/datashaman/audit-jira

---


# Jira Project Health Audit

Query five dimensions of Jira data to build a picture of team health, backlog quality, and delivery risk.

Uses the Atlassian MCP (`mcp__atlassian__*`). The user must have the Atlassian MCP connected and authenticated before running.

## Arguments

- `project` (required): Jira project key (e.g. `ENG`, `PLAT`). Can be passed positionally — `/audit-jira ENG`.
- `since` (optional): JQL date expression for the analysis window (default: `"-52w"`).

## Steps

### 0. Resolve the cloud ID

Call `mcp__atlassian__getAccessibleAtlassianResources` to get the list of Atlassian sites. Use the `id` field of the first result as `cloudId` for all subsequent calls. If multiple sites are returned, ask the user which one to use.

### 1. Gather raw data

Call `mcp__atlassian__searchJiraIssuesUsingJql` for each of the five queries below. Use `maxResults: 100` and page via `nextPageToken` until exhausted for each query.

**Velocity / throughput** — completed issues in the window:
```
jql: "project = $PROJECT AND statusCategory = Done AND resolutiondate >= $SINCE ORDER BY resolutiondate ASC"
fields: ["key", "issuetype", "assignee", "priority", "resolutiondate", "labels", "components"]
```

**Backlog health** — all open issues ordered oldest first:
```
jql: "project = $PROJECT AND statusCategory in ('To Do', 'In Progress') ORDER BY created ASC"
fields: ["key", "issuetype", "summary", "assignee", "priority", "status", "created", "labels", "components", "story_points"]
```

**Bug clusters** — all bugs in the window:
```
jql: "project = $PROJECT AND issuetype = Bug AND created >= $SINCE ORDER BY created ASC"
fields: ["key", "summary", "assignee", "priority", "status", "resolutiondate", "labels", "components", "created"]
```

**Assignee concentration** — all issues in the window:
```
jql: "project = $PROJECT AND created >= $SINCE ORDER BY created ASC"
fields: ["key", "issuetype", "assignee", "status", "priority"]
```

**Firefighting patterns** — high-priority and escalation-labeled issues:
```
jql: "project = $PROJECT AND (priority in (Highest, High) OR labels in (hotfix, urgent, escalation, incident)) AND created >= $SINCE ORDER BY created DESC"
fields: ["key", "summary", "priority", "labels", "assignee", "created", "resolutiondate", "status"]
```

### 2. Analyze each dimension

For each area, produce a short analysis section by aggregating the returned issue data.

#### Velocity & Throughput
- Count completed issues per month: group `resolutiondate` by `YYYY-MM`
- Break down by issue type (story, task, bug, spike)
- Identify the trajectory: growing, stable, declining, erratic
- Flag sharp drops or spikes and hypothesize causes (staff changes, release crunches, holidays)
- Note current velocity relative to the period average

#### Backlog Health
- Count total open issues, split by status (To Do vs In Progress)
- Age distribution: bucket by `created` date — 0-30d / 30-90d / 90-180d / 180d+
- Unestimated items: issues with no `story_points` value
- Unassigned items: issues with no `assignee`
- Stale in-progress: items in `In Progress` status with `created` > 14 days ago and no recent activity signal
- Priority skew: what fraction are High/Highest vs lower priorities

#### Bug Clusters
- Group bugs by `components` and `labels`, count per group
- Calculate bug ratio: bugs as a percentage of all completed work in the window
- **Cross-reference with backlog**: open bugs that are also old and unassigned are highest-risk
- Flag any component with a disproportionate share (2x+ the median)
- Note if bug volume is growing or shrinking month-over-month

#### Assignee Concentration
- Group all issues in the window by `assignee`, count completed and in-progress separately
- Calculate what percentage of work the top assignee accounts for
- Flag single-assignee risk: if one person accounts for 40%+ of in-progress work, that's a bus factor concern
- Identify unassigned work: issues with no owner
- Note contributors active in earlier months but absent recently

#### Firefighting Patterns
- Count P0/P1 (Highest/High) issues and hotfix/escalation/incident-labeled items
- Calculate frequency (e.g. "2 incidents per month")
- Flag if volume is increasing month-over-month
- If frequent: signals unreliable releases, missing test coverage, or a fragile deploy process
- If absent: either the team is stable, or priority/label discipline is inconsistent — check a sample of recent issues before concluding stability

### 3. Cross-reference and synthesize

Combine findings across all five dimensions:

- **Danger zones**: Components with high bug counts AND old backlog items AND a single assignee. Highest-risk areas
- **Team health signals**: Is velocity stable? Are issues distributed? Is knowledge shared or siloed?
- **Process signals**: Are firefighting events increasing? Do bugs cluster around specific components or time periods?
- **Backlog rot indicators**: Items bumped repeatedly but never done. Stale in-progress with no owner. Unestimated work aged 90d+

### 4. Generate the report

Write the report into the skill's own directory, under a subfolder named after the project key. Full path: `{skill-base-dir}/{project-key}/YYYY-MM-DD-audit.md`. Create the subfolder if it doesn't exist.

```markdown
# Jira Project Health Audit

**Date**: [date] | **Project**: [key] | **Window**: [since period] | **Total issues in window**: [count]

## Executive Summary

[3-5 bullet points capturing the most important findings. Lead with risks.]

## Risk Matrix

| Risk | Severity | Evidence | Recommendation |
|------|----------|----------|----------------|
| [risk name] | HIGH/MEDIUM/LOW | [data point from the analysis] | [actionable next step] |

---

## 1. Velocity & Throughput

| Month | Completed | Stories | Bugs | Tasks | Spikes |
|-------|-----------|---------|------|-------|--------|

[ASCII bar chart of monthly completions]

**Trajectory**: [growing / stable / declining / erratic]
**Current velocity vs period average**: [above / at / below]

[Notable inflection points and hypothesized causes]

---

## 2. Backlog Health

**Total open issues**: [N] ([X] To Do, [Y] In Progress)
**Unestimated**: [N] ([X]%)
**Unassigned**: [N] ([X]%)

### Age distribution
| Age bucket  | Count | % of backlog |
|-------------|-------|-------------|
| 0-30 days   | N     | X%          |
| 30-90 days  | N     | X%          |
| 90-180 days | N     | X%          |
| 180+ days   | N     | X%          |

### Stale in-progress (>14 days)
| Issue | Summary | Assignee | Days in progress |
|-------|---------|----------|-----------------|

**Analysis**: [interpretation]

---

## 3. Bug Clusters

### Bugs by component
| # | Component | Bug count | % of all bugs |
|---|-----------|-----------|--------------|

### Bugs by label
| # | Label | Bug count |
|---|-------|-----------|

**Bug ratio**: [N]% of completed work was bugs (window average)

### Highest-risk areas (bugs + stale backlog)
| Component/Label | Bug count | Open bugs | Oldest open | Primary assignee |
|----------------|-----------|-----------|-------------|-----------------|

**These areas are the top candidates for dedicated attention or test investment.**

---

## 4. Assignee Concentration

### All work in window
| # | Assignee | Issues | % of total |
|---|----------|--------|------------|

### Current in-progress
| # | Assignee | Issues | % of in-progress |
|---|----------|--------|-----------------|

### Knowledge gap analysis
[Assignees active earlier in the window but absent recently, what they likely owned]

---

## 5. Firefighting Patterns

**Total P0/P1 issues**: [N] in [time window]
**Hotfix/escalation/incident labels**: [N]
**Frequency**: [X per month/quarter]

| Issue | Summary | Priority | Created | Resolved | Assignee |
|-------|---------|----------|---------|----------|---------|

[Analysis of what this signals about release process and stability]

---

## Cross-Reference Analysis

### Danger zones
[Components/areas that score badly across multiple dimensions — the highest-value findings]

### Positive signals
[Areas that show health — steady velocity, distributed ownership, low bug rate]

---

## Recommendations

1. [Most urgent action based on findings]
2. [Second priority]
3. [Third priority]
```

### 5. Present findings

- Tell the user where the report was saved
- Lead with the executive summary and risk matrix
- Highlight the cross-referenced danger zones — these are the highest-value findings
- If assignee concentration is concerning, call it out prominently
- Ask if they want to drill deeper into any specific area (e.g. read the actual stale issues, trace a specific assignee's workload, analyze a specific danger zone component)