# Groom Backlog Item

> Prepare backlog items for work by discovering related context.

- Skill: `tools-only/groom-backlog-item-6` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add tools-only/groom-backlog-item-6`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tools-only/groom-backlog-item-6/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Product & Planning
- Author: tools-only (https://skillmd.com/u/tools-only)
- Updated: 2026-09-29
- Page: https://skillmd.com/skills/tools-only/groom-backlog-item-6

---


# Groom Backlog Item

Prepare backlog items for work by discovering related context.

## Arguments

`$ARGUMENTS` can be:

- **Item title substring**: e.g., "Error Recovery" - grooms matching item
- **Section**: e.g., "P1" or "P2" or "Ideas" - grooms all items in section
- **"all"**: grooms all items (uses parallel agents)

## Workflow

### Step 1: Parse Arguments and Load Backlog

Read `.claude/BACKLOG.md` and identify target items.

**If title substring**: Find items whose title contains the substring (case-insensitive)
**If section**: Collect all items under that priority section
**If "all"**: Collect all items from P0, P1, P2, Ideas

### Step 2: Extract Item Details

For each target item, extract:

- Title
- Description
- Research first questions (if present)
- Source
- Suggested location

### Step 3: Spawn Groomer Agents

**For single item**: Run `@backlog-item-groomer` inline

**For multiple items**: Spawn parallel agents using Task tool:

```text
Task(
  subagent_type: "general-purpose",
  prompt: "Act as @backlog-item-groomer. Groom this item: {item details}",
  model: "haiku"
)
```

Spawn up to 5 agents in parallel. If more than 5 items, batch in waves.

### Step 4: Collect Results

Gather context manifests from all agents.

### Step 5: Produce Summary Report

Create a grooming report:

```markdown
# Backlog Grooming Report

**Date**: {YYYY-MM-DD}
**Items groomed**: {count}
**Arguments**: {original arguments}

## Summary

| Item | Research Found | Skills | Agents | Blockers |
|------|----------------|--------|--------|----------|
| {title} | {count} | {count} | {count} | {count} |

## Individual Manifests

### {Item 1 title}
{manifest from agent}

### {Item 2 title}
{manifest from agent}

...

## Cross-Item Findings

### Shared Dependencies
- {items that multiple backlog items depend on}

### Suggested Groupings
- {items that could be worked together}

### Research Gaps
- {topics needing research-and-compare runs — skill moved to [stateless-agent-methodology](https://github.com/bitflight-devops/stateless-agent-methodology) repo}
```

### Step 6: Save Report (Optional)

If grooming multiple items, offer to save report to:

```text
.claude/grooming-reports/grooming-{YYYY-MM-DD}.md
```

## Example Usage

```text
# Groom a specific item
/groom-backlog-item Error Recovery

# Groom all P1 items
/groom-backlog-item P1

# Groom everything
/groom-backlog-item all
```

## Success Criteria

- [ ] Target items identified from arguments
- [ ] Groomer agent(s) spawned for each item
- [ ] Context manifests collected
- [ ] Summary report produced
- [ ] Cross-item findings identified (if multiple items)