backlog-grooming

backlog-grooming is a skill for Claude Code, Codex from AgiFlow/ai-plugin. It costs 55 tokens per session (442 once invoked), scanned A, a copy of backlog-grooming, MIT.

A backlog-review tool for Agiflow Planning tasks. Backlog grooming means checking planned work, clarifying it, ordering it, and grouping related tasks before development begins.

In plain words
What is it for?
Use it to assess task readiness, set priorities, group related work, check dependencies, and propose tasks for approval before moving them to Todo.
Why use it?
It helps prevent unclear, duplicated, blocked, or poorly specified tasks from reaching the work queue.

Skill for Claude CodeCodex

Part of the agiflow-ai-plugin plugin — 9 skills, 1 MCP server shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/agiflow/ai-plugin/backlog-grooming
Any agent
npx skills add AgiFlow/ai-plugin --skill backlog-grooming
Clone the repo
git clone --depth 1 https://github.com/AgiFlow/ai-plugin

Made for: Claude Code, Codex.

Or install agiflow-ai-plugin, the plugin that ships this one along with the rest of its 9 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for backlog-grooming

README.md
[![agentmods](https://agentmods.dev/badge/skills/agiflow/ai-plugin/backlog-grooming.svg)](https://agentmods.dev/skills/agiflow/ai-plugin/backlog-grooming)
Your own site
<a href="https://agentmods.dev/skills/agiflow/ai-plugin/backlog-grooming"><img src="https://agentmods.dev/badge/skills/agiflow/ai-plugin/backlog-grooming.svg" alt="Measured on agentmods" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 442 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00055 $0.00442
Opus 5 $0.00028 $0.00221
Sonnet 5 $0.00011 $0.00088
Haiku 4.5 $0.00006 $0.00044

Measured 5d ago against content hash 911830f4ec48, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

backlog-grooming scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to backlog-grooming — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/backlog-grooming/SKILL.md · 41 lines

What it actually says

Agiflow Backlog Grooming

Treat grooming as the quality gate between Planning and Todo.

Workflow

  1. Resolve the target project and call list_project_statuses to discover its exact status names.
  2. Call list_tasks for Planning tasks, list_work_units for existing active work units, and list_members for assignment context.
  3. Call get_task for tasks whose readiness, dependencies, or acceptance criteria are unclear.
  4. Classify every Planning task as:
    • Ready
    • Needs refinement
    • Blocked by a dependency
    • Duplicate or obsolete
  5. Require a clear outcome, sufficient context, and at least two testable acceptance criteria before promotion.
  6. Propose a priority order and explain the tradeoffs.
  7. Group three to eight cohesive tasks into a work unit only when they deliver one shared capability. Leave one or two related tasks standalone and split groups larger than eight.
  8. Review existing work units for shared dependencies and sequencing conflicts.
  9. Present the proposed promotions, work units, assignments, and dependency order. Request approval before writing.
  10. After approval:
    • Use batch_create_work_units for approved groups.
    • Use update_task to move only ready, ungated tasks to the exact Todo status and set their ordering.
  11. Verify with list_tasks and list_work_units.

Guardrails

  • Never promote a vague task or a task missing testable acceptance criteria.
  • Never promote a downstream task whose required upstream work is incomplete.
  • Do not force unrelated work into one work unit.
  • Do not modify records before the user approves the proposed grooming plan.
  • Recommend refine-task for tasks that fail readiness checks.

Response

Report promoted tasks, created work units, items left in Planning, dependency gates, and the recommended execution order.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 41 lines · 55 tokens per session scan A 911830f4ec48

Subscribe to this mod's changes

backlog-grooming is a skill published in the GitHub repository AgiFlow/ai-plugin (3 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 442 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to backlog-grooming, differing in 0 lines, and is treated as a copy.

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