Backlog Grooming

OrganizationPopular

hashgraph-online

backlog-grooming

Review Agiflow Planning tasks for readiness, prioritize approved work, group related tasks into work units, and promote ready tasks to Todo. Use when grooming a backlog, organizing planned tasks, creating work units, or deciding what should be executed next.

Overview

Publisher	hashgraph-online
Repository	`awesome-codex-plugins`
Skill name	`backlog-grooming`
Stars	1.3K
Forks	360
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository Homepage

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 hashgraph-online on GitHub. Read the source before you install it.

Installation

Install the Backlog Grooming AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/AgiFlow/ai-plugin/skills/backlog-grooming
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/AgiFlow/ai-plugin/skills/backlog-grooming

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/hashgraph-online/awesome-codex-plugins.git /tmp/awesome-codex-plugins
mkdir -p .claude/skills
cp -r /tmp/awesome-codex-plugins/plugins/AgiFlow/ai-plugin/skills/backlog-grooming .claude/skills/backlog-grooming

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Backlog Grooming 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 Backlog Grooming 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 Backlog Grooming 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.

Agiflow Backlog Grooming

Treat grooming as the quality gate between Planning and Todo.

Workflow

Resolve the target project and call list_project_statuses to discover its exact status names.
Call list_tasks for Planning tasks, list_work_units for existing active work units, and list_members for assignment context.
Call get_task for tasks whose readiness, dependencies, or acceptance criteria are unclear.
Classify every Planning task as:
- Ready
- Needs refinement
- Blocked by a dependency
- Duplicate or obsolete
Require a clear outcome, sufficient context, and at least two testable acceptance criteria before promotion.
Propose a priority order and explain the tradeoffs.
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.
Review existing work units for shared dependencies and sequencing conflicts.
Present the proposed promotions, work units, assignments, and dependency order. Request approval before writing.
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.
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.

Frequently asked questions

What does the Backlog Grooming AI skill do?

Why use Backlog Grooming on TypingMind?

Because you install it once and use it with any model. Backlog Grooming 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 Backlog Grooming in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hashgraph-online/awesome-codex-plugins/tree/main/plugins/AgiFlow/ai-plugin/skills/backlog-grooming. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Backlog Grooming?

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 Backlog Grooming?

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

Is the Backlog Grooming AI skill free?

Yes. It is published on GitHub by hashgraph-online under the Apache-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Agiflow Backlog Grooming

Workflow

Guardrails

Response

Frequently asked questions

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