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Backlog Generator

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murphytrueman
backlog-generator

Turn existing audit findings into sprint-ready tickets with T-shirt estimates, acceptance criteria, dependencies and phases. Triggers: tickets from audit, backlog from findings, sprint planning from report. Not for the contribution process (contribution-workflow).

Overview

Publishermurphytrueman
Repositorydesign-system-ops
Skill namebacklog-generator
Stars
211
Forks
7
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 murphytrueman on GitHub. Read the source before you install it.

Installation

Install the Backlog Generator 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.
    https://github.com/murphytrueman/design-system-ops/tree/main/skills/backlog-generator
  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/murphytrueman/design-system-ops.git /tmp/design-system-ops
mkdir -p .claude/skills
cp -r /tmp/design-system-ops/skills/backlog-generator .claude/skills/backlog-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Backlog Generator 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 Generator 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 Generator 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.

Backlog Generator

Before you begin: verify references

Confirm that every path in this skill's frontmatter references: exists relative to this SKILL.md. If any is missing, stop: the install is incomplete, usually because a flattening installer (for example npx skills install) dropped the repo-root knowledge-notes/ directory. Tell the user to reinstall by a method in 1-INSTALL.md and run verify-install.sh from the install root. Proceed without the references only if the user explicitly says to, and then say in the output that it was produced without the pack's reference material.

Context

The gap between "here are the problems" and "here is the plan for the next eight weeks" is where most audit value dies. Findings get filed, not acted on. Teams read the audit, nod, then move on because the step from "TA-07: Button component tokens not aliased correctly" to "what do we build next sprint?" is too large.

This skill bridges that gap by transforming audit output into structured work items that engineers and product managers can prioritise, estimate, and execute without needing to re-read the audit or negotiate what "fix the tokens" means. The output is sprints, not a wall of problems.

Boundaries

This skill transforms existing audit findings into work items. It does not run audits itself — if no audit output exists, run the relevant audit skill first. If the audit produced zero findings, there is no backlog to generate; confirm this with the user and stop. If the findings lack severity ratings or remediation guidance, flag the gap and produce work items with what is available, but note the missing context.


Steps

1. Accept and Parse Input

Accept input in any form: copy-paste audit report, file path, reference to prior audit skill output, or natural language description of findings.

Parse findings and extract:

  • Finding ID (e.g., TA-01, COL-03)
  • Severity: Critical, High, Medium, Low
  • Category: Token structure, component API, documentation, tooling, process
  • Current state: the problem as written in the audit
  • Remediation: what the audit says should happen

Do not assume findings are complete. If a finding is vague ("tokens are broken"), ask clarifying questions before proceeding.

Also ask for team size and sprint length (and, if they have one, the capacity they can give system work per sprint). Phases in step 4 are sized against these; without them, group by dependency and priority only and say the phases aren't capacity-checked.

Ask how the team sizes work. If they use story points, or their own T-shirt definitions, use theirs and say so; the scale in step 3 is the default for teams that don't have one. A backlog in a foreign sizing scale gets re-estimated in the planning meeting, which wastes the estimate.

2. Classify Work Item Type

For each finding, assign one of these work item types:

  • Bug fix: Something is broken and prevents correct usage (e.g., token alias creates circular reference, component prop ignored in doc site)
  • Tech debt: Working-as-designed but the design itself is unsustainable (e.g., token hierarchy is flat, component prop not typed)
  • Enhancement: New capability that enables better practice (e.g., new token tier, new component variant)
  • Migration: Moving from one system to another (e.g., migrating component token refs from old to new tier)
  • Documentation: Clarifying existing behaviour or adding examples

Document your reasoning for each classification. Different teams prioritise differently — tech debt might be lower priority than bugs, and that's a decision, not a mistake.

3. Generate Structured Work Item

For each finding, produce a work item with all of these fields:

Title: Action-oriented, engineer-readable. Prefer "Route component tokens through semantic tier" over "Fix TA-01" or "Token aliasing issue". The title should be understandable without the audit context.

Type: Bug fix, tech debt, enhancement, migration, or documentation (from step 2).

Effort estimate: in the team's own scale (step 1), or this default T-shirt scale with sizing rationale:

  • S: under half a day (single-file change, tests included, no API review needed)
  • M: half a day to two days (multi-file work, API implications, needs review)
  • L: three days to a week (multi-component impact, a migration step, cross-team coordination)
  • XL: more than a week (system-wide change, a major migration, new tooling); split it, or mark it for a scoping spike

Design system work is rarely three days when it crosses components or consumers, so the upper sizes run longer than a generic app scale.

Include 2–3 sentences of sizing rationale (e.g., "M because the Button component changes are isolated to one file, but we need to update three variants and test accessibility scenarios"). Do not estimate from severity — a critical bug might be 30 minutes (S) if it's a one-line fix.

Acceptance criteria: 2–4 testable statements. Each should be verifiable by a reviewer without ambiguity.

  • Good: "No component token directly references a primitive token (checked via regex in token definitions)"
  • Bad: "Fix tier leakage" or "Make tokens work better"

Evidence: the finding id and the audit's evidence for it (the file and line, token path or component it names), copied from the audit so the ticket stands without the report.

Rationale: 1–2 sentences explaining why this work matters to the system and team, not just a technical description. Address: What breaks or becomes harder if this is deferred? Who does this unblock? Cite roadmap or business impact only when the audit or the user states it; otherwise describe the technical consequence. Example: "Prevents new components from accidentally breaking the token hierarchy, so every new component token has to be re-checked by hand until this lands."

Dependencies: List other work item IDs or titles that must complete first. Be strict — only list blocking dependencies, not nice-to-haves. If a task has no dependencies, write "None".

Priority: Inherit from audit severity (Critical → P0, High → P1, Medium → P2, Low → P3), but adjust upward if the item unblocks multiple other items, or downward if high-effort with low impact.

4. Group into Phases

Sort work items into phases based on dependencies and effort:

  • Immediate (Sprint 1): Critical items and P1 work that has no dependencies or depends only on other Sprint 1 items. Fill to one sprint of the team's stated capacity.
  • Near-term (Sprint 2–3): P1–P2 items that depend on Immediate work, or independent P2 items. Fill to two sprints of stated capacity.

If the user hasn't given team size and sprint length, don't fill phases to a guessed capacity; group by priority and dependency and mark the phases "not capacity-checked".

  • Backlog (future): P3 items, nice-to-have enhancements, documentation that can wait.

Do not create a phase assignment that violates dependency order (e.g., assigning a Sprint 1 item that depends on a Sprint 2 item). Review the dependency DAG before finalising phase groups.

5. Build Dependency Map

Create a visual or textual dependency map showing which items block which other items. Format: simple text DAG or a markdown table with columns for Item, Depends On, Unblocks.

Flag any circular dependencies immediately — they indicate a misunderstanding of the problem or a need to re-scope work items. Example: If Item A depends on Item B and Item B depends on Item A, stop and clarify the actual order with the team.

6. Produce Markdown Output

Generate a markdown document with:

Headline: One or two sentences opening the document: what the backlog amounts to and what to start first (e.g. "Two critical token fixes unblock most of this backlog; start there").

Summary section:

  • Total findings analysed
  • Work item count by type (bugs, tech debt, enhancements, migrations, docs)
  • Work item count by phase (Immediate, Near-term, Backlog)
  • Effort: count of items per T-shirt size, plus the range those sizes imply (e.g. "[n] S, [n] M, [n] L: roughly [low]–[high] days"). Translate to weeks or sprints only against capacity the user supplied, and label it as an estimate
  • Dependency warnings, if any

Summary table: All work items in a single table with columns: ID, Title, Type, Effort, Priority, Phase, Dependencies. Keep descriptions short; readers will dive into detailed cards for context.

Detailed cards: One card per work item with all fields from step 3 (title, type, estimate with rationale, acceptance criteria, rationale, dependencies, priority).

Dependency section: Text or ASCII representation of the dependency DAG. Highlight any chain longer than 3 items (suggests the phase grouping may be too aggressive).

Tracker export (on request only): When the user wants the backlog in Jira, Linear, GitHub Issues or Asana, write a CSV next to the markdown with one row per item and the columns title, type, effort, priority, phase, dependencies, acceptance_criteria, rationale, evidence, source_finding (criteria and dependencies joined with ; ), which every tracker's importer accepts, and say which columns map to which fields in their tool (Effort → story points or a custom field; Priority → the tool's priority; Phase → sprint or a label). A JSON array of the same objects on request. No API calls.

End with Based on: [audit report, date] and a Scope block: which findings were converted, which were left out and why (no remediation, duplicates of another item), and whether the phases were capacity-checked.

Quality Checks

  1. Every work item title is action-oriented and engineer-readable. A developer should understand what to build without re-reading the audit. Titles like "Flatten token hierarchy" or "Add size variant to Button" pass; "Fix token issue" does not.

  2. Acceptance criteria are testable. Each criterion should be verifiable by code review, test output, or inspection without debate. "Button component uses semantic tokens" is vague; "Button component has zero direct references to primitive tokens" is testable.

  3. Effort estimates include sizing rationale, not just a letter. Why is this S and not M? Why is that L and not XL? Document the reasoning (scope, risk, coordination overhead, etc.).

  4. Rationale explains business impact, not just technical description. Do not repeat the audit finding. Explain why engineers should care, what risk the team avoids, what capability it unblocks.

  5. Dependencies form a valid order. No circular references, directly or transitively; walk the chains before finalising.

  6. Every card carries the finding's evidence and the backlog ends with Based on: and a Scope block.

  7. Phase grouping respects dependency order. No Sprint 1 item can depend on a Sprint 2 item. No Sprint 2 item can depend on a Backlog item (unless you're willing to schedule Backlog work early). Review the dependency map against phase assignments before finalising.

Small-System Note

For audit reports with fewer than 10 findings, produce a single prioritised list (Immediate, Near-term, Backlog) rather than phased sprints. The structure is the same; the grouping is simpler. Dependencies still matter even for small systems.

Frequently asked questions

What does the Backlog Generator AI skill do?

Turn existing audit findings into sprint-ready tickets with T-shirt estimates, acceptance criteria, dependencies and phases. Triggers: tickets from audit, backlog from findings, sprint planning from report. Not for the contribution process (contribution-workflow).

Why use Backlog Generator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/murphytrueman/design-system-ops/tree/main/skills/backlog-generator. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Backlog Generator?

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 Generator?

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

Is the Backlog Generator AI skill free?

Yes. It is published on GitHub by murphytrueman 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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