Factory Queue

Organization

tikalk

factory-queue

Use when managing mission brief intake — triage, AI-assisted advisory scoring, gating label stamping, and milestones or epics generation (plan mode).

Overview

Publisher	tikalk
Repository	`adlc-team-skills`
Skill name	`factory-queue`
Stars	144
Forks	2
Bundled files	Instructions only
License	MIT
Links	Skill source Repository

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 tikalk on GitHub. Read the source before you install it.

Installation

Install the Factory Queue 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/tikalk/adlc-team-skills/tree/main/skills/factory/factory-queue
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.

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/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/factory/factory-queue .claude/skills/factory-queue

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

Use it in TypingMind

Enable Factory Queue 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 Factory Queue 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 Factory Queue 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.

factory-queue

What this skill does

factory-queue manages the ingestion, triage, and planning boundaries of the software factory. It operates directly against the external issue tracker (the Queue) as the single source of truth using the tracker-agnostic integration layer (factory-mission/references/tracker-integration.md).

It performs two primary control-plane operations:

Advisory Triage & Ingestion (default triage mode): Pulls un-triaged candidate briefs, runs stateless AI triage scoring, presents the Intent Gate to the human, and stamps gating/dispatch labels. Before stamping executing on any issue, applies the dependency hard gate (Operation 12): an issue whose **Depends on** line names still-open dependencies is parked at spec-gated (never stamped executing) with a blocked: dependency-gate note naming the blockers.
Product Planning (plan mode): Reads accepted product/architecture decisions and the PRD/AD, generates milestones (done-means) and epics, and pushes them as structured, labeled issues to the Q.

When to use

At product intake, to triage candidate briefs and approve them for execution.
After a product/architect lifecycle converges, to decompose the specification into prioritized tracker issues.
You want to run an advisory-only Intent Gate with automatic label transition gating.

When NOT to use:

For inner-loop feature execution (use factory-mission instead).
As a standalone markdown generator (this requires issue-tracker connectivity).

Operating Modes

1. Ingestion & Triage Mode (default)

Operates on candidate issues (labeled intent) or local draft briefs:

Pull Candidate Briefs: Discover the active tracker provider and fetch issues with status intent (factory-mission/references/tracker-integration.md).
AI Triage Scoring: Compute stateless advisory scores on three dimensions:
- Risk: Blast radius, data sensitivity, and architectural impact (Low / Medium / High).
- Complexity: Scope and cross-service dependencies (Low / Medium / High).
- Agent Confidence: Estimation of end-to-end execution success (0-100%, High/Medium/Low bands).
Intent Gate Presentation: Present candidate briefs + advisory scores to the human. If running unattended (no active human terminal), publish the Intent Gate presentation and AI scores as a structured comment with marker  on the candidate issue. The queue run pauses. The human reviews the proposal, and approves by commenting "approve" or changing the label to spec-gated. When resumed, the queue reads the comment/label state and continues. The Human Intent Gate is non-negotiable; no brief is auto-approved or auto-rejected.
Label Stamping:
- On approval: Transition lifecycle label intent ──► spec-gated. Stamp automation-gating (agent-can-execute or human-required) and dispatch (autonomous | supervised | interactive) labels.
- On rejection: Transition intent ──► cancelled (or delete draft).
- On deferral: Move to backlog.

2. Plan Mode

Operates post-lifecycle convergence to populate the backlog:

Load Artifacts: Read accepted PDRs + ADRs and the generated docs/adlc/product/PRD.md / docs/adlc/architect/AD.md. When no team record-class index was injected at session start, read the binding records directly from docs/adlc/memory/ (dual-read order: docs/adlc/memory first, legacy .adlc/memory fallback) and state that fallback in one line.
Generate Milestones & Epics: Extract sequencing, requirement groupings, and "done-means" definitions into prioritized epics and milestones. Gate/task issues MUST use the mission-brief format — Goal / Constraints / Non-Goals / Success Criteria (each criterion with a measurement method) — per the canonical template in factory-mission's references/mission-brief-template.md. Milestone descriptions carry the demo sentence + done-means (milestones are grouping containers, not executable units). Issue titles are short work-item names; [Gx]/Mx: prefixes are forbidden (milestone assignment carries grouping; descriptions carry references).
Deduplication Check: Fetch existing issues to match task summaries and prevent duplicates.
Push to Q: Push epics and milestone issues to the tracker via MCP. Stamp appropriate automation-gating and dispatch labels on each generated issue based on AI triage.
Output: Write a versioned docs/adlc/product/roadmap.md linking the local spec elements to the newly created external tracker issues.

Invariants & Safety Constraints

Stateless: The platform holds no queue state of its own. All queries and mutations are performed live via the tracker's MCP/CLI connectors.
Dry-Run Gating: No tracker write may occur without dry-run confirmation. The skill must output a detailed preview of all issues, comments, or label changes, requiring the user to explicitly confirm before execution.
Advisory Scores: Scores are advisory metadata and never bypass human gates.

Frequently asked questions

What does the Factory Queue AI skill do?

Use when managing mission brief intake — triage, AI-assisted advisory scoring, gating label stamping, and milestones or epics generation (plan mode).

Why use Factory Queue on TypingMind?

Because you install it once and use it with any model. Factory Queue 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 Factory Queue in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/factory/factory-queue. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Factory Queue?

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 Factory Queue?

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

Is the Factory Queue AI skill free?

Yes. It is published on GitHub by tikalk 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

factory-queue

What this skill does

When to use

Operating Modes

1. Ingestion & Triage Mode (default)

2. Plan Mode

Invariants & Safety Constraints

Frequently asked questions

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