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.
npx skills add tikalk/adlc-team-skills --skill factory-queuegit clone --depth 1 https://github.com/tikalk/adlc-team-skillsWrote 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.
[](https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-queue)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-queue"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-queue/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-queue"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-queue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.01135 |
| Opus 5.5 | $0.00013 | $0.00454 |
| Sonnet 5.5 | $0.00007 | $0.00227 |
| Haiku 4.5 | $0.00003 | $0.00113 |
Grade A, and why
factory-queue 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
triagemode): Pulls un-triaged candidate briefs, runs stateless AI triage scoring, presents the Intent Gate to the human, and stamps gating/dispatch labels. - Product Planning (
planmode): 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-missioninstead). - 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
<!-- factory-queue:triage:proposal -->on the candidate issue. The queue run pauses. The human reviews the proposal, and approves by commenting "approve" or changing the label tospec-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-executeorhuman-required) and dispatch (autonomous|supervised|interactive) labels. - On rejection: Transition
intent──►cancelled(or delete draft). - On deferral: Move to backlog.
- On approval: Transition lifecycle label
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.
- 10d ago Changed 3d71e05e7ca2
- 15d ago Changed e74df7a73643
- 21d ago Changed 8ffb8e2f752b
- 25d ago First seen · 64 lines · 33 tokens per session scan A 29ac5b0d1e14
factory-queue is a skill published in the GitHub repository tikalk/adlc-team-skills (141 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 1,135 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-21.
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post-issue-comment
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ticket-craft
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create-scrap-issue
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project-plan
Turn a product goal or feature request into a clear Agiflow project plan with small, testable tasks in Planning status. Use when starting a project, decomposing a feature, clarifying requirements, or converting an idea into an actionable backlog.