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-setupgit 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-setup)<a href="https://agentmods.dev/skills/tikalk/adlc-team-skills/factory-setup"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-setup/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-setup"><img src="https://agentmods.dev/badge/skills/tikalk/adlc-team-skills/factory-setup.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 | $0.00035 | $0.01172 |
| Opus 5.5 | $0.00014 | $0.00469 |
| Sonnet 5.5 | $0.00007 | $0.00234 |
| Haiku 4.5 | $0.00003 | $0.00117 |
Grade A, and why
factory-setup 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 6d 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
factory-setup
Overview
factory-setup is an interactive skill that wires a repo to its issue tracker
for the factory (factory-mission, factory-queue, factory-review). It
asks which tracker the repo uses, then writes .adlc/issues-provider.yml
from the release template — provider and field mappings only, never
credentials. It is invoked three ways:
- User-invoked (
/factory-setup) — anytime, to configure or check a project. - Model-invoked by
team-boot— suggested when factory skills are installed but no provider config resolves (self-install hint, liketeam-setup). - Headless via
adlc-cli factory setup— scripted runs;--providerpre-answers the pick so no question is asked.
The skill is non-destructive: it never overwrites an existing
.adlc/issues-provider.yml. If one exists, it verifies the wiring instead
(Mode 3).
When to Use
- A tracker run halted with "no provider resolves" and named this skill.
- Setting up a new repo for factory runs (GitHub, GitLab, Linear, or Jira).
- You're unsure which tracker a repo is wired to and want a quick check.
- Switching a repo from one tracker to another (re-pick, file rewritten only on explicit confirmation).
team-bootsuggested it because factory skills are installed but no provider config resolves.
Decline Handling (when model-invoked by team-boot)
The user may choose not to configure the tracker right now. Handle decline explicitly to avoid a re-prompt loop:
- If the user declines, do not write anything. Exit cleanly; the decline is session-scoped only.
- There is deliberately no persistent opt-out marker: the marker would
live in
.adlc/init-options.json, whose schema is shared with adlc-cli — a tracker-specific key there is schema churn for a session preference. team-boot simply asks again next session. - Never force a mode; setup is user-consented at every step. In headless
runs there is no one to ask —
--provideris required, else halt.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 6d ago First seen · 105 lines · 35 tokens per session scan A 0af2e6b2d623
factory-setup is a skill published in the GitHub repository tikalk/adlc-team-skills (141 stars, last pushed yesterday), licensed MIT. It adds 35 tokens to every session and 1,172 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 2026-10-10.
Other skills, from other repositories
batch-all-issues
Resolve every open issue one at a time: fact-check each with web research, close the ones that need no action, and run the goal-pr skill to fix, review, and merge the ones that do — repeating until no actionable issues remain.
post-issue-comment
Post a reply comment on a GitHub issue in English with a natural, concise writing style, based on the intent passed as arguments.
orchestrate
Pipeline orchestration: dispatch the highest-priority ready tasks/work units to agents, manage capacity, and coordinate the Todo to Done flow. Invoked as /agiflow:orchestrate. Uses listtasks, listactivetasksbyorg, listmembers, updatetask, getworkunitprogress.
ticket-craft
Create Jira/Asana/Linear tickets optimized for Claude Code execution - AI-native ticket writing.
create-scrap-issue
Create a GitHub issue that consolidates passed content into a single scrap issue with background context and solution details, labeled as maintainer-scrap. Use when the user wants to create a scrap issue, jot down notes as a GitHub issue, or save findings for later.
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.