Agent skill

Team Setup

by tikalk in tikalk/adlc-team-skills

A skill your agent uses when bootstrapping a team directives repository from scratch, cloning an existing one, pointing to a local path, or checking an existing configuration.

MITAuto-check passedDevelopment

Install Team Setup

skills CLI
$ npx skills add tikalk/adlc-team-skills --skill team-setup -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install tikalk/adlc-team-skills team-setup --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/tikalk/adlc-team-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/team/team-setup .claude/skills/team-setup && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
team-setup
GitHub stars
141
Token cost
~3.3k tokens
SKILL.md length
1,559 words
Files
9 (incl. references)
Skills in repo
44
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when bootstrapping a team directives repository from scratch, cloning an existing one, pointing to a local path, or checking an existing configuration.

  • Works in 4 steps: Explore: Present the user with four… → Present: For the chosen mode, explain… → Confirm: Ask the user to confirm before… → …
  • Bootstrapping a team directives repository from scratch
  • SKILL.md covers Overview, When to Use, Decline Handling (when… and Core Process, plus 5 more sections
  • Runs PowerShell and Shell scripts from its folder; calls git

What it does

Team Setup is an agent skill from tikalk/adlc-team-skills. Use when bootstrapping a team directives repository from scratch, cloning an existing one, pointing to a local path, or checking an existing configuration.

Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files, including reference files (for example `references/mode-clone.md`, `references/mode-configured.md` and `references/mode-local.md`).

It sits in Development. The repository describes itself as: Agent skills for the Agentic SDLC: team lifecycle (team-boot, team-learn, team-init, team-repair), software factory, evals, CDR lifecycle with confidence scoring, and… The licence is MIT.

When your agent uses it

  • Bootstrapping a team directives repository from scratch
  • Cloning an existing one
  • Pointing to a local path
  • Checking an existing configuration

Example prompts

  • “/team-setup”

Requirements

  • Python 3
  • A Bash shell
  • PowerShell

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Explore: Present the user with four options
  2. Present: For the chosen mode, explain what will happen and show details.
  3. Confirm: Ask the user to confirm before executing.
  4. Write/Execute: Perform the setup for the chosen mode.

What it can do on your machine

Read from SKILL.md and the folder at commit 2dbed36. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (PowerShell and Shell), which the agent can run.

    Shell commands in SKILL.md call:

    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Team Setup loads about 3.3k tokens when it runs, and up to ~7.9k if it reads all its reference files. Until then it costs about 42 tokens; SKILL.md has 1,559 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~42
When it runs · the whole SKILL.md, loaded when a task matches
~3.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from tikalk/adlc-team-skills at commit 2dbed36, republished under its MIT licence (© tikalk). 1,559 words, ~3,278 tokens.

Download SKILL.mdSave it as .claude/skills/team-setup/SKILL.md (or your agent's skills folder). This skill also uses 8 other files; get the full folder from GitHub.
name
team-setup
description
Use when bootstrapping a team directives repository from scratch, cloning an existing one, pointing to a local path, or checking an existing configuration.

team-setup

Overview

team-setup is an interactive skill that guides you through setting up the team AI directives. It presents four modes, explains each option, confirms your choice, and executes the setup.

It is invoked in two ways:

  • User-invoked (/team-setup) — anytime, to configure or check a project.
  • Model-invoked by team-boot — automatically at session start when a project has no .adlc/init-options.json configuration (self-install), so an unconfigured project wires itself without the user knowing the command.

The skill is non-destructive: it never overwrites existing files or directories. If the target path already contains a configured team AI directives, it detects this and offers the "Already configured" mode instead.

When to Use

  • Starting a new team from scratch and need a neutral team AI directives scaffold to fill in later.
  • Your team already has a directives repo on GitHub and you want to clone it locally.
  • You have a local team AI directives directory already (e.g., from a previous project) and want to wire it up.
  • You're unsure whether the team AI directives is already configured and want a quick check.
  • When the project isn't yet wired to a team AI directives (no .adlc/init-options.json team_ai_directives field).
  • Automatically via team-boot when it detects an unconfigured project at session start (self-install).

Decline Handling (when model-invoked by team-boot)

When team-boot invokes this skill because the project is unconfigured, the user may choose not to set up team AI directives right now. Handle decline explicitly to avoid a re-prompt loop:

  • If the user declines at mode selection, do not run any mode. Exit cleanly and tell team-boot the user declined.
  • Offer a persistent opt-out: "Don't ask again for this project?" On yes (build mode only), write .adlc/init-options.json with team_ai_directives: null:
    bash
    echo '{"team_ai_directives": null}' > ".adlc/init-options.json"
    This marker makes team-boot skip setup silently on every future prompt.
  • In plan/read-only mode, a persistent opt-out cannot be written — the decline is session-scoped only; tell team-boot to defer.
  • Never force a mode; the setup is user-consented at every step.

Core Process

Goal

Set up a team AI directives using one of four modes.

Security: Input Validation (all modes)

Before executing any mode, validate every user-supplied value (paths, URLs, team names). These values are interpolated into shell commands; unvalidated input is a command-injection vector.

  • Paths ({DEST}, {ABSOLUTE_PATH}): reject if they contain any of `, $, ;, |, &, (, ), <, >, newline, or backslash. Resolve to an absolute path with realpath/Resolve-Path before use.
  • Team name: must match ^[A-Za-z0-9 ._-]+$. Reject anything else.
  • Clone URL (Mode 1): must start with https://. Reject file://, ssh://, and any non-https scheme unless the user explicitly confirms the risk. Cloning runs no code from the repo, but the cloned content is read by agents later — only clone repositories you trust.

If any value fails validation, report which value and why, and re-ask. Never interpolate a user value into a Python/eval source string — pass it through the environment (see Mode 2).

Fast path: if .adlc/init-options.json already contains a valid team_ai_directives path, skip to Mode 4 (Already Configured) — do not re-clone, re-point, or re-scaffold.

Modes at a Glance
#ModeWhenDetails in
1Clone from GitHubteam already has a directives reporeferences/mode-clone.md
2Point to existing local pathdirectives dir already exists locallyreferences/mode-local.md
3Scaffold new empty directivesstarting a team from scratchreferences/mode-scaffold.md
4Already configuredcheck / verify existing wiringreferences/mode-configured.md
Mode 1: Clone from GitHub

Full walkthrough in references/mode-clone.md.

Mode 2: Point to Existing Local Path

Full walkthrough in references/mode-local.md.

Mode 3: Scaffold New Empty team AI directives

Full walkthrough in references/mode-scaffold.md.

Mode 4: Already Configured

Full walkthrough in references/mode-configured.md.

Mode Selection Flow
  1. Explore: Present the user with four options:

    How would you like to set up team-ai-directives?
    
    1) Clone from GitHub — Clone an existing repository
    2) Point to existing local path — Use a team AI directives you already have
    3) Scaffold new empty team AI directives — Create a fresh neutral team AI directives
    4) Already configured — Check existing configuration
  2. Present: For the chosen mode, explain what will happen and show details.

  3. Confirm: Ask the user to confirm before executing.

  4. Write/Execute: Perform the setup for the chosen mode.

Post-Setup Configuration

After any mode completes successfully, update the project configuration:

  1. Write team_ai_directives to .adlc/init-options.json
  2. Verify the team AI directives is accessible by running a quick health check:
    • {TEAM_AI_DIRECTIVES}/context_modules/constitution.md exists
    • {TEAM_AI_DIRECTIVES}/.skills.json exists and is valid JSON
  3. Check the .gitignore convention (ADR-401 R7 allowlist — see the Gitignore Convention Check step below).
  4. Inject the project-level AGENTS.md directive — full command and managed-section contract in references/post-setup-agents.md.
  5. Install MCP config — merge details in references/post-setup-mcp.md.
Gitignore Convention Check (ADR-401 R7)

Verify .gitignore follows the ADLC allowlist — a fresh team setup must never leave a wholesale .adlc/ ignore in place (it would hide the tracked .adlc/ artifacts the team model relies on):

  1. If .gitignore contains a bare .adlc/ rule, replace it (and any .adlc/* + !.adlc/... lines that conflict) with the R7 allowlist: ignore .adlc/*, .adlc/evals/results/, .adlc/memory/*, .adlc/team-learn-report.md, .adlc/team-levelup-report.md, and graphify-out/, while re-including !.adlc/init-options.json, !.adlc/workspace.yml, !.adlc/drafts/, !.adlc/evals/, !.adlc/memory/, !.adlc/memory/evals/, and !.adlc/memory/evals/holdout.json. The canonical rule list lives in the workspace skill's scripts/bash/paths.sh (GITIGNORE_RULES_ALLOWLIST, mirrored in scripts/powershell/paths.ps1).
  2. Also verify the agent-install surface rules are ignored (.agents/, .opencode/, .claude/, .cursor/, .codex/, .gemini/, .qwen/, .devin/, .tabnine/, skills-lock.json, .skills.json, .mcp.json, .events.json, .pytest_cache/, .ruff_cache/).
  3. Never remove unrelated (non-ADLC) rules; only add missing allowlist rules.
  4. Tracked legacy files keep working — the allowlist gates only untracked files, so no migration is required before the switch.
Show full SKILL.md (723 more words)Show less

Common Rationalizations

RationalizationWhy it's wrongWhat to do instead
"I'll just clone it manually."Manual cloning skips the .adlc/init-options.json wiring, so agents won't find the team AI directives.Use Mode 1 — it clones AND configures.
"I already have a team AI directives directory, I'll just use it."The directory may be incomplete (missing required files) or not wired in config.Use Mode 2 — it validates the structure and creates the config entry.
"I'll just create a few files by hand."An incomplete scaffold breaks health checks and agent discovery.Use Mode 3 — it creates all 10 required files with valid structure.
"I'm sure it's already configured."The path may be stale, moved, or the env var may point to a deleted dir.Use Mode 4 — it validates the existing configuration.
"Scaffolding without a team name is fine."The team name is used in README.md — a blank name makes the team AI directives anonymous and harder to audit.Always provide a team name in Mode 3.

Red Flags

  • Cloning over an existing directory — Mode 1 refuses if the destination already exists to prevent overwrites.
  • Pointing to a non-existent path — Mode 2 validates the path exists before proceeding.
  • Scaffolding without required dirs being writable — Mode 3 creates directories with mkdir -p but will fail on permission errors; check permissions first.
  • Skipping the team_ai_directives config write — without this field in init-options.json, agents cannot discover the team AI directives.
  • Using a relative path in init-options.json — always resolve to an absolute path so the config is portable across working directories.
  • Skipping git init in Mode 3 — a scaffolded team AI directives without git cannot be used by /team-levelup (branch/commit/PR flow). Mode 3 runs git init automatically; if you skip it, run git init manually before /team-levelup.
  • Skipping the project-level AGENTS.md injection — without the <!-- TEAM_AI_DIRECTIVES START --> managed section in the project's AGENTS.md, agents without event support have no session-start instruction to load team context. The .adlc/init-options.json config alone is insufficient — it tells skills where the team AI directives is, but nothing tells the agent to check. (For agents with event support, the session-start hook injects the orientation regardless, but AGENTS.md remains the fallback and the source of the Team Context in Use output contract.)
  • Interpolating user input into Python/shell source strings — pass paths through the environment (os.environ) instead; string interpolation of $ABSOLUTE_PATH into a Python one-liner is a command-injection vector.
  • Cloning a non-https:// URL in Mode 1 — reject file:///ssh:///other schemes; cloned content is read by agents later, so only clone trusted repos.
  • Skipping the MCP config install — .mcp.json servers stay unconfigured; the project won't have access to team-declared MCP servers.
  • Accepting shell metacharacters in paths or team names — validate before interpolating into mkdir/git commit/heredocs (see Input Validation).
  • Treating user decline as an error — declining setup is a valid outcome; exit cleanly, tell team-boot the user declined, and offer the team_ai_directives: null opt-out marker (build mode only).
  • Writing the opt-out marker in plan/read-only mode — a persistent opt-out requires a write; in plan mode the decline is session-scoped and setup defers instead.

Verification

  • The team AI directives directory exists at the configured path.
  • {TEAM_AI_DIRECTIVES}/context_modules/constitution.md exists.
  • {TEAM_AI_DIRECTIVES}/context_modules/rules/ exists.
  • {TEAM_AI_DIRECTIVES}/context_modules/personas/ exists.
  • {TEAM_AI_DIRECTIVES}/context_modules/examples/ exists.
  • {TEAM_AI_DIRECTIVES}/CDR.md exists.
  • {TEAM_AI_DIRECTIVES}/.skills.json exists and is valid JSON.
  • .adlc/init-options.json contains a team_ai_directives field with the absolute path.
  • .gitignore follows the ADR-401 R7 allowlist (no wholesale .adlc/ ignore; tracked exceptions !.adlc/init-options.json, !.adlc/workspace.yml, !.adlc/drafts/, !.adlc/evals/, !.adlc/memory/evals/holdout.json present).
  • Project-level AGENTS.md exists and contains the <!-- TEAM_AI_DIRECTIVES START --> managed section with the event-hook awareness note, fallback team-boot invocation, and the Team Context in Use output contract.
  • (Mode 3 only) git rev-parse --is-inside-work-tree succeeds inside {TEAM_AI_DIRECTIVES}.
  • Running team-verify (Phase 0 of team-repair) passes all 7 checks.
  • All user-supplied paths/URLs/team names passed Input Validation (no shell metacharacters; clone URL is https://).
  • Mode 2 wrote team_ai_directives via the environment (no $ABSOLUTE_PATH interpolation into Python source).
  • If {TEAM_AI_DIRECTIVES}/.mcp.json exists, any declared mcpServers were successfully merged into the project's config, and unresolved env vars were highlighted.
  • (Model-invoked by team-boot) a user decline exited cleanly without running any mode; the persistent opt-out was offered, and team_ai_directives: null was written only in build mode.

Configuration

  • TEAM_AI_DIRECTIVES — Path to the team AI directives (overrides .adlc/init-options.json).
  • .adlc/init-options.json — Project-level config file with team_ai_directives field.
  • Default fallback: team-ai-directives/ relative to project root.
  • team-helpers.sh / team-helpers.ps1 — Shared scripts used for scaffolding and path resolution.

12-Factor Alignment

Factor XI (Directives as Code) — establishes a version-controlled team directives repository.

© tikalk, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 8 other files (references) in skills/team/team-setup of tikalk/adlc-team-skills.

  • SKILL.md
  • references/mode-clone.md
  • references/mode-configured.md
  • references/mode-local.md
  • references/mode-scaffold.md
  • references/post-setup-agents.md
  • references/post-setup-mcp.md
  • team-helpers.ps1
  • team-helpers.sh

Open the folder on GitHubat commit 2dbed36

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Categories

Questions about Team Setup

What does Team Setup do?

A skill your agent uses when bootstrapping a team directives repository from scratch, cloning an existing one, pointing to a local path, or checking an existing configuration. Team Setup is an agent skill from tikalk/adlc-team-skills. Use when bootstrapping a team directives repository from scratch, cloning an existing one, pointing to a local path, or checking an existing configuration.

When should I use Team Setup?

Team Setup fits situations like: bootstrapping a team directives repository from scratch; cloning an existing one; pointing to a local path; checking an existing configuration.

How do I install Team Setup in Claude Code?

Run `npx skills add tikalk/adlc-team-skills --skill team-setup -a claude-code`. Or copy the skill folder (skills/team/team-setup in tikalk/adlc-team-skills) into .claude/skills/team-setup in your project. Claude Code loads it when a task matches its description.

How do I install Team Setup in Codex?

Run `npx skills add tikalk/adlc-team-skills --skill team-setup -a codex`. Or copy the skill folder (skills/team/team-setup in tikalk/adlc-team-skills) into .agents/skills/team-setup in your project. Codex loads it when a task matches its description.

Can I use Team Setup in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add tikalk/adlc-team-skills --skill team-setup -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/team-setup, .gemini/skills/team-setup, .github/skills/team-setup and .opencode/skills/team-setup in your project.

What does Team Setup need to run?

Going by SKILL.md and its folder, Team Setup needs PowerShell and a shell for the scripts in its folder and the command-line tools its instructions call (git). Our summary lists: Python 3; A Bash shell; PowerShell.

Does Team Setup access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Team Setup safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Team Setup use?

Team Setup is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Team Setup use?

About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 4.6k tokens, read only when the agent opens those files.

What are the alternatives to Team Setup?

Skills that share tags, products or a category with Team Setup: Finishing a Development Branch (obra/superpowers, 296k stars), Typescript Advanced Types (rolling-scopes/rsschool-app, 10k stars), PR Babysitter (openinterpreter/openinterpreter, 69k stars) and Code Review Checklist (shareAI-lab/learn-claude-code, 78k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Team Setup?

tikalk (a GitHub organization) maintains it in tikalk/adlc-team-skills, which has 141 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on October 6, 2026.

Source: tikalk/adlc-team-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.