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Auto Repo Setup

CommunityPopular
daymade
auto-repo-setup

Diagnose, repair, and standardize repository setup and safe Git workflows for Claude Code or Codex. Use when a repository will not run, a collaborator is onboarding, dependencies or credentials are missing, the user wants startup sync, SessionStart output is duplicated, project instructions or hooks need auditing, or commit/push/conflict/history-cleanup needs a guarded workflow. Route ordinary startup behavior through project instructions or a natural language request; use lifecycle hooks only when behavior must occur before the first prompt and the target runtime has been verified.

Overview

Publisherdaymade
Repositoryclaude-code-skills
Skill nameauto-repo-setup
Stars
1.4K
Forks
219
Bundled files
9
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.

  • 9 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by daymade on GitHub. Read the source before you install it.

Installation

Install the Auto Repo Setup 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.
  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/daymade/claude-code-skills.git /tmp/claude-code-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-skills/auto-repo-setup .claude/skills/auto-repo-setup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Auto Repo Setup 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 Auto Repo Setup 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 Auto Repo Setup 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.

Auto Repo Setup

Make the repository usable without changing the user's normal way of working. Treat setup as an evidence-driven repository task, not as a special interface for people with a particular job title.

Entry router

Classify the request before changing anything. Do not combine modes merely because they all contain the word "setup".

User outcomeModeFirst action
"It won't run", missing dependency, new machineEnvironment repairRead project instructions and detect the actual stack
"Sync before we work", "pull the latest"Session routineUse project instructions or execute the user's natural-language request
Repeated or unexpected hook outputHook diagnosisCount registrations and firing sessions before editing configuration
A teammate needs a repeatable handoffRepository handoffAudit existing onboarding and fill only the missing operational contract
Commit, push, conflict, leaked secret/historyGit safetyRead references/git_safety.md before mutation
Behavior must happen before the first promptStartup automationRead references/startup_automation.md and pass its hook gate

Operating principles

  1. Do not infer competence from role. Match the user's demonstrated altitude. A collaborator can tell an agent "sync the remote first"; do not invent a launcher, shortcut, or wizard unless the user asked for one.
  2. Use the least mechanism that satisfies the outcome. Prefer, in order: direct Agent instruction → project instruction → explicit command → lifecycle hook. A hook is not a more professional version of a sentence.
  3. Inspect authority before producing. Read the current user request, then project instructions, onboarding/runbooks, manifests, lockfiles, and actual command output. Do not make ONBOARDING.md, Python, uv, ffmpeg, or .env mandatory for repositories that do not declare them.
  4. Preserve user work. Never auto-stash, merge, rebase, force, discard, or overwrite local changes to make setup look successful.
  5. Define a falsifiable success state. "Dependencies installed" is not enough; run the project's real smoke test or startup command and verify the observable result.
  6. Keep diagnosis read-only until the cause is known. Installation, config edits, hook registration, commits, and external writes require the authority implied by the user's request; destructive or public actions require explicit approval.

Workflow A — Environment repair

A1. Read the project map

Read only files that exist, in this order:

  1. AGENTS.md and AGENTS.override.md
  2. CLAUDE.md and scoped project rules
  3. ONBOARDING.md or the repository's named setup/runbook
  4. README.md
  5. Manifests, lockfiles, task runners, CI, and container configuration
  6. Existing Claude/Codex settings only when the request concerns agent behavior

If no onboarding guide exists, infer the setup path from authoritative manifests and exercised commands. Do not stop to ask whether to create documentation unless a durable handoff is part of the request.

A2. Run a read-only capability inventory

Use the bundled scripts/check_env.py against the repository root when a Python 3.10+ runner is already available. It detects the declared ecosystem and checks only the relevant toolchain; it does not install dependencies or read secret values. If Python is itself the missing prerequisite, perform the same read-only manifest inventory directly instead of installing Python merely to run the audit.

bash
uv run python scripts/check_env.py --repo <repo-root>
uv run python scripts/check_env.py --repo <repo-root> --json

Treat the inventory as evidence, not as the project setup specification. A custom runbook or CI workflow can require tools the generic inventory cannot infer.

Expected result:

  • exit 0: all toolchains inferred from manifests are available;
  • exit 1: one or more inferred prerequisites are missing;
  • exit 2: the repository or its metadata could not be inspected safely.

A3. Repair the root cause

For each failed requirement:

  1. Capture the exact command, exit code, stdout, and stderr.
  2. Trace the failure to the declaring source: project guide, manifest, lockfile, configuration, or runtime log.
  3. Apply the smallest project-consistent repair.
  4. Re-run the failed check before moving on.

Do not restart, reinstall everything, or switch package managers as a first move. Do not print .env or credential contents; verify presence and behavior without echoing values.

A4. Verify the product path

Run the project's documented smoke test, build, or start command. If none exists, derive one from CI/task-runner configuration and label the derivation. Verify the observable result, not merely a zero exit code.

Report:

  • what was broken and the evidence;
  • what changed;
  • the exact verification and result;
  • any remaining user action or blocked authority.

Workflow B — Routine Git sync

For "sync before work", prefer a short project instruction shared by the agents that use the repository. The routine is:

  1. Inspect branch, upstream, and working-tree state.
  2. If the working tree is clean, run git pull --ff-only.
  3. If there are local changes, do not auto-stash or pull; explain the state.
  4. If local and remote histories diverged, do not auto-merge, rebase, or force; explain the state.
  5. If the network fails, say so and continue locally only when the user's task can safely proceed on the local version.

A collaborator may also simply say:

Sync the latest remote version before starting.

That is a normal Agent instruction, not a degraded fallback.

Do not register SessionStart merely to automate this routine. Static behavior belongs in AGENTS.md/CLAUDE.md; the Agent can inspect context and handle exceptional Git states instead of hiding them in a shell process.

Workflow C — Repository handoff

Create or revise onboarding only when the user wants a durable handoff and the existing project map does not already provide one.

Use references/onboarding_template.md as a checklist, not as a literal Python/video template:

  • derive prerequisites from manifests and actual project commands;
  • include expected output after each command;
  • separate one-time setup from daily use;
  • include recovery for real observed failures;
  • keep project instructions as the operational SSOT and avoid duplicating values.

Validate every command on the target operating systems that matter to the user.

Workflow D — Startup automation and hook diagnosis

Read references/startup_automation.md before adding or changing any hook.

Diagnose repeated output first

  1. Enumerate project, local, user, managed, and plugin hook registrations.
  2. Count matching entries; do not infer "three registrations" from three outputs.
  3. Identify which root sessions, resumes, compactions, or subagents fired them.
  4. Verify the event matcher and current runtime payload.
  5. State the root cause before proposing a change.

Install only after the hook gate passes

The bundled initializer is Claude Code-specific and installs only a lightweight startup context nudge. It preserves unrelated settings, adds matcher startup, validates the guide path, writes atomically, and is idempotent.

Preview first:

bash
uv run python scripts/init_session_start_hook.py \
  --repo <repo-root> \
  --guide ONBOARDING.md \
  --dry-run

After the user confirms that pre-prompt injection is genuinely required, run the same command without --dry-run. Remove only the managed entry with --remove.

Never copy this configuration into Codex by analogy. Codex project behavior should normally live in AGENTS.md; if a Codex hook is truly required, verify the installed version's root/subagent behavior and payload first.

Git safety

Read references/git_safety.md before commit, push, conflict resolution, or history rewrite. The load-bearing rules are:

  • stage only intended paths;
  • never bypass hooks unless the user explicitly typed the bypass in this session;
  • verify repository visibility from the hosting service before push;
  • get explicit approval before public push, force push, or history rewrite;
  • resolve conflicts from project semantics, never by blindly choosing "ours" or "theirs";
  • revoke leaked credentials before treating history cleanup as complete.

Use references/pii_guard.md for public-distribution content review. A green scanner does not replace semantic review.

For a read-only history scan, the existing scripts/sanitize_history.sh remains available. It never rewrites history; treat its findings as candidates and do not execute rewrite commands without explicit approval.

Counter-review boundary

Use counter-review when the approved work materially changes security policy, shared lifecycle hooks, CI/CD, dependencies, or destructive Git behavior. Do not spawn a review team for an ordinary setup check or a one-line project instruction. Filter every finding by probability, cost, real usage, and direct verification.

Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Auto Repo Setup AI skill do?

Diagnose, repair, and standardize repository setup and safe Git workflows for Claude Code or Codex. Use when a repository will not run, a collaborator is onboarding, dependencies or credentials are missing, the user wants startup sync, SessionStart output is duplicated, project instructions or hooks need auditing, or commit/push/conflict/history-cleanup needs a guarded workflow. Route ordinary startup behavior through project instructions or a natural language request; use lifecycle hooks only when behavior must occur before the first prompt and the target runtime has been verified.

Why use Auto Repo Setup on TypingMind?

Because you install it once and use it with any model. Auto Repo Setup 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 Auto Repo Setup in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/daymade/claude-code-skills/tree/main/auto-repo-setup. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Auto Repo Setup?

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 Auto Repo Setup?

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

Is the Auto Repo Setup AI skill free?

Yes. It is published on GitHub by daymade 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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