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Seal

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GadaaLabs
seal

Use when implementation is complete and all tests pass — guides branch completion through four structured options (merge, PR, keep, discard) with verification and worktree cleanup

Overview

PublisherGadaaLabs
Repositoryclaude-code-on-steroids
Skill nameseal
Stars
67
Forks
10
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Seal 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/GadaaLabs/claude-code-on-steroids.git /tmp/claude-code-on-steroids
mkdir -p .claude/skills
cp -r /tmp/claude-code-on-steroids/skills/seal .claude/skills/seal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seal 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 Seal 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 Seal 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.

Finishing a Development Branch

Overview

SEALA seal closes and secures — nothing leaves without passing inspection. When invoked: verifies tests pass, presents exactly four structured completion options, executes the chosen path, and cleans up the isolated workspace. Nothing merges without evidence.

Core principle: Verify tests → Present options → Execute choice → Clean up.

Announce at start: "Running SEAL to complete this branch."


The Process

Step 1: Verify Tests

Before presenting any options:

bash
npm test / cargo test / pytest / go test ./...

If tests fail:

Tests failing (<N> failures). Must fix before completing:
[Show failures]
Cannot proceed until tests pass.

Stop. Do not present options.

If tests pass: proceed to Step 2.

Step 2: Determine Base Branch

bash
git merge-base HEAD main 2>/dev/null || git merge-base HEAD master 2>/dev/null

Or confirm: "This branch split from main — is that correct?"

Step 3: Present Exactly 4 Options

Implementation complete. What would you like to do?

1. Merge back to <base-branch> locally
2. Push and create a Pull Request
3. Keep the branch as-is (I'll handle it later)
4. Discard this work

Which option?

No extra explanation — keep options concise.

Step 4: Execute the Choice

Option 1 — Merge Locally
bash
git checkout <base-branch>
git pull
git merge <feature-branch>
<test command>          # Verify merged result
git branch -d <feature-branch>

Then: cleanup worktree (Step 5).

Option 2 — Push and Create PR
bash
git push -u origin <feature-branch>
gh pr create --title "<title>" --body "$(cat <<'EOF'
## Summary
<2-3 bullets of what changed>

## Test Plan
- [ ] <verification steps>
EOF
)"

Then: cleanup worktree (Step 5).

Option 3 — Keep As-Is

Report: "Keeping branch <name>. Worktree preserved at <path>."

Do not clean up the worktree.

Option 4 — Discard

Confirm first — require typed confirmation:

This will permanently delete:
- Branch <name>
- All commits: <commit-list>
- Worktree at <path>

Type 'discard' to confirm.

If confirmed:

bash
git checkout <base-branch>
git branch -D <feature-branch>

Then: cleanup worktree (Step 5).

Step 5: Cleanup Worktree (Options 1, 2, 4 only)

bash
git worktree list | grep $(git branch --show-current)
git worktree remove <worktree-path>

Option 3: keep worktree intact.


Quick Reference

OptionMergePushKeep WorktreeCleanup Branch
1. Merge locally
2. Create PR
3. Keep as-is
4. Discard✓ (force)

Red Flags

Never:

  • Present options before verifying tests
  • Merge without re-verifying tests on the merged result
  • Delete work without typed confirmation
  • Force-push without explicit user request

Always:

  • Verify tests before offering options
  • Present exactly 4 options — no more, no less
  • Require 'discard' typed confirmation for Option 4
  • Only clean up the worktree for Options 1 and 4

Integration

Called by:

  • phantom — after all plan tasks complete
  • exodus — after all execution batches complete

Pairs with:

  • vault — cleans up the worktree that VAULT created

Frequently asked questions

What does the Seal AI skill do?

Use when implementation is complete and all tests pass — guides branch completion through four structured options (merge, PR, keep, discard) with verification and worktree cleanup

Why use Seal on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/GadaaLabs/claude-code-on-steroids/tree/main/skills/seal. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Seal?

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 Seal?

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

Is the Seal AI skill free?

It is published on GitHub by GadaaLabs. Check the repository for licensing terms. 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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