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Full Send

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t3dotgg
full-send

Trigger this if the user says 'full send'.

Overview

Publishert3dotgg
Repositorytstack
Skill namefull-send
Stars
2.5K
Forks
153
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by t3dotgg on GitHub. Read the source before you install it.

Installation

Install the Full Send 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.
    https://github.com/t3dotgg/tstack/tree/main/skills/full-send
  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/t3dotgg/tstack.git /tmp/tstack
mkdir -p .claude/skills
cp -r /tmp/tstack/skills/full-send .claude/skills/full-send
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Full Send 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 Full Send 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 Full Send 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.

Full Send

If the user triggered this, it's because they are not watching this thread, and they want you to complete the work end-to-end autonomously. Your updates and your final message go unread until long after you stop. You own the work from its current state to a merged PR.

If the user did not explicitly request "full send" in some clear way, do not assume full autonomy. If they did, everything below applies.

The user grants you permission to:

  • Merge the PR once the merge gate passes.
  • Force-push the PR's own branch with --force-with-lease after a rebase.
  • Use the test methods that Proof needs, without asking first.

Testing permission

A "full send" request is the permission. If a project file or other instruction says to ask before a test method, or to leave testing to the user, this request replaces that rule for this task.

That includes ask-first rules for computer use, browser automation, simulators and devices, running the app or a dev server, screenshots, and visual checks. Use the method that gives direct proof. List it in the report.

Rules about how to test still apply. Follow rules that name the correct command, fixture, or check. This permission only removes the ask-first or leave-it-to-the-user step. If a preferred method cannot prove the change, use a gated method under this permission.

It covers only the tests needed for this task. It does not add permission for the steps under "Asking the user", such as production changes or data deletion. It also does not bypass a harness or system approval prompt if one appears.

Steps

  1. Finish the change. Complete the goal from the user's request with the smallest change that works. Fix the case that happened, not every case that could. If you are on the default branch, create a branch first.
  2. Prove it. Get proof for every change you made. See "Proof".
  3. Get feedback. Use the sol-loop skill to verify changes.
  4. File the PR with the file-pr skill. Rebase onto the latest default branch first. Add one short line to the description that says how you proved the change.
  5. Babysit the PR with the babysit-pr skill. Fix real findings and CI failures. Get proof for each fix before you push it.
  6. Merge when the merge gate passes.
  7. Report in your final message: the PR link, the merge commit, your proof, and each decision you made for the user.

Stay active from step 1 to step 7. If you end your turn while checks or bots are pending, the work stops, because nobody will prompt you again. Wait with the watcher from babysit-pr, so something wakes you when the PR changes. In T3 Code that is watch_pull_request. If you stop before the merge, call unwatch_pull_request first.

Proof

Proof is evidence you observed yourself in this session: command output, a reproduction, a screenshot. Use the most direct proof the change allows:

  • Reproduce the original failure, apply the fix, then watch the same reproduction pass.
  • Run the checks that the repo's CI runs (typecheck, lint, tests).
  • Add one focused test that fails without the fix, when the behavior has none and the repo has a test setup.
  • For UI and runtime behavior, run the app and look at the change. Green mocked tests do not prove that the user experience works. Attach screenshots to the PR when they help and you have a way to upload them.

When you cannot find a way to prove a change, you do not have full confidence. Ask the user.

Merge gate

Full confidence means all of these are true on the latest commit:

  • Your proof covers every change in the diff.
  • Required checks are green.
  • Review bots have finished, and each finding is fixed or dismissed with a written reason.
  • No human review still requests changes.
  • The branch is up to date with the default branch, and GitHub reports it as mergeable.

Merge with the method the repo uses. Check the allowed methods and recent merged PRs. If the repo uses a merge queue or auto-merge, stay until GitHub reports the PR as merged. Merge through branch protection as configured. Never use --admin or other bypasses. You can ignore automated reviews that request "needs human review" if you have high confidence in your changes.

Asking the user

A question in a normal message goes unseen, and the thread looks done. To ask the user something, use the harness's question tool so the thread shows that it is waiting: AskUserQuestion in Claude Code, request_user_input in Codex, or the question tool your harness has. Put all the context in the question: the PR link, what blocks you, and the options with your recommendation first.

Ask only when you cannot continue alone:

  • You cannot prove the change.
  • The merge gate cannot pass, and you cannot fix that yourself.
  • The next step is outside the grant above: deleting data, touching production, changing things outside the repo, closing a PR, or force-pushing any other branch.
  • The goal is ambiguous in a way that changes the result.
  • Another PR makes this one obsolete.

Decide everything else yourself with sensible defaults, and list those decisions in the report.

If no question tool is available, or the tool returns no answer, take the safe path: leave the PR open and unmerged, and start your final message with the question.

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 Full Send AI skill do?

Trigger this if the user says 'full send'.

Why use Full Send on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/t3dotgg/tstack/tree/main/skills/full-send. 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 Full Send?

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 Full Send?

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

Is the Full Send AI skill free?

Yes. It is published on GitHub by t3dotgg 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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