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Trellis Finish Work

OrganizationPopular
fy-agent
trellis-finish-work

Wrap up the current session: verify quality gate passed, remind user to commit, archive completed tasks, and record session progress to the developer journal. Use when done coding and ready to end the session.

Overview

Publisherfy-agent
Repositoryfyagent
Skill nametrellis-finish-work
Stars
1.7K
Forks
139
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 fy-agent on GitHub. Read the source before you install it.

Installation

Install the Trellis Finish Work 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/fy-agent/fyagent/tree/main/.agents/skills/trellis-finish-work
  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/fy-agent/fyagent.git /tmp/fyagent
mkdir -p .claude/skills
cp -r /tmp/fyagent/.agents/skills/trellis-finish-work .claude/skills/trellis-finish-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trellis Finish Work 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 Trellis Finish Work 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 Trellis Finish Work 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.

Finish Work

Wrap up the current session: archive the active task (and any other completed-but-unarchived tasks the user wants to clean up) and record the session journal. Code commits are NOT done here — those happen in workflow Phase 3.4 before you invoke this command.

Step 1: Survey current state

bash
python ./.trellis/scripts/get_context.py --mode record

This prints:

  • My active tasks — review whether any besides the current one are actually done (code merged, AC met) and should be archived this round.
  • Git status — quick visual on what's dirty.
  • Recent commits — you'll need their hashes in Step 4 for --commit.

If --mode record surfaces other completed tasks not tied to the current session, surface them to the user with a one-shot confirmation: "These N tasks look done — archive them too in this round? [y/N]". Default is no; the current active task is always archived in Step 3 regardless.

Step 2: Sanity check — classify dirty paths

Run:

bash
git status --porcelain

Filter out paths under .trellis/workspace/ and .trellis/tasks/ — those are managed by add_session.py and task.py archive auto-commits and will appear dirty as part of this skill's own work.

For each remaining dirty path, decide whether it belongs to the current task or to other parallel work (e.g., another terminal window editing the same repo). Heuristics:

  • Paths referenced in the current task's prd.md / implement.jsonl / check.jsonl → current task
  • Paths in code areas matching the task's stated scope, or that you remember editing this session → current task
  • Paths in unrelated areas you have no recollection of touching this session → other parallel work

Then route:

  • Any remaining path looks like current-task work — bail out with:

    "Working tree has uncommitted code changes from this task: <list>. Return to workflow Phase 3.4 to commit them before running ``finish-work (Trellis command)."

    Do NOT run git commit here. Do NOT prompt the user to commit. The user goes back to Phase 3.4 and the AI drives the batched commit there.

  • All remaining paths look unrelated (other parallel-window work) — report them once and continue to Step 3:

    "FYI, dirty files outside this task's scope — leaving them for the other window: <list>."

  • Genuinely unsure — ask the user once: "Are <list> this task's work I forgot to commit, or another window's? (commit / ignore)" — then route per their answer.

Step 3: Archive task(s)

bash
python ./.trellis/scripts/task.py archive <task-name>

At minimum: the current active task (if any). Plus any extra tasks the user confirmed in Step 1. Each archive produces a chore(task): archive ... commit via the script's auto-commit.

If there is no active task and the user did not confirm any cleanup archives, skip this step.

Step 4: Record session journal

bash
python ./.trellis/scripts/add_session.py \
  --title "Session Title" \
  --commit "hash1,hash2" \
  --summary "Brief summary"

Use the work-commit hashes produced in Phase 3.4 (visible in Step 1's Recent commits list, or via git log --oneline) for --commit. Do not include the archive commit hashes from Step 3. This produces a chore: record journal commit.

Final git log order: <work commits from 3.4> → chore(task): archive ... (one or more) → chore: record journal.

Frequently asked questions

What does the Trellis Finish Work AI skill do?

Wrap up the current session: verify quality gate passed, remind user to commit, archive completed tasks, and record session progress to the developer journal. Use when done coding and ready to end the session.

Why use Trellis Finish Work on TypingMind?

Because you install it once and use it with any model. Trellis Finish Work 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 Trellis Finish Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fy-agent/fyagent/tree/main/.agents/skills/trellis-finish-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Trellis Finish Work?

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 Trellis Finish Work?

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

Is the Trellis Finish Work AI skill free?

It is published on GitHub by fy-agent. 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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