finish-work

name	finish-work
description	Wrap up an active Trellis task: archive it (and any other completed-but-unarchived tasks the user wants to clean up) and record a session journal. Refuses to run if the working tree has uncommitted code changes (those belong in workflow Phase 3.4 first). Use when the user asks to finish / wrap up / call it a day, or invokes $finish-work.

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 skill.

Step 1: Survey current state

python3 ./.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:

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."

  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)

python3 ./.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

python3 ./.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.

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Relationship to Other Skills

Development Flow:
  Phase 3.4 (workflow.md) -> AI drafts batched commits -> user confirms -> git commit
                                                                              |
                                                                              v
                                                                    $finish-work
                                                                    (survey + archive + journal)

Debug Flow:
  Hit bug -> Fix -> $break-loop -> Knowledge capture

• $finish-work — this skill, survey + archive + record session
• $break-loop — deep analysis after debugging