Pane Work logo

Pane Work

Organization
greenfield-inc
pane-work

Answer questions about the user's own work in Pane, read-only. Recap what they worked on, finished, or shipped over a time window, or rank what to do next across panes, pull requests, issues, checks, and reviews. Use for "what have I been working on?", "what did I finish yesterday?", "what should I work on next?", "which PRs are closest to shipping?", or "what should I ignore for now?".

Overview

Publishergreenfield-inc
RepositoryPane
Skill namepane-work
Stars
529
Forks
227
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 greenfield-inc on GitHub. Read the source before you install it.

Installation

Install the Pane 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/greenfield-inc/Pane/tree/main/main/src/services/paneChatBundle/skills/pane-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/greenfield-inc/Pane.git /tmp/Pane
mkdir -p .claude/skills
cp -r /tmp/Pane/main/src/services/paneChatBundle/skills/pane-work .claude/skills/pane-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pane 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 Pane 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 Pane 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.

Pane work questions

Two kinds of question, one set of evidence:

  • Recap rebuilds memory: what happened, what shipped, what is still active, and what evidence exists.
  • Next judges the queue: what to do first, what is blocked, what is close to shipping, and what to leave for now.

Answer in this conversation. This skill is read-only: repositories, panes, branches, PRs, and deployments stay as they are unless the user explicitly asks for a change.

Scope

  • Time window. An exact duration ("last 24 hours") is a rolling window from now. Calendar words ("yesterday", "this week") use the user's local calendar; otherwise state the timezone. An open-ended "recently" covers active panes plus panes archived or updated in the last 7 days. SQLite CURRENT_TIMESTAMP values are UTC; say whether times you report are local or UTC.
  • Next, with no window: current state plus recent activity. By default, cover active panes, recently updated or archived panes, the 10 most recently active repositories, and GitHub queues the authenticated user can see. Respect any scope the user gives.

Collect the evidence

Prefer RunPane JSON. Reading SQLite or logs directly is a fallback; say so and note its caveats.

1. Pane state

bash
runpane doctor --json
runpane panes list --json
runpane repos list --json
runpane agent-context --json

Find the Pane data directory from runpane doctor --json, PANE_DIR, or the user. If a RunPane build offers a recap command, use it:

bash
runpane panes recap --since <window> --include-active --include-archived --json

When archived history is missing, read sessions.db in the data directory if the schema is there:

bash
sqlite3 "$PANE_DIR/sessions.db" ".tables"
sqlite3 "$PANE_DIR/sessions.db" ".schema sessions"

Useful tables: sessions (name, status, archived flag, timestamps, worktree path, project ID), projects (repository names and paths), tool_panels (terminal and agent panels), and session_outputs (mostly archive logs). With no archived_at column, use archived = 1 plus updated_at, and say so.

2. Branches and PRs

For each pane or worktree, run git where the worktree still exists; otherwise infer candidate branches from the saved repository path and the worktree slug.

bash
git branch --all --format='%(refname:short)|%(committerdate:iso8601)|%(subject)'
gh pr list --repo <owner>/<repo> --head <branch> --state all \
  --json number,title,state,url,headRefName,baseRefName,createdAt,updatedAt,mergedAt,closedAt,author

For "next", also check the queues, review requests first:

bash
gh search prs --review-requested=@me --state=open --archived=false \
  --json repository,number,title,url,author,updatedAt,isDraft --limit 100
gh search prs --author=@me --state=open --archived=false --sort updated --order desc \
  --json repository,number,title,url,author,updatedAt,isDraft --limit 100
gh search issues --assignee=@me --state=open --archived=false --sort updated --order desc \
  --json repository,number,title,url,updatedAt,labels,author --limit 100
gh pr view <number> --repo <owner>/<repo> \
  --json number,title,url,isDraft,author,updatedAt,reviewDecision,mergeStateStatus,statusCheckRollup,latestReviews,reviews,reviewRequests,comments,labels
  • statusCheckRollup separates passing, failing, pending, and skipped checks.
  • Search results omit reviewDecision; add it with gh pr view before ranking by review state.
  • comments is top-level discussion only. Fetch inline review comments with gh api repos/<owner>/<repo>/pulls/<number>/comments --paginate when the ranking depends on them.

3. Agent logs

Use transcript paths from RunPane when it gives them. Otherwise:

  • Codex: ${CODEX_HOME:-$HOME/.codex}/sessions, matched on session_meta.payload.cwd, the worktree path, branch slug, pane name, or PR URL.
  • Claude: ${CLAUDE_CONFIG_DIR:-$HOME/.claude}/projects, matched on the worktree path slug, pane name, branch slug, or PR URL, including subagents/*.meta.json.

Search first with rg -l, then read only matching files. Pull out final summaries, PR URLs, checks, deploy notes, and review findings; quote raw transcripts only when asked.

Judge

Recap: group into shipped or merged (merges, deploys, releases), finished but not merged, still active or open, what agents did and what review caught, and caveats. A pane can hold several workstreams; summarize the workstreams. Local changes and open PRs are unshipped work.

Next: rank with this order as a starting point, then explain your judgment:

  1. Review requests, incidents, security issues, billing or migration blockers, failed releases, broken deploys.
  2. Open PRs close to shipping: passing checks, recent, a small follow-up left.
  3. Draft PRs waiting on one human or product decision.
  4. Active pane workstreams with recent activity and a clear next action.
  5. Assigned high-priority issues (bug, security, reliability, data loss, billing, customer-facing).
  6. Large backlog, stale PRs, old drafts, experiments.

Finish work near merge before starting new backlog, unless the new item is a real incident or blocker. A pane name or an old branch alone doesn't make work live; confirm with branches, PRs, logs, and recent updates.

Name the next skill for each item:

  • Unknown failure or regression: investigate, or bug-intake to write it up.
  • Fuzzy idea or open decision: discussion, then options or brief, then create-ticket.
  • Clear implementation work: an implementation session with tdd, quick-verify, and prepare-pr.
  • Finished branch that needs confidence: review, then verify-app or pr-test-automation.
  • Open PR waiting on checks or bots: babysit-pr.
  • Large finished diff: refactor.

Answer

Lead with the answer:

text
I would do <item> first, then <item>. The reason is <signal>.

or, for a recap:

text
You shipped three things and had one open workstream in that window.

Then:

  • Next: 3 to 7 ranked items, each with its link, why it ranks there, one concrete next action, the next skill, and any blocker. Add a short "probably not next" list when the queue is noisy.
  • Recap: one entry per workstream (pane -> repo/branch -> PRs), with what changed, checks or deploys, and what is still open. Write it as a narrative; a table helps only for a dense PR list.

End with the evidence you used and its gaps (missing GitHub auth, removed worktrees, unreadable logs). Call something shipped only with evidence: a merge, release, deploy, passing checks, or a clear agent or PR record.

Frequently asked questions

What does the Pane Work AI skill do?

Answer questions about the user's own work in Pane, read-only. Recap what they worked on, finished, or shipped over a time window, or rank what to do next across panes, pull requests, issues, checks, and reviews. Use for "what have I been working on?", "what did I finish yesterday?", "what should I work on next?", "which PRs are closest to shipping?", or "what should I ignore for now?".

Why use Pane Work on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/greenfield-inc/Pane/tree/main/main/src/services/paneChatBundle/skills/pane-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pane 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 Pane Work?

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

Is the Pane Work AI skill free?

It is published on GitHub by greenfield-inc. 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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