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Catchup

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oliver-kriska
catchup

Summarize and review what changed while you were away. Use after a weekend, vacation, or flight to check missed PRs, git commits, Linear tickets, and meetings — one prioritized brief, not a firehose.

Overview

Publisheroliver-kriska
Repositoryclaude-elixir-phoenix
Skill namecatchup
Stars
555
Forks
40
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by oliver-kriska on GitHub. Read the source before you install it.

Installation

Install the Catchup 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/oliver-kriska/claude-elixir-phoenix.git /tmp/claude-elixir-phoenix
mkdir -p .claude/skills
cp -r /tmp/claude-elixir-phoenix/plugins/catchup/skills/catchup .claude/skills/catchup
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Catchup 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 Catchup 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 Catchup 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.

Catchup — Async-Team Return Briefing

You've been away. This skill is a thin orchestrator: it resolves the window + sources here, then delegates the I/O fan-out, impact analysis, and brief assembly to the catchup-runner agent (Sonnet) so your (often Opus) session does not pay for summarization. You only print what the agent returns.

Usage

/catchup                                  # since you were last active
/catchup --since "friday"
/catchup --since "2h" --focus reviews-requested
/catchup --since last-commit --depth deep
/catchup --scope all                      # include cross-repo pings/reviews

Default is repo-scoped: every GitHub signal (reviews requested, notifications, mentions) is filtered to the repo you ran it in. Pass --scope all to also include cross-repo activity, which is then listed in its own separate section — never mixed into this repo's lists.

Iron Laws

  1. Delegate the heavy work — spawn catchup-runner (sonnet) for fan-out + assembly. Do NOT run the gh/git fan-out in this session; that defeats the cost/speed purpose.
  2. Resolve the window here, once — the agent must never re-resolve it. Pass absolute SINCE_* values.
  3. MCP runs here, not in the agent — Linear/Calendar MCP tools are unreliable in subagents. If present, fetch in this context and pass the text to the agent; else mark absent.
  4. Validate --since before any shell — match the grammar; on no match fall back to 24h and note the assumption.
  5. Stop after the brief — print the agent's summary, never auto-transition to another command.
  6. Repo-scoped by default — pass SCOPE=repo unless the user passed --scope all. A brief run inside one repo must not leak another repo's reviews/notifications. Cross-repo is opt-in only.

Workflow

1. Parse arguments

From $ARGUMENTS: --since, --scope, --sources, --depth, --focus. Defaults: --since last-active, --scope repo, all detected sources, --depth standard, no focus. --scope accepts repo (default — every GitHub signal filtered to the current repo) or all (cross-repo allowed, listed in its own section).

2. Resolve the time window (here, in this context)

Read ${CLAUDE_SKILL_DIR}/references/time-window.md. Resolve calendar words (friday, yesterday, a date) in the user's local timezone (this machine = the user's TZ), pivot through SINCE_EPOCH, derive SINCE_ISO (UTC) + SINCE_LABEL (with TZ abbrev) + LOCAL_TZ.

Default last-active = MAX of: newest Claude session mtime for this repo, your last own commit (git log --author=<you> -1 --format=%ct), your last own PR in this repo (gh pr list --repo <repo> --author @me --state all, repo-scoped — a global search would anchor to other repos). The latest footprint is "you were last here". Record which signal won. Variants: last-session (sessions only), last-commit/last-mine (your git/PR only). No signal → 24h, noted.

3. Detect sources + pull MCP data (here)

gh:   command -v gh && gh auth status               → github ON
git:  git rev-parse --is-inside-work-tree           → git ON
linear/calendar: a Linear / Google-Calendar MCP tool present?

If Linear/Calendar MCP is present, query it in this context now (assigned/updated tickets since SINCE_ISO; missed + today's meetings in LOCAL_TZ) and keep the short text as LINEAR_DATA / CALENDAR_DATA. If absent, set them to absent (the agent will proxy-harvest XXX-#### refs for Linear; skip calendar with a note).

4. Delegate to catchup-runner (Sonnet)

Spawn one agent, foreground, passing a self-contained prompt:

Agent(subagent_type: "catchup-runner", prompt: """
SINCE_EPOCH={…}  SINCE_ISO={…Z}  SINCE_LABEL="{… local TZ}"
LOCAL_TZ={…}  SOURCES={github,git}  SCOPE={repo|all}  DEPTH={…}  FOCUS={…}
OUT_PATH={cwd}/.claude/catchup/brief-{YYYY-MM-DD}.md   # local date (date +%F), not UTC
LINEAR_DATA={text or "absent"}
CALENDAR_DATA={text or "absent"}
Window anchor signal: {which one won, for the Risks note}
Do the gh+git fan-out, impact analysis, and brief assembly per your
instructions. Write the file. Return ONLY the inline summary.
""")

The agent inlines all recipes (it cannot read this plugin's references). Do not re-implement its work here.

5. Present + stop

Print the agent's returned summary verbatim and the brief path.

If the agent returned no summary (e.g. it hit its turn budget mid-assembly — the brief file is usually already written): do NOT re-summarize the brief yourself; that pulls the expensive step back into this (often Opus) session, defeating the delegation. Instead SendMessage the agent by the agentId from its stop usage: "Return only the inline summary now." — it finishes cheaply in Sonnet. Only if that also fails, read the brief's Intent + Top priorities section (not the whole file) and print that.

Do NOT auto-invoke any other command. The user decides what's first.

Sources at MVP

GitHub (gh), Git (git), Linear MCP (optional), Google Calendar MCP (optional). Slack/Gmail are v2 opt-in — never queried, never piped raw. Scheduling + per-project config are designed in ${CLAUDE_SKILL_DIR}/references/config-schema.md, not built at MVP.

Graceful degradation contract

A missing source degrades the brief, never breaks it. git log alone (always available in a repo) is a valid minimum brief. Every absent or failed source becomes one honest line under the brief's Risks/assumptions block, so the reader knows what it does not cover.

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 Catchup AI skill do?

Summarize and review what changed while you were away. Use after a weekend, vacation, or flight to check missed PRs, git commits, Linear tickets, and meetings — one prioritized brief, not a firehose.

Why use Catchup on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/oliver-kriska/claude-elixir-phoenix/tree/main/plugins/catchup/skills/catchup. 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 Catchup?

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 Catchup?

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

Is the Catchup AI skill free?

Yes. It is published on GitHub by oliver-kriska 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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