Wrap Up logo

Wrap Up

Organization
codewithmukesh
wrap-up

Owns the session handoff lifecycle: the end-of-session ritual that captures completed work, pending tasks, and learnings into .claude/handoff.md, and the session-start protocol that loads it back. Triggers on: /wrap-up, "wrap up", "done for today", "that's all", "end session", "signing off", "handoff" — and at session start: "start session", "session start", "load handoff", "pick up where we left off", "what were we working on".

Overview

Publishercodewithmukesh
Repositorydotnet-claude-kit
Skill namewrap-up
Stars
721
Forks
170
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Wrap Up 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/codewithmukesh/dotnet-claude-kit.git /tmp/dotnet-claude-kit
mkdir -p .claude/skills
cp -r /tmp/dotnet-claude-kit/skills/wrap-up .claude/skills/wrap-up
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wrap Up 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 Wrap Up 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 Wrap Up 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.

/wrap-up

What

The session continuity ritual. Sessions are ephemeral; knowledge is permanent. Wrap-up bridges sessions in both directions:

  • Session END — capture exactly three things: what was DONE, what is PENDING, and what was LEARNED. Write them to .claude/handoff.md and flow durable learnings into MEMORY.md.
  • Session START — load the handoff, memory, and instincts, then present a resume summary so no session starts blind.

When

  • End of a working session — "done for today", "that's all", "signing off"
  • Before switching projects or after a major milestone
  • Implicit endings — "thanks" after completed tasks, "good enough for now": offer the handoff, don't just say goodbye
  • Start of a session — "start session", "load handoff", "what were we working on"
  • For a mid-session save without ending, use /checkpoint instead

How

Session End

  1. Review the session — From git status/diff and the conversation: files touched, tasks completed vs unfinished, decisions made and why, user corrections observed.
  2. Check uncommitted changes — If any exist, offer to commit before wrapping.
  3. Write the handoff.claude/handoff.md, using the format below. Single file, always overwritten — only the current state matters. If the existing handoff has pending tasks from someone else, ask before overwriting: merge, overwrite, or skip.
  4. Extract learnings — Corrections and discoveries worth keeping go to MEMORY.md (via instinct-system); emerging patterns update .claude/instincts.md (via instinct-system). The handoff's Learned section is the trigger, not the destination — handoffs are ephemeral.
  5. Confirm — Summarize the handoff and learnings captured for the user.

Handoff File Format (.claude/handoff.md)

Write it for a stranger with zero context — file paths, rationale, specific next steps. "Continue the refactor" is useless; "Refactor src/Orders/CreateOrder.cs to the Result pattern — see Catalog for the established pattern" is actionable.

markdown
# Session Handoff

> Generated: 2026-06-12 | Branch: feature/order-validation

## Completed
- [x] Added FluentValidation to CreateOrder (src/Orders/Features/CreateOrder.cs:15-35)
- [x] Fixed N+1 in GetOrderDetails (src/Orders/Features/GetOrderDetails.cs:28)

## Pending
- [ ] Add validation to UpdateOrder (src/Orders/Features/UpdateOrder.cs)
  - Reference: CreateOrder validator for the established pattern
- [ ] Full test run — 2 pre-existing Catalog failures, not from today

## Learned
- FluentValidation validators must be registered in the module's DI setup
- Test fixture seeds 1 item per order, which hides N+1 queries

## Context
- Branch: feature/order-validation | Last commit: "Add CreateOrder validation"
- Uncommitted changes: no | Solution: src/MyApp.slnx

When multiple developers share the project, add a ## Decisions Made table (Decision | Choice | Rationale) and ## Open Questions.

Learning Extraction Checklist

  1. Did the user correct anything? → MEMORY.md via instinct-system
  2. Did we discover something non-obvious? → Learned section
  3. Did we decide something with rationale? → document the "why"
  4. Did a tool/approach fail? → note it so the next session doesn't retry it

Good learning: "EF compiled queries don't work with Include()". Bad learning: "Worked on Orders module" (vague) or "Used TimeProvider" (already a rule).

Session Start

  1. Load context — Read .claude/handoff.md (pending work), MEMORY.md (permanent rules), .claude/instincts.md (patterns at 0.7+ confidence). Missing files are fine: note it and start clean — never block on them.
  2. Detect the solution — Find .slnx/.sln (current dir, then parents, then children) so Roslyn MCP tools work from the first prompt.
  3. Present a resume summary — Last session's work, pending tasks, active rules/instincts count, detected solution. Then ask what to work on — never auto-start pending work.

Example

User: /wrap-up

Claude: Wrapping up session...

## Session Summary
- Implemented OrderService with validation pipeline
- Fixed N+1 query in GetOrdersByCustomer

## Pending
- [ ] Add retry logic to payment gateway calls (OrderService.cs:42)

## Learnings Captured
- User prefers explicit mapping over AutoMapper → MEMORY.md

Written to .claude/handoff.md. See you next session.

Related

  • /checkpoint — Mid-session save (commit + brief note) without ending the session
  • instinct-system — Routes session learnings: patterns become instincts, user corrections become permanent MEMORY.md rules

Frequently asked questions

What does the Wrap Up AI skill do?

Owns the session handoff lifecycle: the end-of-session ritual that captures completed work, pending tasks, and learnings into .claude/handoff.md, and the session-start protocol that loads it back. Triggers on: /wrap-up, "wrap up", "done for today", "that's all", "end session", "signing off", "handoff" — and at session start: "start session", "session start", "load handoff", "pick up where we left off", "what were we working on".

Why use Wrap Up on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codewithmukesh/dotnet-claude-kit/tree/main/skills/wrap-up. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wrap Up?

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 Wrap Up?

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

Is the Wrap Up AI skill free?

Yes. It is published on GitHub by codewithmukesh 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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