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Money Restore

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iamzifei
money-restore

Restore a prior business-state snapshot saved by /money-save, so you can continue from exactly where the last session left off. Loads the most recent snapshot for the current project (auto-detected from the working directory), or a specific one by index. Use when starting a new Claude Code conversation and the user wants to continue prior work. Triggered by: 'continue from last time', 'where did we leave off', 'pick up from before', 'resume', '接着上次', '续上', '之前的结论', '上次诊断到哪了'.

Overview

Publisheriamzifei
Repositoryshow-me-the-money
Skill namemoney-restore
Stars
1K
Forks
159
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 iamzifei on GitHub. Read the source before you install it.

Installation

Install the Money Restore 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/iamzifei/show-me-the-money/tree/master/skills/money-restore
  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/iamzifei/show-me-the-money.git /tmp/show-me-the-money
mkdir -p .claude/skills
cp -r /tmp/show-me-the-money/skills/money-restore .claude/skills/money-restore
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Money Restore 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 Money Restore 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 Money Restore 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.

/money-restore — Continue From Where You Left Off

Your job is to load the most recent business-state snapshot from ~/.smtm/sessions/{project}/, parse it, and present the state to the user so they can pick up where they left off.

You do not run diagnoses. You do not jump to other skills. You only restore memory.

After restoring, recommend the next skill — but let the user decide whether to invoke it.


Triggers

CommandBehavior
/money-restoreLoad the latest snapshot for the current project
/money-restore <N>Load the Nth snapshot (use indices from /money-save list)
/money-restore listSame as /money-save list — list all snapshots
/money-restore --slug <project>Switch to a different project's history

Natural-language equivalents (any of these → run /money-restore):

  • English: "continue from last time", "where did we leave off", "pick up from before", "resume the diagnosis", "what did we conclude last time"
  • 中文: "接着上次", "续上", "之前的结论", "上次诊断到哪了", "上次说到哪"

Project resolution

Same rules as /money-save:

  • Default = basename($(pwd)), sanitized
  • Override = --slug <name>
  • Fallback = default

Refer to it as business or project in conversation.


Workflow

Step 1 — Locate the snapshot file

In order:

  1. If user passed <N> → list snapshots in current project, sorted oldest → newest by filename timestamp, return the Nth.
  2. If user passed --slug X → use X as the project name.
  3. Otherwise → use the default project, find the newest file in ~/.smtm/sessions/{project}/ by filename timestamp.

Always sort by the YYYYMMDD-HHMMSS filename prefix, never by file mtime. mtime is unreliable when sessions are synced via iCloud, Dropbox, or git.

Step 2 — Handle missing data gracefully

Case A: project directory empty or missing

Check whether ~/.smtm/sessions/ exists at all and what other projects are in it.

If other projects exist with snapshots, list the 3 most recently active:

No saved state for project `{project}`. You've been active in these other projects recently:

1. musicapi             (latest 2026-04-22)
2. kolfind              (latest 2026-04-15)
3. adwhiz               (latest 2026-03-30)

Run `/money-restore --slug <name>` to load a specific project's state.

If ~/.smtm/sessions/ doesn't exist at all:

No saved state yet. Run /money-discover, /money-strategy, or any diagnostic skill, then `/money-save` to lock in the conclusion. Next time you can `/money-restore` to continue.

Case B: list mode

Hand off to /money-save list logic. Output format identical.

Step 3 — Read and parse

Open the snapshot file, parse YAML frontmatter, parse the 6 markdown sections (Business state, Confirmed conclusions, Ruled out, Open hypotheses, Next move, Notes).

If the file is malformed (frontmatter incomplete, sections missing — perhaps the user hand-edited it), present whatever fields are valid. Do not refuse to load just because the format is imperfect.

Step 4 — Present the state

Output a concise summary in the user's chosen language. Don't dump the full file — extract what matters and let the user ask for more.

Format:

markdown
## Picking up where you left off

**Project**: {project}
**When**: {timestamp formatted as `2026-05-03 14:23`}
**Title**: {title from frontmatter}
**Status**: {status — translate to user-friendly form: in-progress / resolved / abandoned → 进行中 / 已结论 / 已放弃 if Chinese}
**Last from**: {source_skill}

---

### Where you were

{Business state paragraph, verbatim from the snapshot}

### What you'd already decided
- {Each confirmed conclusion}

### What you'd ruled out
- {Each ruled-out direction}
- ({if empty, write "(nothing ruled out yet)")}

### Open hypotheses to test
- {Each open hypothesis}
- ({if empty, write "(none yet)")}

### Next move you'd planned
{Next move paragraph}

---

**Recommendation**: Continue with `{next_skill}` — that was the planned next step.

If you want to revise direction first, type a new `/money-*` command.

Step 5 — Wait for user direction

After presenting the state, stop. Do not auto-invoke next_skill.

The user's intent for this new conversation may have shifted. Let them confirm or redirect:

  • "Yes continue with <next_skill>" → invoke it
  • "Actually, I want to switch to <other-skill>" → invoke that instead
  • "Just refresh my memory, I'll decide later" → fine, do nothing further

Edge cases

  • Multiple snapshots in current project — default to the newest. If the user wants something older, suggest /money-save list to get indices, then /money-restore <N>.
  • Snapshot status was abandoned — surface this prominently. The user may have decided not to pursue this anymore; remind them.
  • Snapshot is more than 90 days old — flag with: "⚠️ This snapshot is from {N days ago}. Verify the conclusions still hold before acting on them."
  • Conflicting prior decisions across snapshots — only show the newest. Use /money-report if the user wants the full history merged.

Principles

  • Restore, don't replace — Present prior state, let the user decide what's still valid.
  • Time-aware — Old snapshots may be stale. Surface the date prominently.
  • No silent jumps — Never auto-invoke next_skill. The user just opened a new conversation; they may have a new agenda.
  • Match the user's language — If the snapshot was saved in Chinese but the user is now in English, present in English but quote original-language content verbatim where it preserves nuance.

Value Quantification (Required After Successful Restore)

After presenting the prior state and the next-step recommendation, append:

markdown
---

### 📊 What this restore is worth

- 📦 **Restored** — {N conclusions, M ruled-out directions, K open hypotheses from {date}}
- ⏱ **Saved this session** — ~15-30 minutes of re-explaining context to the AI
- ⚠️ **Risk avoided** — Re-suggesting a direction you already ruled out (the AI has no memory across sessions without `/money-save` and `/money-restore`)
- ⏭ **Suggested next** — {next_skill from frontmatter, or "decide based on the state above"}

If the snapshot is older than 90 days, prepend a warning row:

⚠️ Stale: This snapshot is {N days} old. Verify the conclusions still hold before acting on them.

If the snapshot's status is abandoned, prepend:

⚠️ Abandoned: This snapshot was marked abandoned. Restoring is fine for review — but the conclusions here were rejected, not validated.

Frequently asked questions

What does the Money Restore AI skill do?

Restore a prior business-state snapshot saved by /money-save, so you can continue from exactly where the last session left off. Loads the most recent snapshot for the current project (auto-detected from the working directory), or a specific one by index. Use when starting a new Claude Code conversation and the user wants to continue prior work. Triggered by: 'continue from last time', 'where did we leave off', 'pick up from before', 'resume', '接着上次', '续上', '之前的结论', '上次诊断到哪了'.

Why use Money Restore on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/iamzifei/show-me-the-money/tree/master/skills/money-restore. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Money Restore?

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 Money Restore?

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

Is the Money Restore AI skill free?

It is published on GitHub by iamzifei. 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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