Money Retro logo

Money Retro

CommunityPopular
iamzifei
money-retro

Weekly business retrospective. Reads all snapshots, learnings, skill-usage telemetry, and (if provided) revenue data from the past week. Outputs: what got decided, what shipped, what's stalled, skill activity, suggested next focus. Surfaces unused skills as activation prompts. Use at end of week or end of sprint. Triggered by: 'weekly retro', 'business retro', 'what did we ship', 'how's the week going', '周复盘', '业务回顾', '本周复盘'.

Overview

Publisheriamzifei
Repositoryshow-me-the-money
Skill namemoney-retro
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 Retro 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-retro
  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-retro .claude/skills/money-retro
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Money Retro 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 Retro 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 Retro 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-retro — Weekly Business Retrospective

Standard startup: before producing output, run the 5-step startup sequence per /money § Standard Skill Startup (resolve slug → telemetry write → /money-retro reads from telemetry rather than auto-loading learnings, since it's analyzing usage patterns themselves → surface project-local skills if any → load ALL atom categories so retro recommendations can cite the founder principles they reflect).

Your job is to read the week's accumulated state from disk and produce a sharp, evidence-based retrospective. This is not a pep talk. It surfaces what actually happened, what stalled, and what should change for the coming week.

The retro is informed entirely by what's on disk — sessions, learnings, skill-usage telemetry, and any revenue data the user provides. Do not improvise narrative beyond what the evidence supports.


Triggers

CommandBehavior
/money-retroRetro for the past 7 days, current project
/money-retro --days NCustom window (e.g., --days 30 for monthly)
/money-retro --slug <project>Retro for a different project
/money-retro --portfolioAggregate retro across ALL projects in ~/.smtm/sessions/

Natural-language equivalents:

  • "Weekly retro", "Business retro", "What did we ship", "How's the week going"
  • "周复盘", "业务回顾", "本周怎么样"

What to load

For the time window (default 7 days, sliding from now):

  1. Snapshots at ~/.smtm/sessions/{slug}/ — filter by filename timestamp ≥ window start
  2. Learnings added at ~/.smtm/projects/{slug}/learnings.jsonl — filter by captured_at ≥ window start
  3. Skill-usage telemetry at ~/.smtm/analytics/skill-usage.jsonl — filter by ts ≥ window start, group by skill
  4. Optional: revenue data — if user provides numbers (e.g., "MRR went from $X to $Y"), incorporate. Otherwise omit revenue section.
  5. Prior retros at ~/.smtm/projects/{slug}/retros/ — for trend comparison

If the window is empty (no snapshots, no learnings, no skill usage), say so plainly:

No activity recorded in {project} for the past {N} days. Either you weren't running money-* skills with ~/.smtm/ enabled, or this project was dormant. Either is fine — but there's nothing to retro.


Workflow

Step 1 — Aggregate the week

For the time window, compute:

  • Snapshots in window: count, with each snapshot's title and status
  • Decisions made: pull from each snapshot's Confirmed conclusions section
  • Things ruled out: pull from each snapshot's Ruled out section
  • Open hypotheses: pull from each snapshot's Open hypotheses section
  • Stalled hypotheses: open hypotheses from snapshots ≥14 days old without a follow-up snapshot
  • Learnings added: count + categories
  • Skills used: histogram, sorted by frequency

Step 2 — Identify "stalled" items

A hypothesis is "stalled" if:

  • It was opened in a snapshot ≥14 days ago
  • No subsequent snapshot in the same project has either confirmed it or ruled it out
  • No learning has been added that addresses it

These are the highest-value findings — usually founders forget about hypotheses they meant to test.

Step 3 — Find unused skills

From skill-usage.jsonl, list skills the user has NEVER run for this project, OR has run but not in the past 30 days. These are activation candidates.

Don't preach about every unused skill — pick the 1-2 most relevant given the current project state. If they have a shipped product but never run /money-ads, that's a candidate. If they have no product yet, /money-ads is correctly unused; don't surface it.

Step 4 — Output

markdown
# Weekly Business Retro — {project}

**Window**: {start date} → {end date} ({N} days)
**Generated**: {now}

---

## What you decided this week

{For each snapshot's "Confirmed conclusions" — list with snapshot title and date.}

If no snapshots: "No new decisions checkpointed. Either nothing was decided, or `/money-save` wasn't run."

---

## What you ruled out

{Aggregated from snapshots' "Ruled out" sections, deduplicated.}

If empty: "Nothing was explicitly ruled out this week."

---

## Stalled hypotheses (action required)

These hypotheses were opened ≥14 days ago and never tested:

| Opened | Days stalled | Hypothesis | From |
|---|---|---|---|
| 2026-04-15 | 18 | Reddit r/SaaS will convert at >2% | snapshot:wedge-locked |
| ... | | | |

For each stalled item, suggest the cheapest test to resolve it.

If empty: "All open hypotheses are <14 days old. No stalls."

---

## Learnings captured

{Show recent learnings added in the window, with category and confidence.}

If empty: "No new learnings captured. If you observed any patterns this week, run `/money-learn add`."

---

## Skill activity

| Skill | Runs this week | Trend vs prior week |
|---|---|---|
| /money-discover | 3 | ↑ (was 1) |
| /money-content | 8 | → (was 7) |
| /money-ads | 0 | — (never run) |
| ... | | |

---

## Activation candidates (skills you haven't used)

Pick 1-2 high-relevance unused skills. Frame as a question:

> 💡 You've shipped the product and run `/money-content` 8 times — but never `/money-ads`. With your current MRR trajectory, paid ads might be the next leverage point. Want me to walk you through `/money-ads` next session?

---

## Revenue (only if user provided)

Show MRR/revenue if the user shared numbers. Otherwise omit.

---

## Recommended focus for next week

ONE thing. Not three. The single highest-leverage move based on what the retro surfaced.

Format: "**Focus**: {one sentence}. **First action**: {today or tomorrow specific step}."

---

## Snapshot of project state

- Total snapshots in project — {N}
- Total learnings — {N} ({validated} validated, {emerging} emerging, {hypothesis} hypothesis)
- Project age — {months since first snapshot}
- Open hypotheses — {N}

Step 5 — Save the retro

Write the retro itself to ~/.smtm/projects/{slug}/retros/{YYYYMMDD-HHMMSS}-retro.md. Each retro is a new file (never overwrite). This builds a longitudinal record of how the project is moving.

Confirm to the user:

✅ Retro saved to ~/.smtm/projects/{slug}/retros/{filename}.md Compare to last week's retro: ~/.smtm/projects/{slug}/retros/{prior filename}.md


Portfolio mode (--portfolio)

If invoked with --portfolio, aggregate across ALL projects in ~/.smtm/sessions/. Output is similar but adds a top-level "Project rollup" table:

ProjectSnapshots this weekNew learningsStalledStatus (most recent snapshot)
musicapi320in-progress
kolfind004⚠️ stalled
ccapi101in-progress

The "stalled" surface here is especially valuable — solo founders often have one product that's silently dying while they pour attention into the noisier one.


Principles

  • Evidence-only narrative — If it's not in a snapshot, learning, or telemetry log, it's not in the retro. No improvised storytelling.
  • Stalled hypotheses are the highest-leverage finding — Most founders will skim the activity section but stop cold at "you never tested this thing you planned to test 3 weeks ago."
  • One focus, not five — Recommendations dilute as they multiply. Pick the single highest-leverage move.
  • Trend over snapshot — Show comparison to prior weeks where possible. Trend reveals what point-in-time can hide.
  • Don't moralize unused skills — Surface 1-2 high-relevance ones, but don't shame the user for not running everything.

After the retro

Always recommend one of:

  • /money-learn add if the retro surfaced an obvious pattern that wasn't yet logged
  • /money-save after acting on the recommended focus
  • /money-panel if multiple stalled hypotheses suggest the plan itself needs re-review
  • /money-diagnose if stalled items are stalled because of an execution blocker, not lack of time

Value Quantification (Required at End of Output)

  • ⏱ Time saved — ~1-2 hours of digging through old chats, snapshots, and trying to remember what you decided
  • ⚠️ Risks avoided — (1) Stalled hypotheses going untested for months; (2) running the same activity (e.g., /money-content 12x) without checking ROI; (3) ignoring a quietly-dying side project while focusing on the noisy one; (4) repeating the same week-over-week without intentional course correction
  • ✅ What you got — Decisions / ruled-out / stalled / learnings / skill activity, plus ONE recommended focus and a portfolio rollup if multi-project
  • 🚧 Without this skill — You'd reach the end of the quarter unable to articulate what changed, and at least one hypothesis would have silently aged out without ever being tested

Frequently asked questions

What does the Money Retro AI skill do?

Weekly business retrospective. Reads all snapshots, learnings, skill-usage telemetry, and (if provided) revenue data from the past week. Outputs: what got decided, what shipped, what's stalled, skill activity, suggested next focus. Surfaces unused skills as activation prompts. Use at end of week or end of sprint. Triggered by: 'weekly retro', 'business retro', 'what did we ship', 'how's the week going', '周复盘', '业务回顾', '本周复盘'.

Why use Money Retro on TypingMind?

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

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

Which AI models can use Money Retro?

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 Retro?

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

Is the Money Retro 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇