Quarterly Letter Synthesizer logo

Quarterly Letter Synthesizer

Community
latour-ai
quarterly-letter-synthesizer

Synthesize a stack of manager quarterly letters into a single cross-manager memo for allocators, fund-of-funds analysts, OCIOs, and family offices. Trigger on phrases like "synthesize these letters," "what are my managers saying this quarter," "summarize the quarterly letters," "cross-manager themes," "compare these investor letters," "read these letters and tell me what stands out," or when the user uploads multiple manager letters (PDFs or text) and wants themes, divergences, positioning shifts, or contradictions surfaced. Do NOT use for a single manager's quarterly check-in. Do NOT use for a first look at a new manager — use the hedge-fund-diligence skill. Do NOT use for drafting your own LP letter — use the lp-update-generator skill.

Overview

Publisherlatour-ai
Repositoryskills
Skill namequarterly-letter-synthesizer
Stars
69
Forks
4
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 latour-ai on GitHub. Read the source before you install it.

Installation

Install the Quarterly Letter Synthesizer 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/latour-ai/skills/tree/main/quarterly-letter-synthesizer
  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/latour-ai/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/quarterly-letter-synthesizer .claude/skills/quarterly-letter-synthesizer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Quarterly Letter Synthesizer 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 Quarterly Letter Synthesizer 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 Quarterly Letter Synthesizer 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.

Quarterly Letter Synthesizer

Read a stack of manager quarterly letters and produce one cross-manager synthesis memo: what everyone agrees on, where they disagree, who changed their positioning, what's new, and who is contradicting their own prior letters. The reader is an allocator who has 20 letters in their inbox and one hour.

Why this skill exists

Every quarter, allocators receive a pile of letters and most get skimmed at best. The real signal isn't in any single letter — it's across them: three managers independently building the same trade, one manager quietly reversing what they argued six months ago, a crowded theme nobody flags as crowded. Reading for that signal manually takes a full day. This skill compresses it into a memo and preserves the receipts (quotes and page references) so every claim can be checked against the source.

Inputs

Required:

  • Two or more manager letters (PDFs, pasted text, or files in a folder). One letter is not a synthesis — if only one is provided, say so and offer a single-manager read instead.

Optional (each one materially improves the output):

  • Prior-quarter letters from the same managers — enables the "shift vs. prior letter" and "contradictions" sections
  • The user's portfolio context (which managers they're invested in, sizing)
  • Specific questions ("what is everyone saying about rates?")
  • Manager strategy labels, if not obvious from the letters

Missing-input behavior: If letters lack dates or manager names, ask once for a quick mapping. Otherwise proceed — label any assumption explicitly (e.g., "Assumed Letter 3 is Q1 2026 based on references to March data"). Never guess a manager's strategy silently; mark it "strategy inferred from letter content."

How it works

  1. Inventory the stack. List every letter: manager, fund, period covered, length, whether a prior letter is available. Flag any letter that is marketing material rather than a true quarterly letter.
  2. Extract per letter, before comparing. For each: macro/market views, key positioning statements, new positions or themes mentioned, exited or trimmed positions, performance commentary (only as stated — never compute or infer returns), notable tone (defensive, aggressive, apologetic), and anything unusual (personnel notes, terms changes, AUM comments).
  3. Build the theme map. Cluster views across managers: where do 2+ managers converge? Note whether convergence is independent reasoning or everyone citing the same data point.
  4. Find the divergences. Where do managers take opposite sides of the same question? These are the most valuable findings — quote both sides.
  5. Diff against prior letters (if provided). For each manager: what changed in positioning, tone, or argument? Flag explicit reversals and silent reversals (a previously emphasized theme that simply disappeared).
  6. Flag contradictions. A manager saying something inconsistent with their own prior letter, or with their stated strategy, gets quoted side by side with both dates.
  7. Write the memo using the output format below, with every claim attributed to a specific manager and letter. Every manager in the stack appears at least once in the memo.

Output format

Length follows the substance: cover what the task needs and leave out filler sections, restated summaries, and boilerplate.

# Quarterly Letter Synthesis — [Period]
Letters reviewed: [N] | Managers: [list] | Prior letters available: [Y/N per manager]

## 1. One-paragraph read
[The 3–4 sentences you'd say to the CIO in the hallway.]

## 2. Shared themes (convergence)
- **[Theme]** — held by [Manager A, B, C]. [1–2 sentences each on how they express it.]
  > "[Short supporting quote]" — [Manager], [period letter], p.[X]
- Crowding note: [is this theme consensus across the stack?]

## 3. Divergent views
- **[Question]**: [Manager A] argues [X]; [Manager B] argues the opposite.
  > A: "[quote]"  |  B: "[quote]"

## 4. Positioning shifts vs. prior letters
| Manager | Then ([prior period]) | Now ([current period]) | Shift |
|---|---|---|---|

## 5. Notable new / exited positions and themes
[Per manager, only as explicitly disclosed in the letter.]

## 6. Contradictions and silent reversals
- **[Manager]**: said "[quote]" ([prior date]) vs. "[quote]" ([current date]). [Why it matters; possible benign explanation.]

## 7. Tone and red-flag watch
[Defensiveness, blame-shifting, benchmark switching, AUM/personnel mentions.]

## 8. Questions for manager calls
[3–7 specific questions, each tied to a finding above.]

## Not covered in these letters
[Topics the user might expect that no manager addressed.]

Adjust length to the stack: 5 letters → ~2 pages; 20 letters → 4–5 pages with the table doing more work.

Guardrails

  • Quote, don't paraphrase, for anything contentious. Every contradiction, reversal, or red flag must carry a verbatim quote with manager name and letter date. If you can't find the quote, drop the claim.
  • Never invent or compute performance figures. Report returns only as stated in a letter, with the as-of date. Do not annualize, net, or compare returns the managers didn't compare themselves.
  • Separate fact from inference. "Manager A exited the position" (stated) vs. "Manager A appears to have reduced conviction" (inference — label it as such).
  • No investment advice. The memo informs manager conversations; it does not recommend allocations, redemptions, or trades.
  • Confidentiality. Manager letters are typically confidential LP communications. Remind the user not to circulate the synthesis outside the team that is entitled to see the underlying letters.
  • A missing theme is data, not license to speculate. If no manager discussed something, say so — don't fill the gap with your own market view.

Example prompt

"Here are Q1 letters from our six hedge fund managers, plus their Q4 letters. Synthesize: what themes are shared, where they disagree, who shifted positioning, and whether anyone is contradicting what they told us last quarter. We're especially interested in what they're saying about credit."

Frequently asked questions

What does the Quarterly Letter Synthesizer AI skill do?

Synthesize a stack of manager quarterly letters into a single cross-manager memo for allocators, fund-of-funds analysts, OCIOs, and family offices. Trigger on phrases like "synthesize these letters," "what are my managers saying this quarter," "summarize the quarterly letters," "cross-manager themes," "compare these investor letters," "read these letters and tell me what stands out," or when the user uploads multiple manager letters (PDFs or text) and wants themes, divergences, positioning shifts, or contradictions surfaced. Do NOT use for a single manager's quarterly check-in. Do NOT use f...

Why use Quarterly Letter Synthesizer on TypingMind?

Because you install it once and use it with any model. Quarterly Letter Synthesizer 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 Quarterly Letter Synthesizer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/latour-ai/skills/tree/main/quarterly-letter-synthesizer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Quarterly Letter Synthesizer?

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 Quarterly Letter Synthesizer?

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

Is the Quarterly Letter Synthesizer AI skill free?

Yes. It is published on GitHub by latour-ai 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.

View all

Set up your own AI workspace now

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