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Framing Doc

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rjs
framing-doc

Create a framing document from conversation transcripts. Use when the user has transcripts (VTT, call notes, etc.) and wants to produce a frame that captures the problem worth solving and why it was chosen over alternatives.

Overview

Publisherrjs
Repositoryshaping-skills
Skill nameframing-doc
Stars
1.4K
Forks
90
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 rjs on GitHub. Read the source before you install it.

Installation

Install the Framing Doc 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/rjs/shaping-skills.git /tmp/shaping-skills
mkdir -p .claude/skills
cp -r /tmp/shaping-skills/framing-doc .claude/skills/framing-doc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Framing Doc 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 Framing Doc 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 Framing Doc 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.

Framing from Conversation Transcripts

Produce a frame document from one or more conversation transcripts. The frame captures the "why" — what problem to solve and why this one, not the others.


Before You Start

Ask the user:

  1. Which transcripts? Get specific file paths. Read them in the order the user specifies — conversation order often matters because ideas build across calls.
  2. What's the topic area? A rough sense of what these conversations were about (e.g., "AI features," "onboarding redesign") so you know what to listen for.

What a Frame Document Contains

A frame has three sections:

1. Source — What was actually said

Capture verbatim quotes from the transcripts. This is ground truth. Everything else in the document is interpretation of this material.

  • Attribute quotes to speakers
  • Include enough context that the quote makes sense standalone
  • Add brief connective tissue between quotes where needed, but keep your voice minimal

2. Pre-work — What options surfaced and why this one

Survey the landscape of options that came up across conversations. This is not background — it's the argument for why we're framing this particular problem.

For each option that surfaced:

  • What is it?
  • Who benefits?
  • How strong is the signal? (How many people raised it independently? Did others pick it up or let it drop?)

Then make the case for the one to pursue now:

  • Why is it more urgent or important than the others?
  • Why not the others right now? (This is "why not now," not "why not ever" — don't dismiss ideas, just explain why they aren't the priority.)

Do not invent a roadmap or sequence for the other options. Whether and when they happen is a future decision. The only claim you're making is: this one first, for these reasons.

3. Problem / Outcome — The specific frame

Problem: What's broken, what pain exists. Render as bullets for easy scanning.

Outcome: What success looks like. Also bullets. Stay high-level — this is not solution-specific.

4. Less about / More about — Boundary on the solution space (optional)

A guardrail that orients the reader toward what kind of solution fits and away from what kind doesn't. It sits after Problem/Outcome as a separate section. Same format — two symmetric bullet lists.

What it does: Prevents someone from reading the Problem and Outcome correctly but proposing the wrong kind of solution. For example, a problem about getting programs into the system could reasonably lead someone to propose "AI that advises coaches on what to program" — which technically addresses the problem but completely misses the point.

When to include it: When there's a common misunderstanding or an obvious-but-wrong direction that people could easily head toward. The signal is that people in the conversations are actively drawing a line — saying what this is NOT. If nobody felt the need to draw that line, you probably don't need the section.

How it surfaces: This probably won't emerge mechanically from a first pass through the transcripts. It's more likely to surface during review — either the shaper notices a pattern of people saying "not about X," or someone reading the frame proposes something that fits the Problem/Outcome but misses the point. That's the moment where you realize the boundary needs to be made explicit.

What it looks like:

markdown
## Less about

- [What this project is NOT trying to solve]
- [The wrong direction people might naturally head toward]

## More about

- [What kind of solution actually fits]
- [The real nature of the problem being addressed]

No quotes or attribution needed — it's a synthesis. Keep it to the key points.


The Key Discipline: Evidence, Not Editorializing

After writing each line in Problem and Outcome, ask: who said this, and where?

  • If you can point to a specific person and moment → keep it, cite it
  • If it's directly implied by what someone said → keep it, mark it as implied with your reasoning
  • If you can't trace it back → drop it

Common traps to avoid

Don't connect dots and present inferences as facts. "Coaches bounce during trials because data entry is too hard" sounds plausible but if nobody said it, don't write it as established truth.

Don't embellish for vividness. Adding specificity that nobody stated ("every exercise, set, rep, and percentage") to make the problem sound more concrete is editorializing.

Don't inflate the options list. An idea that one person mentioned and nobody picked up is not an "option with traction." Be honest about signal strength. Ask yourself: did multiple people raise this independently? Did others build on it or let it die?

Don't editorialize in the Problem statement. Phrases like "TB has to earn the switch" or "the single biggest friction point" are rhetoric, not evidence. Make the case using what was said.

Don't present your framing as their words. Keep your interpretive voice separate from what was actually said. The Source section is theirs. Problem/Outcome are your distillation — and should be traceable back to Source.


Reviewing the Frame

After drafting, examine every line in Problem and Outcome:

  1. Read each bullet
  2. Ask: what's my evidence for this claim?
  3. If the evidence is a direct quote — cite it parenthetically
  4. If the evidence is implied — say so explicitly
  5. If there's no evidence — drop the line

Do the same for the pre-work options table. For each option listed, verify:

  • Did it actually get traction in the conversations? (Multiple people, independently?)
  • Or was it one person's idea that others didn't pick up?

Be willing to shrink the options list. Fewer real options are better than a padded list.


Document Format

markdown
---
shaping: true
---

# [Topic] — Frame

## Source

### [Speaker] ([Date])

> "Verbatim quote..."

> "Another quote..."

[Brief connective context where needed.]

### [Speaker] ([Date])

> "Verbatim quote..."

---

## Pre-work: [Topic] Options Landscape

[N] options surfaced with real traction across conversations:

| Option | What it does | Who benefits | Signal strength |
|--------|-------------|--------------|-----------------|
| **A. [Name]** | ... | ... | ... |
| **B. [Name]** | ... | ... | ... |

([Note on ideas that were mentioned but didn't get traction — dropped.])

**Why A now:** [Evidence-based argument for urgency/importance.]

---

## Problem

- [Bullet — traceable to source]
- [Bullet — traceable to source]

## Outcome

- [Bullet — high-level, not solution-specific]
- [Bullet — high-level, not solution-specific]

---

## Less about

- [What this is not]

## More about

- [What this is]

What This Skill Does NOT Do

  • Does not shape solutions. The frame is the "why." Shapes (the "how") come after, in a separate shaping doc.
  • Does not sequence future work. It picks one thing as the priority now. It does not roadmap the rest.
  • Does not summarize conversations. The Source section captures key quotes. The rest of the document is a distillation, not a summary.

Frequently asked questions

What does the Framing Doc AI skill do?

Create a framing document from conversation transcripts. Use when the user has transcripts (VTT, call notes, etc.) and wants to produce a frame that captures the problem worth solving and why it was chosen over alternatives.

Why use Framing Doc on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rjs/shaping-skills/tree/main/framing-doc. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Framing Doc?

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 Framing Doc?

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

Is the Framing Doc AI skill free?

It is published on GitHub by rjs. 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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