
Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
This skill makes an agent write in Minto’s Pyramid Principle: the answer comes first, then grouped reasons, then supporting evidence. It also asks before rewriting, and it declines drafts where a pyramid structure would make the result worse.
Builders who want their agent to rewrite drafts into clearer, decision-first business writing.
You can turn buried-lede drafts into concise, executive-style writing without re-explaining the structure every time.
Moves the recommendation or conclusion to the top instead of leaving it buried in the middle.
Forces the middle layer to be organized as parallel reasons that answer the same question.
Keeps supporting details nested under the reason they support instead of mixing them into the lead.
On shared drafts, it points out the issue and asks whether to restructure instead of changing the text immediately.
Declines timelines, runbooks, tutorials, and similar documents where chronology is the point.
Defines restructure, buried-lede test, reason audit, so-what pass, and email version as explicit skill actions.
git clone https://github.com/millwright-labs/minto-pyramid-skill ~/.claude/skills/minto-pyramid
git clone https://github.com/millwright-labs/minto-pyramid-skill "$env:USERPROFILE\.claude\skills\minto-pyramid"
An Agent Skill that makes your coding agent write the way consultants are taught to write: answer first, then the reasons, then the evidence.
Works in Claude Code, Claude Desktop, and anything else that reads Agent Skills. One file, no dependencies.
git clone https://github.com/millwright-labs/minto-pyramid-skill ~/.claude/skills/minto-pyramid
Windows (PowerShell):
git clone https://github.com/millwright-labs/minto-pyramid-skill "$env:USERPROFILE\.claude\skills\minto-pyramid"
Or hand the repo URL to your agent and ask it to install the skill. Or download SKILL.md and drop it in ~/.claude/skills/minto-pyramid/SKILL.md. Restart your session and it's live.
It asks before it rewrites you. That's the design. When it loads on a draft you shared, it doesn't hand back a restructured version — it says one line about what it noticed and waits:
"This looks like a pyramid case — your recommendation lands in paragraph four. Want me to restructure it, conclusion first?"
Say yes and it goes. Say nothing and your draft stays yours. You know who the reader is, what's political, and what was already settled on a call; the agent doesn't. The exceptions are when you asked for it outright, or asked the agent to write the document — then it just does the work:
Use the minto-pyramid skill on this draft.
And when the document isn't a decision document at all — a timeline, a runbook, a thank-you note — it says so in a line and leaves it alone.
Five operations are defined inside: restructure, buried-lede test, reason audit, so-what pass, email version. Ask for one by name, or hand over a draft and let it start with the restructure.
The default order for a model is narrative: background, then findings, then the point — because that's the order most prose is written in. It buries the one line your reader needed. This skill inverts the order, enforces Minto's grouping rules on the middle layer, and bars the model from inventing facts while it tightens.
It is deliberately narrow. It declines incident timelines, runbooks, tutorials, and anything where chronology is the content, because forcing a pyramid onto those makes them worse.
The restraint behaviour — offering instead of rewriting, declining documents it shouldn't touch — is verified on Opus, Sonnet, GPT-5 and Gemini. It does not hold on Haiku, which restructured an incident timeline into a postmortem template, invented figures while "helping", and on one run matched a different installed skill altogether. Full detail in evals/RESULTS.md. Use it on a frontier model, or check its output when you don't.
The idea came from an Instagram post by @thewizeai (16 Aug 2026). The five prompts in that post are the seed of the five operations here. The framing around them had some slippage, so everything was checked before it was written down, and the skill was then reviewed by three other models and tested against a control.
Checks that held
Corrections made
Tested
The same messy 400-word recommendation email was rewritten twice: once by an agent with this skill, once by an identical agent without it. Both led with the recommendation — a capable model already does that much. With the skill, the three-week investigation narrative was cut as process rather than evidence, the reasons were grouped as reasons instead of listed as vendors, and the risks survived in their own section. The skilled run also sharpened one of the source's facts into something stronger than the original said, while the control left it alone — which is why "invent nothing" is now written into every operation, and why the worked example was rebuilt so both versions carry identical facts.
Expect the gain to be largest on long documents and weaker models, and smallest on a short email in a frontier model that was already going to lead with the ask.
MIT — Millwright Labs.
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Turn any codebase, with its docs, SQL schemas, configs, and PDFs, into a queryable knowledge graph. A /graphify skill for Claude Code, Cursor, Codex, and Gemini CLI: local deterministic AST parsing, every edge explained, no vector store.
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