Causal Hypothesis Architect Agent Skill

Build falsifiable causal hypotheses: DAGs, FPCI, equivalence testing.

Context cost
5.8k tokensestimated from the bundle, loaded when it triggers
Bundle
2 files · 23.2 kBtext throughout, nothing executable
Licence
Source-availablethe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Build falsifiable causal hypotheses: DAGs, FPCI, equivalence testing.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

data sciencetesting

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md16.4 kB · preview
--- name: hypothesis-building argument-hint: "[describe your theory or research question]" ---
7# Causal Hypothesis Architect
8
9## Instructions
10
11### 1. The Identification Challenge
12- **Verify FPCI Resolution:** Confirm that random assignment (or the identification strategy) solves the Fundamental Problem of Causal Inference for this design (Druckman 2022).
13- **Four Prerequisites for Experiments:** Before proceeding, verify that the design meets the four prerequisites for causal inference from experiments: (1) *random assignment* to conditions, (2) *exclusion restriction* (the only difference between conditions is the treatment itself), (3) *SUTVA* (Stable Unit Treatment Value Assumption -- one subject's treatment does not affect another's outcome), and (4) *noninterference* between units (Druckman 2022).
14- **Define the Data Generating Process (DGP):** Before drafting the hypothesis, describe the set of rules that governs how the data is created. What are the underlying mechanics of the world being studied?
15- **Map the Causal Diagram:** Where appropriate, draw a DAG. Identify backdoor paths and confirm whether randomization closes them (Mutz 2011).
16- **Close the Backdoors:** State which variables must be controlled for to isolate the treatment effect. If using an experiment, explain how random assignment closes these paths (Mutz 2011).
17- **SATE vs. PATE:** Distinguish between the Sample Average Treatment Effect (SATE) and the Population Average Treatment Effect (PATE). A convenience-sample experiment estimates a SATE; a population-based experiment on a representative sample estimates the PATE directly by design, without requiring statistical modeling or extrapolation (Mutz 2011; Druckman 2022).
18
19### 2. Hypothesis Formulation
20- **Popperian Falsifiability:** Frame the hypothesis as a "basic statement", or a specific observation that, if found to be false, would invalidate the theory.
21- **The Counterfactual Logic:** Every hypothesis must specify a comparison. Define the "untreated" world. If the hypothesis is that X causes Y, what is the specific state of the world where X is absent? Note that in many survey experiments there may be no "pure control" -- each condition provides information, just different information (Druckman 2022). Distinguish between *active* control groups (which receive different information on the same topic, controlling for the act of receiving information) and *passive* control groups (no information), as this choice defines the counterfactual and thus the estimand (Stantcheva 2023).
22- **Directional Clarity:** Avoid "existence" claims (e.g., "there is an effect"). Use "ordinal" claims that specify the direction (higher/lower) and, where possible, the expected functional form.
23- **Beat Credible Competitors:** The goal of a hypothesis test is not merely to reject the null of "no effect" but to beat credible alternative explanations. Design experiments that adjudicate between competing theories -- "the point of the experiment is explication, not demonstration" (Sniderman 2018). A hypothesis is stronger when it specifies which competing theoretical account would be undermined by the predicted result.
24- **Null-by-Design Thinking:** If the theory predicts no effect below a threshold of treatment intensity, specify (a) the intensity threshold, (b) expert-panel review of treatment strength before fielding, and (c) the equivalence bounds for the statistical test. Route such designs to the equivalence test in §3 (Sniderman 2018).
25- **Estimand Specification:** Every hypothesis must map to a specific estimand -- the statistical quantity that, if estimated, would test the hypothesis. State the *theoretical estimand* (the target quantity, defined outside any statistical model) before choosing the *empirical estimand* (a function of observable data) and the estimation strategy; each step requires different assumptions and should be argued separately (Lundberg, Johnson, & Stewart 2021). For experimental designs, this typically means specifying: (a) the treatment contrast (what is compared to what), (b) the outcome metric (probability, scale score, etc.), and (c) the model that produces the estimate (e.g., AMCE from a conjoint, ATE from a vignette experiment). A hypothesis without a named estimand is not pre-registrable. Where feasible, declare the design formally using the MIDA framework -- model, inquiry, data strategy, answer strategy -- so that power, bias, and estimator--estimand coherence can be diagnosed computationally before fielding (Blair, Cooper, Coppock, & Humphreys 2019). For information/pedagogical experiments, distinguish between *first-stage* estimands (the belief or knowledge the treatment shifts) and *second-stage* estimands (the policy views influenced by those beliefs), with the causal chain explicit (Stantcheva 2023).
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md2,241 words
Files2
LicenceSource-available
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈40
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
5,760
on trigger
The instruction body and 1 supporting file, read only when the skill fires.
2.9%
of a 200k window
Ten skills this size would take about 29% of the window before you open a file.
050k100k150k200k context window

5.8k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

2 files, 23.2 kB on disk. A bundle is text throughout: the instructions the model reads, plus the templates it fills in.

  • SKILL.md16.4 kB
  • reference/example.md6.8 kB
What is not in it

No dependencies and nothing executable: a skill is text the agent reads, so the bundle is 2 files you can review in full before installing. The Source-available licence covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Causal Hypothesis Architect · 5.8k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/causal-hypothesis-architect/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/causal-hypothesis-architect" ] && { [ ! -e .claude/skills/causal-hypothesis-architect ] || { mv .claude/skills/causal-hypothesis-architect "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/causal-hypothesis-architect" .claude/skills/ && echo "Installed .claude/skills/causal-hypothesis-architect"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add brycewang-stanford/causal-hypothesis-architect

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/causal-hypothesis-architect/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/causal-hypothesis-architect/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/causal-hypothesis-architect" ] && { [ ! -e ~/.claude/skills/causal-hypothesis-architect ] || { mv ~/.claude/skills/causal-hypothesis-architect "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/causal-hypothesis-architect" ~/.claude/skills/ && echo "Installed ~/.claude/skills/causal-hypothesis-architect" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/causal-hypothesis-architect/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as hypothesis-building in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, causal-hypothesis-architect.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencebrycewang-stanford/causal-hypothesis-architect

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

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