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Agent Causal

Agent Causal Decision Tool helps you and your AI agents answer one question from experiment data: should we ship this change, keep running the test, or roll... Skill: Agent Causal Owner: zhumorris Summary: Agent Causal Decision Tool helps you and your AI agents answer one question from experiment data: should we ship this change, keep running the test, or roll... Tags: latest:0.10.3 Version history: v0.10.3 | 2026-05-07T11:46:56.976Z | user Skill version 0.10.3 aligned with v0.10.2 git tag — SKILL.md tarball URL now uses v0.10.2 (matching the published skill version). Also

OpenClaw
View on ClawHubHomepage

Rank

62

Safety

84

Downloads

1.2k

Updated

Oct 11, 2026

Version

0.10.3

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

  • Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor
Clawhubvendor · observed Oct 11, 2026
Protocol compatibility
OpenClawcompatibility · observed Oct 11, 2026
Adoption signal
1.2K downloadsadoption · observed Oct 11, 2026
Latest release
0.10.3release · observed May 7, 2026
Handshake status
UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17977p9bp8aqeg4qvcakzky5n85syh7:agent-causal
  1. Setup complexity is classified as HIGH. You must provision dedicated cloud infrastructure or an isolated VM. Do not run this directly on your local workstation.
  2. Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-zhumorris-agent-causal/snapshot"

Documentation

CLAWHUB

151,150 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: agent-causal
description: "Agent Causal Decision Tool helps you and your AI agents answer one question from experiment data: should we ship this change, keep running the test, or roll it back? Returns structured JSON decisions, key statistics, and audit trails from A/B tests (frequentist + Bayesian), DiD, cohort/segment analysis, and sequential early stopping."
metadata:
  openclaw:
    category: data-science
    version: "0.10.3"
    license: Apache-2.0
    tools: [exec]
    requires:
      bins: [python3, git, pip]
      python_packages: [click, scipy, numpy, pydantic]
    source: https://github.com/ZhuMorris/agent-causal-decision-tool
---

# Agent Causal Decision Tool

A causal decision and audit tool for AI agents. Evaluate product changes using A/B testing, Difference-in-Differences, and sequential early stopping.

**Source:** https://github.com/ZhuMorris/agent-causal-decision-tool

## What is this?

Agent Causal Decision Tool helps you and your AI agents answer one question from experiment data: "should we ship this change, keep running the test, or roll it back?" It takes in simple A/B or rollout summaries and returns a structured JSON decision, key statistics, and an audit record you can store or review later.

Rather than being a full experimentation platform, it is a **decision engine**. You bring the data (from your logs, BI tool, or CSV); it handles the stats, decision logic, and audit trail.

### Why it exists

In many teams, experiment decisions happen in ad hoc spreadsheets or dashboards. People glance at lift, argue about whether the sample size is enough, and sometimes ship features based on noisy or biased results. Agents make this worse if they are wired to react to any small uplift they see.

This tool wraps a few standard methods into one consistent, agent‑friendly interface:

- **Easy-mode dispatcher (`decide`)** — no need to know which statistical method to use. Paste your numbers and it auto-selects A/B, Bayesian, DiD, or planning from your input fields.
- **Frequentist A/B testing** for classic "control vs variant" questions.
- **Bayesian A/B testing** when you want answers like "there is a 93% chance B is better than A" instead of only p‑values.
- **Difference‑in‑differences (DiD)** for quasi‑experiments like staged rollouts or region‑based launches where you cannot randomize perfectly.
- **Cohort / segment breakdown** when an aggregate result is inconclusive — you can slice by user segment to find hidden signals, with Benjamini-Hochberg correction for 4+ segments.
- **Planning and power checks** so you can see if a test is realistic before you start it.
- **Decision audit** so humans can see what the agent did, why it did it, and how strong the evidence really was.
- **External connectors** — pull experiment data directly from PostHog, normalize it, and run a decision in one step. No manual export needed.

The goal is not to replace your analytics stack, but to give agents a small, reliable decision block they

_meta.json

{
  "ownerId": "kn73d2hycfhnph00er7p4gsn8h85r7cy",
  "slug": "agent-causal",
  "version": "0.10.3",
  "publishedAt": 1778154416976
}

skill-card.md

## Description:

Agent Causal Decision Tool returns structured JSON decisions, key statistics, and audit trails that help agents decide whether to ship, continue, reject, or escalate experiment changes across A/B tests, Bayesian A/B tests, Difference-in-Differences, cohort analysis, planning, and sequential early stopping.

This skill is ready for commercial/non-commercial use.

## Publisher:

[zhumorris](https://clawhub.ai/user/zhumorris)

### License/Terms of Use:

Apache-2.0

## Use Case:

Developers, product analysts, and AI agents use this skill to evaluate experiment or rollout summaries and produce defensible ship, continue, reject, targeted rollout, or review decisions with statistics and audit records.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: Installation can fetch remote code, and the security evidence flags the mutable git clone install path as a review concern.

Mitigation: Prefer a pinned, checksummed release or isolated environment and avoid the mutable git clone install path.

Risk: HTTP JSON-RPC mode can expose a network-facing service if bound or routed beyond local development use.

Mitigation: Keep HTTP mode on localhost unless independent authentication and transport controls are added.

Risk: The PostHog connector makes outbound HTTPS requests and uses API credentials when explicitly invoked.

Mitigation: Use read-only PostHog tokens with minimal scopes and keep credentials in environment variables or local config rather than prompts or logs.

Risk: Experiment recommendations can be misleading when inputs are underpowered, aggregate-only, or violate method assumptions.

Mitigation: Review emitted warnings, confidence, limitations, and audit records before acting on ship, reject, or targeted rollout recommendations.

## Reference(s):

- [Agent Causal source repository](https://github.com/ZhuMorris/agent-causal-decision-tool)
- [Agent Causal ClawHub skill page](https://clawhub.ai/zhumorris/skills/agent-causal)

## Skill Output:

**Output Type(s):** [JSON, Text, Shell commands, Configuration, Guidance]

**Output Format:** [Structured JSON decision objects, text summaries, and Markdown guidance with command examples]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May persist local SQLite audit and experiment history records when save or history commands are used.]

## Skill Version(s):

0.10.3 (source: artifact/_meta.json, SKILL.md metadata.openclaw.version, evidence.release.version)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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Machine-readable data

The same record, as JSON, for agents and crawlers.

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Record generated Oct 11, 2026.

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