decision-hub: Command for Claude Code

.claude/commands/test-upload-evals-skill.md

test-upload-evals-skill is a command for Claude Code from pymc-labs/decision-hub. It costs 0 tokens per session (1,868 once invoked), scanned C, original, MIT.

An end-to-end test command for publishing a skill with automated evaluations in a development environment. It runs the required database, deployment, creation, publishing, and evaluation steps in order.

In plain words
What is it for?
Use it to test that a Bayesian A/B testing skill can be created, published, and evaluated through the development server and dhub CLI.
Why use it?
It checks the complete publishing workflow instead of testing isolated commands. It stops when a step fails so the problem can be reported before continuing.

Command for Claude Code

Written for Claude Code: installed under .claude/.

This is pymc-labs/decision-hub's own configuration. It tells Claude Code how to work on decision-hub itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything decision-hub configures →

Reuse

Borrowing it

Nothing to install: this file belongs to pymc-labs/decision-hub. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/pymc-labs/decision-hub/main/.claude/commands/test-upload-evals-skill.md
Clone the repo
git clone --depth 1 https://github.com/pymc-labs/decision-hub

Made for: Claude Code.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for test-upload-evals-skill

README.md
[![agentmods](https://agentmods.dev/badge/commands/pymc-labs/decision-hub/test-upload-evals-skill/github.svg)](https://agentmods.dev/commands/pymc-labs/decision-hub/test-upload-evals-skill)
Your own site
<a href="https://agentmods.dev/commands/pymc-labs/decision-hub/test-upload-evals-skill"><img src="https://agentmods.dev/badge/commands/pymc-labs/decision-hub/test-upload-evals-skill/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for test-upload-evals-skill

Your own site · 80×15
<a href="https://agentmods.dev/commands/pymc-labs/decision-hub/test-upload-evals-skill"><img src="https://agentmods.dev/badge/commands/pymc-labs/decision-hub/test-upload-evals-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,868 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.01868
Opus 5 $0.00000 $0.00934
Sonnet 5 $0.00000 $0.00374
Haiku 4.5 $0.00000 $0.00187

Measured 9d ago against content hash aabbf6c0f18d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade C, and why

test-upload-evals-skill scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Downloads and executes remote codehighSupply chain

curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.

curl -s "https://pymc-labs--api-dev.modal.run/v1/skills/lfiaschi/bayesian-ab-test/eval-report?semver=<version>" | python3 -m json.tool

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

curl -s "https://pymc-labs--api-dev.modal.run/v1/skills/lfiaschi/bayesian-ab-test/eval-report?semver=<version>" | python3 -m json.tool
.claude/commands/test-upload-evals-skill.md · 170 lines

How it starts

The opening of the file, as written. The whole thing — 170 lines — stays where its author put it; the contents beside it link to each section on GitHub.

End-to-end test of publishing a skill with evals on the dev environment. Run each step sequentially — stop and report if any step fails.

All CLI commands use: DHUB_ENV=dev uv run --package dhub-cli dhub <command>

Prerequisites

  1. DB migration: Before publishing, ensure the dev database has all columns:

    cd server && DHUB_ENV=dev uv run --package decision-hub-server python -c "
    from decision_hub.settings import create_settings
    from decision_hub.infra.database import create_engine, metadata
    settings = create_settings('dev')
    engine = create_engine(settings.database_url)
    metadata.create_all(engine)
    "
    
  2. Deploy server: Deploy the latest server code to dev Modal:

    cd server && DHUB_ENV=dev modal deploy modal_app.py
    

Step 1: Create the test skill

Create all files under <scratchpad>/bayesian-ab-test/.

SKILL.md

---
name: bayesian-ab-test
description: "Bayesian A/B testing skill using PyMC for hypothesis testing on log-normally distributed data"
runtime:
  language: python
  entrypoint: run_ab_test.py
evals:
  agent: claude
  judge_model: claude-sonnet-4-5-20250929
---

# Bayesian A/B Test

You are a statistical analysis assistant that performs Bayesian A/B testing.

When asked to run the A/B test, execute the `run_ab_test.py` script and present the results clearly.

## Instructions

1. Run `python run_ab_test.py` to generate the dataset and perform the analysis.
2. Present the output verbatim, ensuring all sections are included:
   - Hypotheses (H0 and H1)
   - Generated data summary
   - Sampling diagnostics (convergence, R-hat, ESS)
   - Posterior summary and credible intervals
   - Conclusion about the null hypothesis
3. Do NOT modify the script or its output.

pyproject.toml

CRITICAL: requires-python MUST be >=3.11 (not >=3.10) because PyMC 5.27.1 requires Python 3.11+. If set to >=3.10, uv sync will fail in the sandbox with a resolution error. Do NOT include a [build-system] section — it causes uv sync to attempt an editable install which fails.

Read the full file on GitHub · 170 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 170 lines · 0 tokens per session scan C aabbf6c0f18d

Subscribe to this mod's changes

test-upload-evals-skill is a command published in the GitHub repository pymc-labs/decision-hub (99 stars, last pushed 27d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,868 tokens. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.