Replicate

---
name: replicate
description: "Replicates results from the replication package on a clean environment and traces published numbers to code and data."
argument-hint: "[model name or package path]"
user-invocable: true
allowed-tools: Read, Glob, Grep, Write, Edit, Bash, AskUserQuestion, Task
---

# /replicate — Replicate

**Category:** Validation & QA · **Primary agent:** `replication-auditor`

Replicates results from the replication package on a clean environment and traces published numbers to code and data.

Arguments: `$ARGUMENTS`

## Purpose
Replicate published or reported results from the replication package in a clean environment, compare outputs within tolerance, and trace every table and chart number to the script and data version that produced it. The output is a replication report with a discrepancy log and root causes.

**Use it when** results are about to be published or used in advice, after model changes, or when reported numbers are questioned.

**Do not use it when** the replication package does not exist yet; build it first with the reproducibility engineer.

## Before you start
1. **Check required inputs.** For each input below record `FOUND` or `ABSENT` (never 'assumed'):
   - Replication package
   - Published outputs
   If a required input is `ABSENT`, that part is **`NOT ASSESSED — NO DATA`**: do not estimate or invent it; name what is missing and which skill produces it. If all required inputs are absent, stop and say so.
2. **Check confidentiality.** Identify the data classification level of the inputs (`.claude/docs/confidentiality-and-ai-use.md`). Do not proceed with restricted or confidential data in a way the policy does not allow.
3. **Collaboration.** Follow `.claude/docs/collaboration-protocol.md`: ask questions first, present options, get approval before writing files, and update `production/session-state/active.md` after each approved section.

## Agents
- `replication-auditor` — primary agent; coordinates the work and integrates results
- `reproducibility-engineer` — Guarantees reproducibility

Run these agents as subagents (the `Task` tool), in parallel where the steps are independent. Subagents only write to the paths named in your instructions.

## Key design decisions (present options, let the user choose)
| Decision | Typical options | What it changes |
|---|---|---|
| Environment | container, fresh virtual environment, clean machine | Independence |
| Tolerance | exact, numerical tolerance by output | Pass criteria |
| Scope | all outputs, key tables | Effort |

## Workflow
1. Build the clean environment from lock files or containers
2. Run the one-command replication and compare outputs with tolerance
   - Record runtime and any manual steps needed (each is a finding)
3. Trace each table/chart number to its script and data version
4. Log discrepancies and root causes

## Checkpoints with the user
- After scope: confirm outputs and tolerances
- After the run: review discrepancies before root-cause work

## Common pitfalls
- Running on the author's machine
- Accepting "rounding" without checking
- Ignoring numbers quoted only in the text

## Output
`validation/<model>/replication-report.md`

The report documents the environment, run results, the number-tracing map and the discrepancy log with root causes.

## Gate / verdict
- Outputs reproduced within tolerance, or discrepancies logged with cause
- Manual steps needed to replicate are reported as findings

## Suggested next steps
- `/validate-model`
- `/model-release`