# Experiment Writeup

> Help PMs write up experiment results. Use when documenting A/B test outcomes, feature experiment learnings, or sharing results with stakeholders.

- **Type:** Skill
- **Install:** `agentstack add skill-hardiktiwari-pm-operating-os-experiment-writeup`
- **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior
- **Seller:** [hardiktiwari](https://agentstack.voostack.com/s/hardiktiwari)
- **Installs:** 0
- **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml)
- **Latest version:** 0.1.0
- **License:** MIT
- **Upstream author:** [hardiktiwari](https://github.com/hardiktiwari)
- **Source:** https://github.com/hardiktiwari/PM-operating-OS/tree/main/skills/experiment-writeup

## Install

```sh
agentstack add skill-hardiktiwari-pm-operating-os-experiment-writeup
```

Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent.

## About

# Experiment Writeup

Help PMs document experiment results in a structured format. Turns raw data into clear narratives with hypothesis, methodology, results, learnings, and decision.

## When to Use

- Documenting A/B test outcomes
- Writing up feature experiment results
- Sharing experiment learnings with stakeholders
- Creating a record for future reference
- When asked "help me write up the results"

## Process / Template

### 1. Gather the Data

- Hypothesis (original statement)
- Experiment design (variants, duration, sample size)
- Primary, secondary, and guardrail metric results
- Statistical significance (p-values, confidence intervals)
- Any qualitative feedback or observations

### 2. Structure the Writeup

**Hypothesis**
- Restate the original hypothesis
- Brief context on why we ran this

**Methodology**
- Variants tested (control vs. treatment)
- Duration and sample size
- Target segment
- Any caveats (traffic issues, external events)

**Results**
- **Primary metric** — direction, magnitude, significance
- **Secondary metrics** — supporting or conflicting signals
- **Guardrail metrics** — did anything regress?

**Learnings**
- What did we learn? (beyond the numbers)
- Surprises or unexpected findings
- Implications for future work

**Decision**
- Ship / Iterate / Kill
- Rationale for the decision
- Next steps (if iterating)

### 3. Write Clearly

- Lead with the decision and key takeaway
- Use plain language; avoid jargon
- Include numbers with context (e.g., "+12% vs. control")
- Call out statistical significance explicitly

## Output

A structured **Experiment Writeup** suitable for:
- Stakeholder sharing (Slack, email)
- Internal documentation
- Experimentation platform notes
- Retrospectives and planning

Format: concise, scannable, decision-oriented. Typically 1–2 pages or equivalent in markdown.

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [hardiktiwari](https://github.com/hardiktiwari)
- **Source:** [hardiktiwari/PM-operating-OS](https://github.com/hardiktiwari/PM-operating-OS)
- **License:** MIT

Install and usage instructions live in the source repository linked above.

## Pricing

- **Free** — Free

## Security capabilities

Automated source analysis of v0.1.0 — what this tool can access:

- **Network access:** no
- **Filesystem access:** no
- **Shell / process execution:** no
- **Environment & secrets:** no
- **Dynamic code execution:** no

*"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.*


## Versions

- **0.1.0** — security scan: passed — Imported from the upstream source.

## Links

- Listing page: https://agentstack.voostack.com/l/skill-hardiktiwari-pm-operating-os-experiment-writeup
- Seller: https://agentstack.voostack.com/s/hardiktiwari
- Browse the marketplace: https://agentstack.voostack.com/browse

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