Experiment Writeup
Help PMs write up experiment results. Use when documenting A/B test outcomes, feature experiment learnings, or sharing results with stakeholders.
Install
$ agentstack add skill-hardiktiwari-pm-operating-os-experiment-writeup ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README — it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-hardiktiwari-pm-operating-os-experiment-writeup) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming — see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps — measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →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
- Source: hardiktiwari/PM-operating-OS
- License: MIT
Install and usage instructions live in the source repository linked above.
Reviews
No reviews yet — be the first.
Write a review
Versions
- v0.1.0 Imported from the upstream source.