cs-product-analyst — quality + safety report

Name: cs-product-analyst — quality + safety report
Item: cs-product-analyst
Rating: 92
Author: Skillproof

In the Skillier index (alireza__cs-product-analyst) · scanned 2026-06-03 · engine: builtin+triage

Quality

92/100

Safety

1 heuristic flag to review

Heuristic flags from the builtin scanner, which is known to over-flag (it trips on legitimate env-reading integrations, security skills, and library .eval calls). This is NOT an authoritative malicious verdict — re-scan with SkillSpector for the authoritative result. Run the authoritative scan →

📇 This skill is in the Skillier index (curated · deduped · quality-filtered). Install Skillier to route & load it into your AI client.

Quality notes

No explicit trigger / 'when to use'

low · quality · body

→ Add a 'When to use' section or 'Use this when …' line listing trigger conditions.

No example

low · quality · body

→ Add at least one worked example (input → expected action/output).

No explicit output format / contract

low · quality · body

→ State the expected output format (structure, sections, or schema).

Powerful tools granted but their use isn't explained

low · quality · frontmatter

→ Explain in the body why each powerful tool is needed, or drop unused grants.

About this skill

Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation.

📄 Read the SKILL.md

---
name: cs-product-analyst
description: Product analytics agent for KPI definition, dashboard setup, experiment design, and test result interpretation.
skills:
  - product-team/product-analytics
  - product-team/experiment-designer
domain: product
model: sonnet
tools: [Read, Write, Bash, Grep, Glob]
---

# Product Analyst Agent

## Skill Links
- `../../product-team/product-analytics/SKILL.md`
- `../../product-team/experiment-designer/SKILL.md`

## Primary Workflows
1. Metric framework and KPI definition
2. Dashboard design and cohort/retention analysis
3. Experiment design with hypothesis + sample sizing
4. Result interpretation and decision recommendations

## Tooling
- `../../product-team/product-analytics/scripts/metrics_calculator.py`
- `../../product-team/experiment-designer/scripts/sample_size_calculator.py`

## Usage Notes
- Define decision metrics before analysis to avoid post-hoc bias.
- Pair statistical interpretation with practical business significance.
- Use guardrail metrics to prevent local optimization mistakes.

Scan or optimize your own skill →

Want a live grade + an embeddable README badge? Run your skill through the free scanner.

Graded independently by Skillproof — nothing to sell the author. Quality is mechanical + corpus-grounded; safety flags are heuristic (builtin+triage), not a malicious verdict.