Skills zach-feature-demand-validator

📦

zach-feature-demand-validator

Name: zach-feature-demand-validator
Author: zach22-1999

Content revision r1 Medium Risk ⚙️ External commands📁 Filesystem access🌐 Network access⚡ Contains scripts

Validate Feature Demand with Evidence

Product teams often chase features without proof that customers care. This skill compares reviews, keyword signals, and community discussion to validate feature demand.

Supports: Claude Codex Code(CC)

📊 76 Adequate

Install with my Agent

Copy this request to your Agent. It includes the canonical Skill page and manifest.

Agent request

Review the Skillstore skill "zach-feature-demand-validator" from https://skillstore.io/skills/zach22-1999-zach-feature-demand-validator.md and its manifest at https://skillstore.io/api/skills/zach22-1999-zach-feature-demand-validator/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

Your Agent should still show its plan and request any confirmation required by the security policy.

Agent-readable resources

Use these links when an AI agent, crawler, or script needs clean context instead of reading the full page.

Markdown detail GET /skills/zach22-1999-zach-feature-demand-validator.md Signed manifest GET /api/skills/zach22-1999-zach-feature-demand-validator/manifest Signed lockfile GET /api/skills/zach22-1999-zach-feature-demand-validator/lockfile

Test it

Using "zach-feature-demand-validator". Validate steam demand for air fryers in the US market.

Expected outcome:

A demand verdict with confidence level and action recommendation.
A review signal summary with feature mention rate and sentiment split.
Keyword and community signal summaries with source notes.

Using "zach-feature-demand-validator". Analyze a local review_source_pack for anti-drip demand.

Expected outcome:

A fallback report that states which dimensions were fully verified.
A standardized review CSV with source, raw file, and capture fields.
Warnings about missing keyword or community evidence.

Using "zach-feature-demand-validator". Check final deliverables for a feature validation project.

Expected outcome:

A validation result that lists missing report files, missing CSV files, invalid ASIN values, or incomplete fallback evidence.

Security Audit

Medium Risk

v5 • 7/8/2026 Open versioned report

Most static findings are false positives caused by Markdown backticks, fenced command examples, public source URLs, parser regexes, and readable documentation. The skill is not malicious, but it understates its risk because it requests file editing, Bash, and network tools while documenting automated repair and script workflows.

Files scanned

2,354

Lines analyzed

Review items

False positives ignored

Confirmed security concerns (2)

Medium

SKILL.md:9-10

Understated Tool Risk Declaration

The skill declares Write, Edit, Bash, WebSearch, and WebFetch access while marking itself low risk. This mismatch can cause users to underestimate file modification, command execution, and network access.

The broad allowed-tools list and low risk declaration are adjacent in the front matter. The workflow also includes local script execution and public web fetching.

Low

README.md:137-149

Overbroad Automatic Repair Prompt

The README suggests asking an AI to inspect the current workspace and automatically repair issues if possible. This can cause unintended file changes without a clear confirmation boundary.

The troubleshooting prompt explicitly asks the AI to check related files and repair problems where possible. It appears operational rather than malicious, so the severity is low.

Risk Factors

⚙️ External commands (111)

📁 Filesystem access (7)

examples/review-source-pack/README.md:15 README.md:62 README.md:155 README.md:159 scripts/validate_deliverables.py:64 scripts/validate_deliverables.py:77 scripts/validate_deliverables.py:226

🌐 Network access (6)

examples/review-source-pack/source_manifest.json:6 references/review_fallback_pack.md:24 scripts/generate_community_csv.py:14 scripts/parse_review_source_pack.py:20 SKILL.md:170 SKILL.md:240

⚡ Contains scripts (1)

scripts/parse_review_source_pack.py:35-40

Audited by: codex View Audit History →

Share & cite this report

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Open versioned report

Copy report link

https://skillstore.io/skills/zach22-1999-zach-feature-demand-validator/audits/5?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

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Academic citations (APA · BibTeX · CFF)

APA citation

zach22-1999. (2026). zach-feature-demand-validator security audit report (audit version 5) [Author version unspecified]. Skillstore. https://skillstore.io/skills/zach22-1999-zach-feature-demand-validator/audits/5

BibTeX citation

@techreport{zach22-1999-zach22-1999-zach-feature-demand-validator-2026,
  author = {zach22-1999},
  title = {zach-feature-demand-validator security audit report (audit version 5)},
  institution = {Skillstore},
  year = {2026},
  number = {5},
  url = {https://skillstore.io/skills/zach22-1999-zach-feature-demand-validator/audits/5},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "zach-feature-demand-validator security audit report (audit version 5)"
version: "unspecified"
type: report
authors:
  - name: "zach22-1999"
date-released: "2026-07-08"
url: "https://skillstore.io/skills/zach22-1999-zach-feature-demand-validator/audits/5"
identifiers:
  - type: other
    value: "skillstore:zach22-1999-zach-feature-demand-validator:audit:5"
    description: "Skillstore immutable audit report identifier"

Skillstore Score

Why this score Evidence Confidence: Medium

Architecture

Maintainability

Content

Community

Spec Compliance

What You Can Build

Validate a Micro-Feature

Check whether customers mention, request, or reject a proposed feature before sourcing or tooling work.

Compare Competitor Gaps

Use reviews and search signals to decide whether a competitor feature is worth following.

Prepare Evidence for Review

Create a Markdown report and CSV evidence files for an internal product decision meeting.

Try These Prompts

Validate a Feature

Validate whether steam is real customer demand for air fryers in the US market. Use reviews, keyword signals, and community discussion.

Use an ASIN Baseline

Validate whether the self-cleaning feature on this ASIN is worth copying. Compare review sentiment, keyword demand, and competitor evidence.

Run Fallback Review Pack

Use my review_source_pack to validate foldable handle demand. Mark fallback limitations and keep all source fields in the CSV outputs.

Audit Evidence and Decide

Review the feature validation report and CSV files. Identify weak evidence, negative signals, missing sources, and the safest product recommendation.

Best Practices

Use real ASINs and record product names, sources, and capture dates.
Collect enough positive and negative reviews before drawing conclusions.
Separate data facts, public information, and analysis in the final report.

Avoid

Using placeholder ASINs or brand names as evidence.
Treating fallback keyword estimates as precise Sorftime data.
Ignoring negative feature feedback because it conflicts with the desired conclusion.