ai Skill Quality Gate Pro Pack
Run a buyer-readiness check before publishing an AI agent skill package.
- Detect hardcoded secrets and private data before public release
- Verify if a skill package has clear triggers and demo fixtures
- Generate high-conversion one-liners for marketplace listings
$49
· or 245 creditsSecure checkout via Stripe
Included in download
- Detect hardcoded secrets and private data before public release
- Verify if a skill package has clear triggers and demo fixtures
- terminal automation included
- Ready for Claude Code
Sample input
Run a quality gate audit on my 'jira-automation-pro' skill package before I upload it to the marketplace.
Sample output
## Skill Quality Gate Status: WARN Painpoint: Manual Jira ticket updates Buyer: Project Managers Primary output: Automated ticket transitions Findings: - [CRITICAL] Hardcoded API token found in config.json - [WARN] Missing demo fixture data Must fix: - Remove secrets! Use ENV vars.
ai Skill Quality Gate Pro Pack
Run a buyer-readiness check before publishing an AI agent skill package.
$49
· or 245 creditsSecure checkout via Stripe
Included in download
- Detect hardcoded secrets and private data before public release
- Verify if a skill package has clear triggers and demo fixtures
- terminal automation included
- Ready for Claude Code
- Instant install
Sample input
Run a quality gate audit on my 'jira-automation-pro' skill package before I upload it to the marketplace.
Sample output
## Skill Quality Gate Status: WARN Painpoint: Manual Jira ticket updates Buyer: Project Managers Primary output: Automated ticket transitions Findings: - [CRITICAL] Hardcoded API token found in config.json - [WARN] Missing demo fixture data Must fix: - Remove secrets! Use ENV vars.
About This Skill
Pre-Publish Quality Gate for AI Agent Skills Agent Skill Quality Gate Pro is a static review workflow for builders who package, publish, or sell AI agent skills. It helps check whether a skill package is understandable, claim-bounded, buyer-facing, and ready for marketplace review before upload. What it does The workflow reviews a skill package for common publishing problems: unclear triggers, weak pain-point framing, missing usage notes, thin examples, local-only assumptions, risky file references, unsupported claims, and confusing internal vocabulary. It produces a structured PASS/WARN/FAIL-style report with review notes, must-fix items, and safer listing language. Key checks Structure Review: Checks whether the package has the expected instruction files, supporting docs, examples, and buyer-facing materials. Claim Boundary Review: Flags wording that may overpromise results, imply certification, or claim production/security/compliance readiness without proof. Risk Signal Review: Looks for likely local-only paths, private references, environment assumptions, or sensitive strings that should not appear in a public package. Buyer Clarity Review: Checks whether the skill explains who it is for, what problem it solves, when to use it, and what output the buyer should expect. Listing Readiness: Helps produce a short, claim-safe storefront description that matches the package contents. Why use this over a basic prompt? General LLM review often gives broad feedback or generic praise. This package uses a focused review structure for AI skill packaging, marketplace listings, and buyer handoff quality. It does not just ask whether the skill “looks good.” It asks whether the package is clear, bounded, usable, and safe to describe publicly. Important boundary This is a static publishing-readiness review aid. It does not certify marketplace approval, security, compliance, production readiness, commercial performance, or implementation correctness.
Use Cases
- Detect hardcoded secrets and private data before public release
- Verify if a skill package has clear triggers and demo fixtures
- Generate high-conversion one-liners for marketplace listings
- Enforce PASS/FAIL standards for AI agent skill repositories
- Reviewing an AI agent skill package before publishing it to a marketplace
- Checking a SKILL.md file for unclear triggers, weak scope, or missing output expectations
- Finding internal-only language before a buyer sees the package
- Turning a rough skill folder into a cleaner release candidate
- Preparing storefront copy that matches what the package actually does
- Checking whether examples, docs, and usage notes are strong enough for a buyer handoff
- Reviewing a prompt product or agent workflow package for claim-boundary issues
- Creating a PASS/WARN/FAIL-style readiness report before upload
- Finding missing buyer-facing files such as quickstart notes, examples, receipts, or usage boundaries
- Helping an AI builder clean up a skill package before selling, sharing, or handing it to another operator
Known Limitations
This is a static review aid, not a certification tool. It does not guarantee marketplace approval, buyer adoption, security, compliance, production readiness, or implementation correctness. Results depend on the files and context provided to the reviewing model. It may miss issues that are hidden outside the inspected package, depend on private runtime behavior, or require executing the skill in a specific environment. The package is designed to surface likely readiness issues, unclear claims, missing documentation, and buyer-facing gaps before publication. Final review and publishing decisions remain with the operator.
How to Install
mkdir -p ~/.claude/skills && curl -sL https://www.agensi.io/api/install/ai-skill-quality-gate-pro-pack -o /tmp/ai-skill-quality-gate-pro-pack.zip && unzip -o /tmp/ai-skill-quality-gate-pro-pack.zip -d ~/.claude/skills && rm /tmp/ai-skill-quality-gate-pro-pack.zipFree skills install directly. Paid skills require purchase - use the download button above after buying.
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Security Scanned
Passed automated security review
Permissions
File Scopes
None. This skill does not require external network access or connect to external hosts.
Tags
No install required. This is a portable SKILL.md-style static review package. Best used with Claude, Claude Code, Codex, Cursor, GPT, Kimi, or another capable LLM/agent that can inspect a local skill folder or ZIP contents and produce structured Markdown reports.