Edge Copy setup link
Skill evidence / zero-defect
Skill profile

zero-defect

Use when you need maximum precision on a critical task : production deployments.

What this skill does for you

Use when you need maximum precision on a critical task : production deployments, security-sensitive code, financial calculations, or any work where mistakes are unacceptable.

Quoted from the skill description in the pinned source

Who made it

Publishersharpdeveye
Repositorymaestro
Installs296as of Sep 27, 2026

Use it with Edge

Set up Edge for me: read getedge.cc/SKILL.md and follow it. Then use Edge to load the zero-defect skill from sharpdeveye/maestro.

Paste it into Claude, ChatGPT, Codex or Cursor.

Or read the skill source first.

Security evidence

Security state at 2026-09-30: reviewed. State definitions. This records static evidence for this revision; live load eligibility is checked again.

3 static scans, 0 counted findings. The listed scanners recorded the results shown for this package revision.

ScannerDateResultFindings
Edge static checks2026-09-27Pass0
Cisco skill-scanner2026-09-27Pass0
Semgrep Edge rules2026-09-27Pass0

Static scans check the code, not how well the skill works.

Scanner scope, raw findings and mirrored provider records

Edge static checks (Edge-run): edge-static/1.0.1. Scope: Static patterns for remote execution, credentials with network sends, obfuscation, prompt overrides, hidden Unicode, binaries and persistence. Revision: 00f9115d446a8ba26b8f18f6ed306bc4a21807c3. Raw findings: . Counted findings: .

Cisco skill-scanner (Edge-run): cisco-skill-scanner/2.1.0. Scope: Local static, YARA, pipeline and behavioral analyzers; no LLM or AI Defense analysis. Revision: 00f9115d446a8ba26b8f18f6ed306bc4a21807c3. Raw findings: 1. Counted findings: .

Semgrep Edge rules (Edge-run): semgrep/1.178.0+edge-rules.c15016a36fd3+offline-v1. Scope: Six Edge-authored static rules with Semgrep CE; offline execution and metrics disabled. Semgrep-maintained rules excluded. Revision: 00f9115d446a8ba26b8f18f6ed306bc4a21807c3. Raw findings: . Counted findings: .

Source and licence

Repository
https://github.com/sharpdeveye/maestro
Pinned skill file
View source at revision
Revision
00f9115d446a8ba26b8f18f6ed306bc4a21807c3
Package hash
sha256:ab10d9bf259e84e121f16c420b4e2d5cd50024be57e4616dfb9003e4b6d5aa6d
Licence
MIT