Skill audit report

CTCDecoder audit report.

Connectionist Temporal Classification (CTC) decoding algorithms: best path, beam search, lexicon search, prefix search, and token passing. Implemented in Python.

EXPERIMENTALREVIEWSafe to tryGenerated Jun 16, 2026Heuristic metadata audit
83
Audit
82
Trust
77
Quality
94
Security
76
Maintain
92
Install

OpenAgentSkill Trust Score

82
Strong shortlist

Stars, maintenance, license, docs, dependency risk, and installability.

The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.

GitHub adoption

INFO

76

836 GitHub stars

Recent maintenance

INFO

76

5mo since push

License clarity

PASS

86

MIT

README/SKILL.md completeness

PASS

90

Metadata includes enough usage and workflow context

Dependency risk

INFO

72

credential or environment access

Install availability

PASS

92

npx skills add githubharald/CTCDecoder

Repository evidence

PASS

86

https://github.com/githubharald/CTCDecoder

Review status

PASS

88

AI review data available

Checks

Install and adoption review

6 passed 路 3 review

Install path

92

PASS

npx skills add githubharald/CTCDecoder

Repository

88

PASS

https://github.com/githubharald/CTCDecoder

License

86

PASS

MIT

Maintenance

76

CHECK

5mo since push

AI review

88

PASS

Approved with no listed issues

README/SKILL.md completeness

90

PASS

Usable description available

Dependency risk

72

CHECK

credential or environment access

Adoption

88

PASS

836 GitHub stars

Warnings

  • Quality score needs review

Method

This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.

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