Skill Detail

🤖 LLM Application Patterns

This skill provides documentation and patterns for building LLM applications, drawing inspiration from Dify and industry best practices.

GitHub:sickn33/antigravity-awesome-skills llm-app-patterns
version c2f146a757fe
static analysis only
no human review yet
Trusted

Current public label

Trusted

The skill is primarily documentation, and it references secrets, which is normal for this type of application but still requires careful handling.

This label is currently coming from the automated scorecard.

Automated result

Trusted

Driftloom found that this skill is mostly documentation and mentions credentials or secrets.

2 low Final label: trusted.

Human review

No human review has been recorded yet.

The current public label is still relying on automation. A human has not weighed in yet.

What happened

Driftloom completed a static scan. It inspected the skill files, recorded findings, and generated a scorecard.

Runtime evidence

No sandbox runtime result has been recorded yet.

What did not happen

  • Driftloom did not run this skill in an isolated sandbox yet.
  • This label is not a guarantee that the skill is safe, bug-free, or appropriate for every environment.
  • A good score does not replace human judgment when a skill touches secrets, shell access, or external systems.

Source provenance

Source: Workspace import

Originally ingested from a local workspace copy.

Scorecard

Safety
94
Quality
94
Transparency
100
Operational
92
Maintenance
82

2 low Final label: trusted.

Severity mix: 2 low

What Driftloom checked

  • Read the skill files and metadata to understand what the skill claims to do.
  • Looked for shell commands and risky command patterns, even if none stood out strongly.
  • Looked for external URLs and network behavior.
  • Looked for secret, token, password, and credential references.
  • Checked whether the skill structure and references looked internally consistent.

Findings

Documentation-only skill structure
structure.docs_only · quality
Low

The source looks almost entirely documentation-based, with no obvious code or config files to inspect. That does not make it bad, but it limits how much automation can meaningfully verify.

File: SKILL.md
Secret or credential reference detected
secret.reference · safety
Low

The docs mention credentials or secrets. That may be normal, but it still tells you the skill expects sensitive material somewhere in the workflow.

File: SKILL.md
Evidence: token
Driftloom is following the boring, correct architecture: Django control plane first, isolated runner later. Miraculously, restraint survives.