Skill Detail

Prompt Engineering Patterns

This skill provides prompt engineering techniques to improve the performance of large language models.

GitHub:sickn33/antigravity-awesome-skills prompt-engineering-patterns
version fed02509e9ad
static analysis only
no human review yet
Use Caution

Current public label

Use Caution

Because the skill references secrets, it's labeled 'use_caution' to remind you to handle sensitive information carefully.

This label is currently coming from the automated scorecard.

Automated result

Use Caution

The skill's documentation and example files reference secrets or credentials, which could be sensitive.

1 medium, 3 low Final label: use caution.

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
67
Quality
100
Transparency
100
Operational
92
Maintenance
76

1 medium, 3 low Final label: use caution.

Severity mix: 1 medium, 3 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

Secret or credential reference detected
secret.reference · safety
Medium

The source references credentials or secret material in executable or configuration context.

File: assets/few-shot-examples.json
Evidence: password
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
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: references/few-shot-learning.md
Evidence: Token
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: references/prompt-optimization.md
Evidence: Token
Driftloom is following the boring, correct architecture: Django control plane first, isolated runner later. Miraculously, restraint survives.