Skill audit report
Use this to estimate what an LLM call or feature will cost, and to compare models on price, before or after shipping. Trigger on "how much will this cost", "estimate my OpenAI/Anthropic bill", "is a cheaper model worth it", "cost of this prompt", "project my LLM spend". Ships a runnable, tested calculator so the numbers are real, not hand-waved.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
WARN48
33 GitHub stars
Stars/forks activity
WARN48
33 stars, 18 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
17d since push
License clarity
PASS86
CC0-1.0
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
INFO72
credential or environment access
Install availability
PASS92
npx skills add ContextJet-ai/awesome-llm-observability --skill estimate-llm-cost
Install command safety
PASS92
standard package or runtime install path
Permission surface
INFO74
secrets or environment access
Repository evidence
PASS86
https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/estimate-llm-cost
Review status
INFO66
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add ContextJet-ai/awesome-llm-observability --skill estimate-llm-cost
Repository
88
https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/estimate-llm-cost
License
86
CC0-1.0
Maintenance
100
17d since push
AI review
55
The built-in price table is only approximate and can become stale; this is disclosed, but output should always be verified against current provider pricing for billing decisions.
README/SKILL.md completeness
86
Usable description available
Dependency risk
72
credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
74
secrets or environment access
Stars/forks activity
48
33 stars, 18 forks; issue activity unavailable in current metadata
Adoption
42
33 GitHub stars
Warnings
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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