Skill audit report
cargo-context Audit report.
Read and write the workspace GTM knowledge base — the git-backed repository of markdown describing ICPs, personas, plays, proof points, objections, competitors, and signals — plus its runtime sandbox and typed knowledge graph. Triggers: \"document our ICP\", \"write up this persona\", \"what is our positioning\", \"add a battlecard\", \"capture this objection\", \"what do we know about <segment>\", \"update our context\", \"what is in the context repo\", \"who do we sell to\". Skip when: discovering who actually buys from you by analyzing won/lost data — that is cargo-gtm (this skill writes the conclusion down, it does not derive it); storing structured records rather than prose — use cargo-storage; attaching documents to an agent for RAG — use cargo-content.
OpenAgentSkill Trust Score
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
FAIL30
20 GitHub stars
Stars/forks activity
FAIL32
20 stars, 3 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
2d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
FAIL36
command execution surface, credential or environment access
Install availability
PASS92
npx skills add getcargohq/cargo-skills --skill cargo-context
Install command safety
PASS92
standard package or runtime install path
Permission surface
FAIL22
secrets or environment access, shell or command execution
Repository evidence
PASS86
https://github.com/getcargohq/cargo-skills/tree/main/cargo-context
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install and adoption review
Install path
92
npx skills add getcargohq/cargo-skills --skill cargo-context
Repository
88
https://github.com/getcargohq/cargo-skills/tree/main/cargo-context
License
86
MIT
Maintenance
100
2d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
36
command execution surface, credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
22
secrets or environment access, shell or command execution
Stars/forks activity
32
20 stars, 3 forks; issue activity unavailable in current metadata
Adoption
42
20 GitHub stars
Warnings
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Low GitHub adoption signal
- AI review approval is missing
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- GitHub adoption: 20 GitHub stars
- Stars/forks activity: 20 stars, 3 forks; issue activity unavailable in current metadata
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Review status: AI review approval is missing
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.
Compare nearby options
Related skills to audit next
kb-article
Draft a knowledge base article from a resolved issue or common question. Use when a ticket resolution is worth documenting for self-service, the same question keeps coming up, a workaround needs to be published, or a known issue should be communicated to customers.
30K Stars · Audit report
full-output-enforcement
Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.
99K Stars · Audit report
peft-fine-tuning
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
13K Stars · Audit report