Security Audit
countdown-api-automation
github.com/ComposioHQ/awesome-claude-skillsTrust Assessment
countdown-api-automation received a trust score of 85/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.
SkillShield's automated analysis identified 1 finding: 0 critical, 0 high, 1 medium, and 0 low severity. Key findings include Broad access via RUBE_REMOTE_WORKBENCH.
The analysis covered 4 layers: Manifest Analysis, Static Code Analysis, Dependency Graph, LLM Behavioral Safety. All layers scored 70 or above, reflecting consistent security practices.
Last analyzed on February 20, 2026 (commit 27904475). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.
Layer Breakdown
Behavioral Risk Signals
Security Findings1
| Severity | Finding | Layer | Location | |
|---|---|---|---|---|
| MEDIUM | Broad access via RUBE_REMOTE_WORKBENCH The skill instructs the LLM to use `RUBE_REMOTE_WORKBENCH` for 'Bulk ops' with `run_composio_tool()`. `RUBE_REMOTE_WORKBENCH` suggests a remote execution environment, and `run_composio_tool()` implies the ability to execute arbitrary Composio tools. This instruction, without explicit scope limitation to the `countdown_api` toolkit or clear sandboxing details, grants the LLM access to a potentially very broad set of operations, which could extend beyond the intended scope of Countdown API automation. Clarify the scope and limitations of `RUBE_REMOTE_WORKBENCH` and `run_composio_tool()` when used within this skill. Specifically, ensure that `run_composio_tool()` is constrained to only execute tools from the `countdown_api` toolkit or provide explicit sandboxing guarantees for `RUBE_REMOTE_WORKBENCH`. If its capabilities are inherently broad, consider if this skill truly requires such a powerful tool, or if a more constrained alternative is available. | LLM | SKILL.md:80 |
Scan History
Embed Code
[](https://skillshield.io/report/66a8978b9c221606)
Powered by SkillShield