do-in-steps
Execute one complex task as ordered, dependent steps run sequentially, passing context from each step to the next, with per-step LLM-as-a-judge verification. Use when later steps depend on the results of earlier ones.
68
85%
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Critical
Do not install without reviewing
Security
1 critical severity finding. Installing this skill is not recommended: please review these findings carefully if you do intend to do so.
E006: Malicious code pattern detected in skill scripts.
What this means
Detected high-risk code patterns in the skill content — including its prompts, tool definitions, and resources — such as data exfiltration, backdoors, remote code execution, credential theft, system compromise, supply chain attacks, and obfuscation techniques.
Why it was flagged
The skill mandates embedding an environment variable (CLAUDE_PLUGIN_ROOT) into prompts sent to external sub-agents and instructs meta-judges/sub-agents to observe/read artifacts, which intentionally creates an easy channel for leaking environment or file-system information to remote models (potential data exfiltration risk).
Low
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
W011: Third-party content exposure detected (indirect prompt injection risk).
What this means
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Why it was flagged
The skill reads the outsider-authored free-form `task` argument (user-supplied text) and passes it into both the meta-judge prompt (`## User Prompt`) and the implementation agent prompts, so the required runtime workflow ingests outsider text directly without requiring it to first select a specific item.
- Repository
- NeoLabHQ/context-engineering-kit
- Commit
8539779
- Audited
- Security analysis
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