Security Audit

developer-growth-analysis

github.com/davila7/claude-code-templates

AI SkillCommit 458b11867eae

TRUSTED

Scanned 11 days ago

Critical

Immediate action required

High

Priority fixes suggested

Medium

Best practices review

Low

Acknowledged / Tracked

Trust Assessment

developer-growth-analysis received a trust score of 76/100, placing it in the Mostly Trusted category. This skill has passed most security checks with only minor considerations noted.

SkillShield's automated analysis identified 3 findings: 0 critical, 1 high, 1 medium, and 1 low severity. Key findings include Network egress to untrusted endpoints, Covert behavior / concealment directives, Sensitive Chat History Data Sent to External Service (Slack).

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 12, 2026 (commit 458b1186). SkillShield performs automated 4-layer security analysis on AI skills and MCP servers.

Layer Breakdown

Manifest Analysis

91%

Static Code Analysis

100%

Dependency Graph

100%

LLM Behavioral Safety

85%

Behavioral Risk Signals

Network Access

2 findings

Filesystem Write

1 finding

Security Findings3

Severity	Finding	Layer	Location
HIGH	Sensitive Chat History Data Sent to External Service (Slack) The skill explicitly instructs the host LLM to read the user's local Claude chat history (`~/.claude/history.jsonl`). This file can contain highly sensitive information, including code, project details, and potentially credentials (as indicated by `pastedContents` and examples like 'auth configs'). The skill then generates a 'personalized growth report' based on this history and automatically sends the complete report to the user's Slack DMs. While intended for the user's own DMs, this constitutes transferring potentially sensitive local data to a third-party external service (Slack) without explicit mechanisms for redacting or filtering sensitive information from the generated summary. This could inadvertently leak confidential project details, internal technology names, or even parts of credentials if they were present in the chat history and summarized in the report. Implement robust data sanitization and redaction mechanisms before generating and sending the report to Slack. Specifically, ensure that any potentially sensitive information (e.g., API keys, specific project names, internal system details, full code snippets from `pastedContents`, or 'auth configs') is either generalized, masked, or completely removed from the report. Provide clear user consent and warnings about the data transfer to an external service. Consider offering an option to review the report locally before sending it to Slack.	LLM	SKILL.md:50
MEDIUM	Network egress to untrusted endpoints HTTP request to raw IP address Review all outbound network calls. Remove connections to webhook collectors, paste sites, and raw IP addresses. Legitimate API calls should use well-known service domains.	Manifest	cli-tool/components/mcps/devtools/figma-dev-mode.json:4
LOW	Covert behavior / concealment directives Multiple zero-width characters (stealth text) Remove hidden instructions, zero-width characters, and bidirectional overrides. Skill instructions should be fully visible and transparent to users.	Manifest	cli-tool/components/mcps/devtools/jfrog.json:4

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