Trueline Mcp
Repository: trueline-mcp
Author: rjkaes · Source status: Clear source
Smarter reads, safer edits.
Score basis:Clear source · Risk needs review · Universal
Repository: learn-low-code-agentic-ai
Author: panaversity·Source status: Clear source
Low-Code Full-Stack Agentic AI Development using LLMs, n8n, Loveable, UXPilot, Supabase and MCP.
Score basis:Clear source · High risk signals · Universal
Trust level
67 · Review first
Usable, but inspect source, install method, and risk hints before adoption.
Risk decision
Review required
network access
Install readiness
script-backed · copy-only command
SkillTrust only shows install guidance and copy actions; it never executes installs.
Before you install
Review source, permissions, and execution risk first, then alternatives. Scores prioritize review; they do not replace manual judgment.
Review weakest dimensions and next actions before copying commands.
Evidence or risk signals are incomplete; compare alternatives first.
Audit grade
C · Review first
Execution risk
High
Evidence confidence
65%
SAS-v2.1 radar
SAS-v2.1
Audit grade
C · Review first
Execution risk
High
Top threats
unexpected code execution, data exfiltration
Control gaps
missing license, broad permissions
Evidence confidence
65%
Repository
panaversity/learn-low-code-agentic-ai
Author
panaversity
Community signal
443 stars · 183 forks
Last updated
2025-10-08
Primary source
panaversity/learn-low-code-agentic-ai
Source status
Clear source
Install method
script-backed
Command & code execution
34Focus: Whether it runs commands or scripts
Next action: Manually confirm command-running skills in an isolated directory.
High-risk action confirmation
38Focus: Whether destructive or external actions require confirmation
Next action: Avoid directly installing high-risk skills without confirmation controls.
Network & data egress
43Focus: Whether it may send data out
Next action: If unsure, restrict network access or allow only known domains.
Supported tools can change install steps; Universal entries need source review.
Explicitly supported
Candidate support (inferred)
Candidate tools are inferred signals, not official compatibility certifications.
git clone https://github.com/panaversity/learn-low-code-agentic-ai.gitnetwork access
Review source and permissions before copying install commands.
Evidence or risk signals are incomplete; compare alternatives first.
Focus: Who published it and whether it is traceable
Next action: Review repository, author, and README first; do not install directly when source is pending.
Focus: Whether install steps can be reviewed
Next action: Prefer candidates with install docs and repository evidence.
Focus: Whether tool descriptions may hide instructions
Next action: Read README, rules, and tool descriptions before install.
Focus: What it can access
Next action: Grant only task-required permissions and prefer Ask/manual confirmation.
Focus: Whether it runs commands or scripts
Next action: Manually confirm command-running skills in an isolated directory.
Focus: Whether file reads/writes can escape scope
Next action: Check working directory and file access scope before running.
Focus: Whether it may send data out
Next action: If unsure, restrict network access or allow only known domains.
Focus: Whether it handles tokens, private keys, or agent identity
Next action: Do not provide long-lived tokens or private keys to source-pending skills.
Focus: Whether external content can steer behavior
Next action: For browser/RAG/rules skills, review permissions and confirmation controls first.
Focus: Whether memory or retrieved context can be poisoned
Next action: Try RAG/memory skills in a low-privilege environment first.
Focus: Whether external tools and MCP access are clearly bounded
Next action: Confirm which external tools it will connect to before install, and start with the smallest possible set.
Focus: Whether destructive or external actions require confirmation
Next action: Avoid directly installing high-risk skills without confirmation controls.
Focus: How far impact can spread when something goes wrong
Next action: If unsure, test in an isolated project first.
Focus: Whether actions can be traced
Next action: Prefer candidates with logs or previews.
Focus: Whether it is maintained and reusable
Next action: Check license and maintenance before organizational use.
Usable, but inspect source, install method, and risk hints before adoption.
Phase 1 only shows installation-aware, source-backed signals. SkillTrust does not execute install scripts for users.
Risk factors
network access
Permission hints
repository clone, local runtime dependencies
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Why related: Same task category, Keyword overlap, Similar install method
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Author: Piebald-AI · Source status: Clear source
Fast, cross-platform, real-time token usage tracker and cost monitor for Gemini CLI / Claude Code / Codex CLI / Qwen Code / Cline / Roo Code / Kilo Code / GitHub Copilot / OpenCode / Pi Agent / Piebald.
Score basis:Clear source · Risk needs review · Universal
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
Repository: agentic-ai-engineering
Author: agenticloops-ai · Source status: Clear source
Hands-on tutorials for building AI agents from scratch.
Score basis:Clear source · Low risk signals · Universal
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
Repository: claude-pace-maker
Author: LightspeedDMS · Source status: Clear source
Prevent Claude Code overages by throttling tool usage when getting close to the 5-hour limit and weekly limit.
Score basis:Clear source · Risk needs review · Universal
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
Repository: learn-agentic-ai
Author: panaversity · Source status: Clear source
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
Score basis:Clear source · Risk needs review · Universal
Why related: Keyword overlap, Same repository ecosystem...
Why related: Keyword overlap, Same repository ecosystem, Same author
Repository: learn-agentic-ai-from-low-code-to-code
Author: panaversity · Source status: Clear source
Build production-grade agents with OpenAI AgentKit, a no-code platfrom.
Score basis:Clear source · Risk needs review · Universal
Why related: Keyword overlap, Same repository ecosystem...
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Repository: learn-agentic-ai
Author: panaversity · Source status: Clear source
Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.
Score basis:Clear source · Risk needs review · Universal
Repository: learn-agentic-ai-from-low-code-to-code
Author: panaversity · Source status: Clear source
Build production-grade agents with OpenAI AgentKit, a no-code platfrom.
Score basis:Clear source · Risk needs review · Universal
Repository: agentfactory-business-plugins
Author: panaversity · Source status: Clear source
Marketplace of domain-specific plugins for AI agents (Cowork, Claude Code, OpenClaw).
Score basis:Clear source · Risk needs review · Universal