Compare skills
Pick 2–4 skills and compare what really matters: fit, risk, install effort, and community signal.
Comparison matrix
Highlights show current best; tooltip explains diff/best rules.
Score-basis diff rules / risk tag notes
Start with the matrix. Open this section when you need to understand audit grades, top threats, control gaps, and best-value highlights.
Suggested baseline
Repository: openclaw/skills
Author: james-le-twelve-labs · Source status: Clear source
Search inside videos, generate summaries, and analyze video content using TwelveLabs video understanding AI.
Score basis：Clear source · High execution risk · OpenClaw · Evidence completeness 67%
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How differences are detected
A row is marked different when selected skills have distinct values. Only-differences mode hides rows that are identical.
How best values are highlighted
Pre-install score, evidence completeness, and community signal prefer higher values; execution risk and install friction prefer lower values.
How to read risk tags
Risk tags come from SAS-v2.1 public-evidence signals and point to command, network, secret, context, or supply-chain items to review before install.
Selected audit signals
LookupMark File Sender
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
Freshsales
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
predictive-scaler
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, shell without guardrails, network without allowlist
| Dimension | LookupMark File Sender | Freshsales | predictive-scaler |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 85 · Manual review | 82 · Manual review | 85 · Manual review |
Score basis | Clear source, High execution risk, OpenClaw | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal |
Threat tags | unexpected code execution, identity privilege abuse, data exfiltration | unexpected code execution, identity privilege abuse, data exfiltration | unexpected code execution, data exfiltration, human approval gap |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, shell without guardrails, network without allowlist |
Permission summary | Permission review, Network, Secrets, Command | Permission review, Network, Secrets, Command | Permission review, Network, Command |
Evidence completeness | 67% | 67% | 63% |
| Source & provenance | |||
Category | Knowledge & RAG | Data & Analytics | Data & Analytics |
Freshness | 2026-04-05 | 2026-03-04 | 2026-03-25 |
| Risk & permission signals | |||
Audit signals | needs credentials, network access, runs shell, writes files | needs credentials, network access, runs shell, writes files | network access |
| Install & compatibility | |||
Supported tools | OpenClaw | Universal | Universal |
Install friction | 65 | 65 | 40 |
Repository: openclaw/skills
Author: cinience · Source status: Clear source
Use when text embeddings are needed from Alibaba Cloud Model Studio models for semantic search, retrieval-augmented generation, clustering, or offline vectorization pipelines.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: openclaw/skills
Author: cinience · Source status: Clear source
Use when multimodal embeddings are needed from Alibaba Cloud Model Studio models such as `qwen3-vl-embedding` for image, video, and text retrieval, cross-modal search, clustering, or offline vectorization pipelines.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
Repository: openclaw/skills
Author: cinience · Source status: Clear source
Use when working with OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 69%
Repository: openclaw/skills
Author: neroagent · Source status: Clear source
Core memory resilience layer: WAL (Write-Ahead Log), Working Buffer, and three-layer memory integration.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 71%
Repository: openclaw/skills
Author: eternal0404 · Source status: Clear source
The all-in-one master skill — unified interface for every capability domain.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 67%
