據 AlphaMoat 統計,截至 2026-09,Skill Vetter V2 0.0.6 累計下載 4,374 次、獲 0 個星標,在全部 5,078 個 AI 技能中排名第 2,234 位,在「AI 智慧體」分類(969 個)中排名第 426 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 129 | 4302 | - | - |
| SkillHub ↗ | 126 | 4374 | - | — |
各平台的計數口徑不同,統計時間不一致。
該工作流檢查技能的能力、指令邊界、隱藏假設、資料外發和證據不足等風險,並生成供審閱的本地報告。它本身不執行密碼學驗證、不簽發收據,也不能證明技能安全或代替人工信任判斷。掃描工具可能輸出目標檔案中的憑據樣式內容,因此只檢查指定技能目錄,並妥善保護原始掃描結果。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 4374 | — | - |
| 2026-08 | 4122 | +7.3% | - |
| 2026-07 | 3843 | +14.4% | - |
| 2026-06 | 3360 | +5.1% | - |
| 2026-05 | 3197 | — | - |
| 2026-03 | 2354 | — | - |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
--- name: skill-vetter-v2 description: Verification-guided review workflow for inspecting skill packages before use or publication. Classifies risk and flags claims that exceed evidence. Does not itself perform cryptographic verification, emit receipts, or prove a skill is safe. metadata: --------- # Skill Vetter v2 Skill Vetter v2 is a verification-guided review skill for inspecting Claude/OpenClaw skill packages before use or publication.
It helps identify risky instructions, unclear authority boundaries, hidden assumptions, undeclared egress, provenance gaps, and claims that exceed evidence.
This skill does not itself perform cryptographic verification, emit Settlement Attestation Receipts, or prove that a skill is safe. It is a structured review workflow.
For actual receipt verification, use SettlementWitness or DefaultVerifier MCP verify_receipt.
Category: verifiability-guided workflow skill.
Boundary: Skill Vetter v2 reviews claims and risk signals; it does not certify, approve, execute, or cryptographically verify artifacts.
---
Analyze skills before installation or use. Classify capabilities, risks, and trust dependencies with structured local review. Optionally verify the completed report with SettlementWitness.
This is a **packaged vetting skill**, not a thin wrapper. It preserves local inspection as the primary decision path and adds an optional verification layer for auditability.
## Data handling and trust
This skill defines a **local review workflow** with an optional verification step for the final report.
* Perform capability analysis and risk classification locally
* Do **not** send secrets, credentials, private keys, seed phrases, personal data, or full private repositories to any external service
* If optional verification is used, send only the minimum structured task data needed to validate the report
* Verification does **not** decide whether a skill is safe to install; it only validates that the vetting report matches the stated evaluation spec
* Identity is optional; no wallet access, account access, or credentials are required
## Core Principle
Never outsource the safety decision.
External systems may help verify that a report was produced correctly, but the actual judgment about whether a skill should be trusted remains local and reviewable.
## Quick Reference
| Situation | Action |
| ---------------------------------------------- | -------------------------------------------------------------- |
| New skill from unknown source | Run full local vetting workflow |
| Skill asks for secrets or credentials | Escalate risk immediately |
| Skill writes outside workspace | Mark as high risk unless clearly justified |
| Skill calls external services | Classify trust dependency and data exposure |
| Skill contains obfuscation or hidden execution | Mark unsafe |
| Final report is complete | Optionally verify the report structure and verdict consistency |
| Verification returns PASS | Attach receipt metadata to the report |
| Verification returns FAIL | Re-check findings and correct the report |
| Verification returns INDETERMINATE | Hold for manual review; do not treat as verified |
## What This Skill Does
Skill Vetter v2 evaluates a target skill across four dimensions:
1. **Purpose and scope**
What the skill claims to do, and whether its requested capabilities match that purpose.
2. **Install-time behavior**
File writes, package installs, hooks, system changes, or bootstrap modifications.
3. **Runtime behavior**
Commands, file access, network access, external APIs, tool usage, and data handling.
4. **Trust dependency**
Whether the skill depends on transparent and reviewable systems, or on opaque and unverifiable services.
## Core Execution Loop
1. Inspect the skill package locally
* `SKILL.md`
* `README.md` and references
* scripts, hooks, assets, templates
* metadata and install surface
2. Identify declared and implied capabilities
* file reads and writes
* command execution
* package installation
* network or API usage
* handling of credentials, memory, or sensitive files
3. Evaluate risk
* install-time risk
* runtime risk
* data exposure risk
* trust dependency risk
4. Generate a structured report
5. Optional: verify the completed report
* define a deterministic verification spec for the report structure and verdict logic
* run verification only on the minimal structured report payload
* attach receipt metadata only when verification passes
## Risk Evaluation Categories
### Install-Time Risk
Review for:
* file writes outside the workspace
* package installs or dependency changes
* shell profile or system configuration changes
* hook registration or startup injection
* hidden setup steps not stated in the docs
### Runtime Risk
Review for:
* external API calls
* arbitrary command execution
* broad file system access
* credential discovery or token handling
* browser/session access
* transmission of user data or memory files
### Trust Dependency
Classify the skill's external dependencies:
* **none** — local-only behavior, no external trust dependency
* **transparent** — external dependency is narrow, explicit, and independently understandable
* **opaque** — external dependency is broad, hidden, unverifiable, or requests sensitive data
## Red Flags
Reject or escalate immediately if you find:
* requests for credentials, API keys, seed phrases, or private keys
* access to secret stores or credential directories without a clear need
* obfuscated or encoded execution logic intended to hide behavior
* silent network transmission of prompts, memory, or workspace files
* writes outside expected directories without a clear reason
* privilege escalation or system-level modification
* unexplained background processes, persistence, or surveillance behavior
## Output Format
```json id="m8yx3p"
{
"skill_name": "...",
"purpose": "...",
"source": "clawhub | github | local | other",
"capabilities": ["..."],
"install_risk": "low | medium | high | extreme",
"runtime_risk": "low | medium | high | extreme",
"trust_dependency": "none | transparent | opaque",
"warnings": ["..."],
"recommendations": ["..."],
"verdict": "safe | caution | unsafe",
"verified": false,
"verification": {
"status": "not_run | pass | fail | indeterminate",
"receipt_id": null,
"notes": ""
}
}
```
---
## Example Usage
### Input (Skill to Review)
```json id="9j3kdx"
{
"skill_name": "example-email-sender",
"source": "github",
"description": "Sends automated emails using an external API",
"files": ["SKILL.md", "scripts/send-email.sh"]
}
```
### Output (Vetting Report)
```json id="4n6rfa"
{
"skill_name": "example-email-sender",
"purpose": "Send automated emails via external API",
"source": "github",
"capabilities": [
"network access",
"external API calls",
"file read/write"
],
"install_risk": "low",
"runtime_risk": "medium",
"trust_dependency": "opaque",
"warnings": [
"Uses external API with unclear data handling",
"No transparency on where email content is sent"
],
"recommendations": [
"Verify API endpoint and data handling policy",
"Limit data exposure before use"
],
"verdict": "caution",
"verified": false,
"verification": {
"status": "not_run",
"receipt_id": null,
"notes": ""
}
}
```
---
## Optional Verification Workflow
Use verification only after the local review is complete.
Recommended pattern:
1. Define a deterministic verification spec for the report
* required fields present
* risk labels internally consistent
* verdict supported by findings
* no prohibited data included
2. Submit only the structured report and spec
3. Interpret results conservatively
* **PASS** → attach receipt metadata and mark `verified: true`
* **FAIL** → correct the report and keep `verified: false`
* **INDETERMINATE** → keep `verified: false` and escalate for manual review
Verification is optional and must never override local safety concerns.
## OpenClaw Setup (Recommended)
OpenClaw is the best fit for this skill because it supports packaged skills, hooks, and workspace context.
### Installation
**Via ClawHub:**
```bash id="t2j9mf"
clawdhub install skill-vetter-v2
```
**Manual:**
This legacy package does not currently publish a source install URL. Use the ClawHub listing/package as the reference artifact.
### Optional Hook
Install the reminder hook if you want a prompt to vet skills before trusting them:
```bash id="0xptv9"
cp -r hooks/openclaw ~/.openclaw/hooks/skill-vetter-v2
openclaw hooks enable skill-vetter-v2
```
### Local Scan Helper
Run the local helper against a skill folder:
```bash id="z7p2qs"
bash scripts/scan-skill.sh /path/to/skill
```
This helper inventories files and flags common red-patterns locally. It does not make network calls.
## Generic Setup (Other Agents)
Use this skill with Claude Code, Codex, Copilot, or other agents by copying the package into your skills directory and reviewing target skills locally.
Suggested workflow:
1. Read the target `SKILL.md`
2. Read all scripts, hooks, and references
3. Run the local scan helper
4. Write the structured report
5. Optionally verify the report
## What This Is Not
* not an installer
* not an auto-executor for unknown code
* not an external decision authority
* not a replacement for human judgment on high-risk skills
## Outcome
Agents can:
* understand what a skill actually does before use
* identify install-time and runtime risks clearly
* separate transparent dependencies from opaque trust requirements
* keep safety decisions local while optionally producing verifiable records
## Claim precision note
This version clarifies that the skill is a verifiability-guided workflow aid. It does not itself perform cryptographic verification, emit Settlement Attestation Receipts, verify receipts, verify signatures, prove that a skill is safe, or approve execution. For receipt verification, use SettlementWitness or DefaultVerifier MCP verify_receipt.
## Keywords
ai-agents, skill-safety, risk-analysis, verification, trust, security
將開發過程中的經驗、錯誤與修正記錄為 Markdown 檔案,供日後重用並提升為工作區記憶。
用自然語言描述需求,在本機與遠端技能市場尋找技能並取得推薦。
給 AI agent 用的瀏覽器自動化命令列技能。
將使用者明確糾正與自我檢視整理成分層記憶,並依使用情況升級或封存。
在安裝第三方技能之前,按清單審查來源、許可權與危險模式的技能。
將代理記憶與跨技能狀態整理成具型別與約束的知識圖譜。
規劃 AI 代理主動跟進、工作記憶與回顧流程的工作區設定指南。
指導如何建立與更新有效 Claude 技能的寫作指南。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。