nutstrut

Skill Vetter V2 0.0.6

AIエージェントv0.0.6
作者:nutstrut更新日 2026-07-04全 6 バージョン · 最新版の公開日 2026-07-04skill-vetter-v2@clawhub_nutstrut/skill-vetter-v2
エージェントフレームワークワークフロー編成

AlphaMoatによると、2026-09時点でSkill Vetter V2 0.0.6のダウンロード数は4,374、スター数は0で、AIスキル全体5,078件中2,234位、AIエージェントでは969件中426位です。

インストール数
129
ダウンロード数
4374
スター数
-
総合順位
#2,234
全体の上位 44% · AIエージェントの上位 44%
プラットフォームインストール数ダウンロード数スター数コメント
ClawHub ↗1294302--
SkillHub ↗1264374-—

プラットフォームごとに集計方法と集計時点が異なります。

このスキルでできること

機能、権限境界、隠れた前提、データ送信、証拠を超える主張を調べ、レビュー用のローカルレポートを作成します。暗号学的検証や証明書の発行は行わず、スキルが安全だと証明したり、人による信頼判断を代替したりするものではありません。スキャン補助ツールは対象内の認証情報らしい行を出力する可能性があるため、確認対象を指定フォルダーに限定し、生の出力を保護してください。

資料公開日 2026-10-29

できること

  • 第三者スキルの導入前に機能とリスクを確認する
  • スキルの主張が証拠の範囲を超えていないか調べる
  • 人による確認用のローカルリスクレポートを作成する

使う場面

  • スキルパッケージの導入または公開前
  • リスク評価と安全認証を区別する必要があるとき
  • 対象フォルダーを限定し、スキャン出力を保護できるとき

プロンプト例

スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。

このスキルパッケージの機能、データアクセス、実行リスクをローカルで確認し、根拠と不確実な点を示してください。安全認証として説明したり、生のスキャン結果を外部サービスへ送ったりしないでください。

月次の推移

ダウンロード数4374
2192336445362026-03 · ダウンロード数 23542026-05 · ダウンロード数 31972026-06 · ダウンロード数 33602026-07 · ダウンロード数 38432026-08 · ダウンロード数 41222026-09 · ダウンロード数 437426-0326-09
スター数-
0112026-03 · スター数 -2026-05 · スター数 -2026-06 · スター数 -2026-07 · スター数 -2026-08 · スター数 -2026-09 · スター数 -26-0326-09

2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。

月ダウンロード数前月比スター数
2026-094374—-
2026-084122+7.3%-
2026-073843+14.4%-
2026-063360+5.1%-
2026-053197—-
2026-032354—-

SKILL.md 原文

作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。

10 KB・ClawHub v0.0.6
---

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.
すべて表示(全 315 行)

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

AIエージェントの人気スキル

データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。