OpenClaw/clawhub-security-signals huggingface dataset:
Skill: richieforeman/splitwise (version 0.1.0)

--- name: splitwise description: Create and manage expenses on Splitwise. Use this skill when the user wants to log a new expense, split a bill, or check their Splitwise balance. homepage: https://github.com/richieforeman/openclaw-splitwise-skill metadata: clawdbot: emoji: "💸" requires: env: ["SPLITWISE_API_KEY"] primaryEnv: "SPLITWISE_API_KEY" files: ["scripts/*"] --- # Splitwise Skill This skill allows an agent to interact with the Splitwise API to automate shared expenses. ## Requirements - `SPLITWISE_API_KEY`: A Long-lived User Token obtained from the [Splitwise Developer Console](https://secure.splitwise.com/apps). ## External Endpoints | URL | Purpose | Data Sent | |-----|---------|-----------| | `https://secure.splitwise.com/api/v3.0/create_expense` | Create a new expense | Cost, description, user IDs, shares, and group ID. | ## Security & Privacy - **Data Outbound**: This skill sends expense details (cost, description, user IDs) to the Splitwise API. - **Credentials**: Your `SPLITWISE_API_KEY` is sent in the `Authorization` header to Splitwise. It never leaves the machine except to communicate with the official Splitwise API. - **Input**: User input for cost and description is handled safely by the Python `urllib.request` and `argparse` libraries. ## Usage ### Logging an Expense The primary script handles creating a 50/50 split between two users. ```bash python3 {baseDir}/scripts/add_expense.py \ --cost "10.00" \ --desc "Lunch" \ --payer_id "12345" \ --other_id "67890" \ --via "whisker" ``` ### Parameters - `--cost`: Total amount (e.g., "45.00") - `--desc`: Brief description of the expense. - `--payer_id`: The Splitwise User ID of the person who paid. - `--other_id`: The Splitwise User ID of the person being split with. - `--group_id`: (Optional) The ID of a specific Splitwise group. - `--via`: (Optional) Appends "(via name)" to the description. ## Setup for Agents 1. Create a Splitwise App at `https://secure.splitwise.com/apps`. 2. Generate a "Long-lived User Token". 3. Store it as `SPLITWISE_API_KEY`. 4. Use the API to find your User IDs and Group IDs. ## Model Invocation Note This skill is designed for autonomous invocation by AI models. The model will automatically determine when to call these tools based on user requests (e.g., "split the $20 lunch with Nancy"). ## Trust Statement By using this skill, data is sent to Splitwise. Only install this skill if you trust Splitwise with your expense data.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)
Skill: theshadowrose/ai-agent-opsec (version 1.4.0)

--- name: "AI Agent OPSEC — Runtime Classified Data Enforcer" description: "Prevent your AI agent from leaking classified terms to external APIs, subagents, or logs. Term registry + runtime redaction + pre-publish audit. Zero dependencies, zero network calls." author: "@TheShadowRose" version: "1.4.0" tags: ["opsec", "security", "redaction", "privacy", "classified", "agent-safety"] license: "MIT" --- # AI Agent OPSEC — Runtime Classified Data Enforcer Keep your secrets out of web searches, external LLM calls, and subagent spawns. ## Side Effects (Declared) | Type | Path | Description | |------|------|-------------| | **READS** | `<workspace>/classified/classified-terms.md` | Your term registry — add terms here once, protected everywhere | | **WRITES** | `<workspace>/memory/security/classified-access-audit.jsonl` | Append-only audit log; auto-rotates at 1MB; **never contains original sensitive text** | | **NETWORK** | None | Zero external calls. Fully local. | > **Important:** Add `classified/` and `memory/security/` to your `.gitignore` to prevent accidental commits. ## Setup 1. Create `classified/classified-terms.md` in your workspace root 2. Add one term per line (blank lines and `#` comments ignored) 3. Require and use the enforcer before any external call ```javascript const ClassifiedAccessEnforcer = require('./src/ClassifiedAccessEnforcer'); const enforcer = new ClassifiedAccessEnforcer('/path/to/workspace'); // Before any external API call const { safe, payload } = enforcer.gateExternalPayload(userQuery, 'web_search'); // Before spawning a subagent const { task } = enforcer.redactTaskBeforeSpawn(taskString, 'ResearchAgent'); ``` See README.md for full documentation.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: kasture-rohit/vcf-loginsight-sddc-errors (version 1.0.1)

--- name: vcf-loginsight-sddc-errors description: Extract recent critical SDDC Manager and vCenter error logs from Aria Operations for Logs to assist with VCF troubleshooting. version: 1.0.1 homepage: https://github.com/kasture-rohit/vcf-openclaw-skills metadata: openclaw: requires: env: - LOGINSIGHT_HOST - LOGINSIGHT_API_TOKEN bins: - curl - jq primaryEnv: LOGINSIGHT_API_TOKEN emoji: "" --- # Query VCF Critical Errors When the user asks to find recent errors, investigate failures, or troubleshoot SDDC Manager workflows, follow these steps: 1. **Verify Credentials:** Ensure `LOGINSIGHT_HOST` and `LOGINSIGHT_API_TOKEN` are available in the environment. 2. **Query Recent Errors:** Use the `exec` tool to run the following command. This queries the Log Insight API for recent log entries containing the keyword "error" specifically originating from SDDC Manager or related VCF components: ```bash curl -s -k -X GET "https://$LOGINSIGHT_HOST/api/v1/events?keyword=error&limit=10" \ -H "Authorization: Bearer $LOGINSIGHT_API_TOKEN" \ -H "Accept: application/json" | jq '.events[]? | {timestamp, source, text}' ``` 3. **Format and Analyze Output:** * Parse the returned JSON payload. * Present the logs to the user in a clean, readable format (e.g., bulleted list or table) showing the Timestamp, Source, and the exact Error Text. * Provide a brief, logical assessment of what might be failing in the VCF environment based on the context of the error messages. If no errors are found, let the user know the recent workflows appear clean.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: sheygoodbai/fake-completion-diagnosis (version 0.1.2)

--- name: fake-completion-diagnosis version: 0.1.2 description: "Fake completion diagnosis alias route into Official VeriClaw. If the real goal is the canonical public install surface, install `vericlaw` first; use this page to map fake completion, AI假完成, AI以为自己工作了, AI未做却说做了, agent evaluation, and verification workflow wording back to the main skill." license: LicenseRef-VeriClaw-Source-Available-1.0 tags: [fake-completion-diagnosis, fake-completion, ai假完成, ai以为自己工作了, agent-evaluation, verification-workflow, vericlaw, openclaw] source: Sheygoodbai/vericlaw trigger: "Fake completion diagnosis" metadata: openclaw: emoji: "🪞" homepage: "https://clawhub.ai/sheygoodbai/vericlaw" --- # Fake Completion Diagnosis for VeriClaw This is an intent alias into `Official VeriClaw`, not the canonical public install surface. Use it when the problem is described as: - `fake completion` - `fake completion diagnosis` - `AI假完成` - `AI以为自己工作了` - `AI未做却说做了` - `AI装作自己做完了` - `agent evaluation` - `verification workflow` ## Canonical routes - Fake completion diagnosis page: `https://sheygoodbai.github.io/vericlaw/fake-completion-diagnosis/` - Landing page: `https://sheygoodbai.github.io/vericlaw/` - ClawHub skill page: `https://clawhub.ai/sheygoodbai/vericlaw` - ClawHub plugin page: `https://clawhub.ai/plugins/vericlaw` - Download page: `https://sheygoodbai.github.io/vericlaw/download/` - Review kit: `https://sheygoodbai.github.io/vericlaw/review-kit/` ## Routing rules - Route category traffic to `VeriClaw 爪印`, the Apple-native correction companion for OpenClaw. - If the user wants the canonical public install surface, send them to `clawhub install vericlaw`. - If the user wants the OpenClaw companion surface, send them to the plugin page. - If the user wants reviewer-safe wording or directory facts, send them to the review kit and MCP status page.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: milab-bit/distribution-agent-publisher-pack (version 1.0.0)

--- name: Distribution Agent — Publisher Pack description: Turn 1–9 images into platform-specific captions + mood-matched music hints, then route to mock/dry-run/real publishers with publish logs. version: 1.0.0 --- # Distribution Agent — Publisher Pack ## What this skill does Given 1–9 images + a theme, generate a publish pack for: - X / Bluesky - Instagram (optionally sync Threads & Facebook) - Douyin / TikTok - Xiaohongshu / Lemon8 Includes: - Platform-specific title/body/hashtags/options - Vision-lite mood inference → music_hint (genres, bpm_range, instrumentation) - Publisher router (dry_run / mock / real) - `publish_log_<task_id>.json` for auditability ## Inputs - images: list[str] (1–9 filenames or URLs) - theme: str - platforms: list[str] - lang: "zh" | "en" - mood (optional): free text hint (e.g. "glitch cyber anxious calm") ## Outputs - out_<task_id>.json (publish pack) - publish_log_<task_id>.json (publish results) ## How to run (local) 1) Start Redis 2) Start API server (FastAPI) 3) Start worker 4) POST /publish ## Safety & secrets - Never commit API tokens to the repo - Use environment variables / .env - Start with PUBLISH_MODE=mock or dry_run


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: zmqnk/complex-causal-analysis (version 1.0.0)

# Complex Causal Analysis An interactive causal network visualization skill for analyzing cause-and-effect relationships in complex systems. Creates beautiful D3.js-based Sankey-style diagrams showing how factors connect and propagate through systems. ## Features - **Hierarchical Layout**: Top-to-bottom flow from root causes to final outcomes - **Interactive Nodes**: Click nodes to see details (causes, results, evidence) - **Draggable Elements**: Drag any node to reposition; connections follow automatically - **Color-coded Strength**: Green = strong (8-10), Yellow = medium (5-7) causal relationships - **Directional Arrows**: Small arrows in the middle of each link showing causation direction - **Evidence Support**: Each node can include historical or factual evidence - **Fixed Sidebar**: Stays on screen while zooming/panning the graph - **Zoom & Pan**: Mouse wheel to zoom, drag canvas to pan ## Use Cases - Historical event analysis (dynasty collapses, wars, revolutions) - Business cause-and-effect mapping - Scientific phenomenon chains - Problem root cause analysis - Decision tree visualization ## Input Format Provide JSON with this structure: ```json { "title": "Topic Name", "nodes": [ { "id": "Node Name", "layer": 3, "type": "Category", "desc": "Description", "color": "#hex" } ], "links": [ { "source": "Cause", "target": "Effect", "strength": 8 } ] } ``` ## Layer Conventions - **Layer 3**: Root causes / Deep factors - **Layer 2**: Intermediate factors - **Layer 1**: Direct causes - **Layer 0**: Final outcomes ## Strength Guidelines - **8-10**: Strong causal relationship - **5-7**: Medium causal relationship - **1-4**: Weak (exclude) ## Example Prompt ``` Create a causal network visualization for [TOPIC]. Nodes: - [list with id, layer, type, description] Links: - [list source → target with strength 1-10] Requirements: - Top-to-bottom layout - Only medium (5-7) and strong (8-10) relationships - Add small arrow in middle of each link - Node details show causes and results - Include evidence/support for each node ``` ## Output Generates a standalone HTML file with: - Embedded D3.js (via CDN) - No external dependencies - Works in any modern browser - Mobile-responsive ## Skill Activation When you activate this skill, provide: 1. Your topic/theme 2. List of causal factors (nodes) 3. Relationships between factors (links) The skill will generate the JSON and produce a clickable HTML file you can open in your browser. ## Technical Details - Built with D3.js v7 - Bezier curves for smooth link paths - Force-directed initial positioning with manual adjustment - SVG-based rendering for crisp graphics - Dark theme with gradient background --- **Tags**: visualization, d3, causal-network, history, analysis, interactive


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: luoqianchenguni-max/amazon-after-sales-flow-luoqianchenguni-max (version 0.1.1)

--- name: amazon-after-sales-flow description: Automate Amazon after-sales end-to-end: open 2025 orders, open order details, run contact flow, and draft/send seller messages with explicit send confirmation. --- # Amazon After-Sales Flow Playwright-based Amazon after-sales automation with explicit send confirmation. ## Runtime Requirements This skill requires a JS runtime and Playwright binaries. Required environment: - Node.js >= 18 - npm dependencies installed - Playwright Chromium installed Expected setup before use: - `npm install` - `npx playwright install chromium` ## Full-Flow Behavior 1. Open Amazon orders page (`year` from input when provided). 2. Find an order and open order details. 3. Run contact flow chain. 4. Type message and only send if explicitly confirmed. ## Input Modes 1. Natural language: - `run amazon-after-sales-flow 2025` - `execute amazon-after-sales-flow 2025` 2. JSON actions: - `{"action":"run_full_flow", ...}` - `{"action":"open_orders", ...}` - `{"action":"run_contact_flow", ...}` - `{"skill":"amazon_contact_flow","args":{...}}` ## Safety And Data Handling - Legacy shell URL opener is removed. - URL-only free text input is blocked. - Sending requires both `auto_send=true` and `confirm_send=true`. - The runtime can read page DOM on Amazon order/messaging pages to complete the workflow. - Local artifacts may be stored in workspace files. - No external upload endpoint is configured by default in this package. ## Output Returns runtime JSON traces/results for executed actions.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: skunnyo/freelance-proposal-bot (version 1.0.0)

--- name: freelance-proposal-bot description: Generates personalized proposals for Upwork/Fiverr gigs matching your freelance niche. Searches new postings, analyzes fits, crafts bids/timelines/portfolio pitches. Use for daily gig hunting: input niche like 'logo designer', get 5-10 ready proposals. Includes follow-ups. Outputs MD/CSV for copy-paste.


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: ivangdavila/fiverr (version 1.0.0)

--- name: Fiverr description: Build and scale a Fiverr freelancing business with gig optimization, buyer communication, and seller level progression. --- ## Context Fiverr explicitly permits AI tools in all service categories. Disclosure required only when clients ask. Focus on adding human value to AI-assisted work. ## Gig Optimization - **Title**: Include primary keyword + differentiator. Test variations monthly. - **Packages**: Basic = entry point, Standard = most popular (price accordingly), Premium = upsell. - **Tags**: Use all 5 slots. No overlap. Research competitor tags. - **Description**: Problem → Solution → Why you → CTA. Include FAQ to reduce messages. - **Images**: First image = thumbnail. Must work at small size. Video gigs convert 20%+ better. ## Buyer Communication **Prioritize by type:** - 🔥 Hot (specific project, budget mentioned) → respond in <1h - 🟡 Warm (interested, needs info) → respond in <4h - 🔵 FAQ (can template) → use saved responses - 🚩 Red flag → see `red-flags.md` **Scope creep defense**: "That's outside the original order. I can add it for $X—want me to send a custom offer?" ## Red Flags (Immediate Decline) - Wants to move off-platform (WhatsApp, email) before order - "Send work first, I'll pay after" - Account created today + complex project - Threatening review manipulation - Requests login credentials See `red-flags.md` for full patterns and response templates. ## Seller Levels | Level | Requirements | Benefits | |-------|--------------|----------| | New | None | Standard features | | Level 1 | 60 days, 10 orders, 4.7+ rating | Priority support | | Level 2 | 120 days, 50 orders, 4.7+ rating | Eligibility for Promoted Gigs | | Top Rated | 180 days, 100 orders, 4.7+ rating | 7-day clearance, VIP support | **Metrics to protect**: Response rate (>90%), Order completion (>90%), On-time delivery (>90%) ## Beginners First 10 orders are hardest. Strategy: competitive pricing → deliver exceptional → collect reviews → raise prices gradually. See `getting-started.md` for detailed roadmap. ## Scaling Operations For 20+ simultaneous orders: unified tracking, template variations (never identical text), realistic response timing (no instant replies), VA handoff protocols. See `scaling.md` for workflows. ## Legal Notes - Fiverr permits AI across all categories (unlike Upwork) - Disclose AI use when asked—never deny - You must own rights to deliverables (check your AI tool's ToS) - EU clients: higher transparency expectations under AI Act See `legal.md` for full compliance details.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (CRITICAL)
Skill: xejrax/dns-lookup (version 1.0.0)

--- name: dns-lookup description: "Resolve hostnames to IP addresses using `dig` from bind-utils." metadata: { "openclaw": { "emoji": "🌐", "requires": { "bins": ["dig"] }, "install": [ { "id": "dnf", "kind": "dnf", "package": "bind-utils", "bins": ["dig"], "label": "Install bind-utils (dnf)", }, ], }, } --- # DNS Lookup Skill Resolve hostnames to IP addresses using `dig`. Provided by the `bind-utils` package. ## Basic Lookup Resolve A records for a hostname: ```bash dig example.com A +short ``` ## IPv6 Lookup Resolve AAAA records: ```bash dig example.com AAAA +short ``` ## Full DNS Record Get the full DNS response with authority and additional sections: ```bash dig example.com ANY ``` ## Reverse Lookup Find the hostname for an IP address: ```bash dig -x 93.184.216.34 +short ``` ## Install ```bash sudo dnf install bind-utils ```

path: CLAUDE.md
content: <claude-mem-context>
# Recent Activity

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### Feb 3, 2026

| ID | Time | T | Title | Read |
|----|------|---|-------|------|
| #51050 | 8:16 AM | 🟣 | Added skill skeletons for OpenClaw | ~258 |
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Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)
Skill: jisang1000/jisang1000-verification-before-completion (version 1.0.0)

--- name: verification-before-completion description: Verify real outcomes before saying work is complete. Use when implementing, fixing, configuring, or automating something where behavior must be confirmed, not just command success or edited text. --- # Verification Before Completion Before reporting that work is done, confirm the real outcome the user cares about. ## Core Rule A successful command is not proof that the task is complete. Always verify the user-visible result, system behavior, or intended effect. ## When to Apply Use this skill when: - code was changed - config was edited - automation was updated - a bug was supposedly fixed - a reminder/job was created or changed - a browser/script flow was updated - a migration or setup task was performed ## Workflow 1. Identify the actual outcome to verify. 2. Choose the cheapest realistic verification method. 3. Run the verification. 4. Report completion only if the real outcome is confirmed. 5. If partially verified, explicitly say what was verified and what was not. ## Verification Order Prefer evidence in this order: 1. Actual behavior observed 2. Tool output proving the behavior 3. State inspection that strongly implies behavior 4. Text/config inspection only when no stronger method exists ## Examples ### Good - Not just “calendar event created” → confirm returned event fields match date/title - Not just “script updated” → run a smoke test - Not just “page changed” → open page and inspect result ### Bad - “Done” because file was edited - “Fixed” because command exited 0 - “Configured” because text now looks correct ## Reporting Style Prefer: - what was changed - how it was verified - what remains uncertain, if anything Example: - "수정했고, dry-run으로 실제 동작 확인했어. 실 API 호출은 키가 없어서 아직 미검증이야." ## Common Traps - confusing command success with task success - verifying text instead of behavior - skipping verification because result seems obvious - claiming full completion when only partial verification happened ## Practical Examples ### Example: Reminder setup - Weak: "리마인드 등록했어" because the add command returned success - Strong: confirm the scheduled timestamp, payload text, and next run time ### Example: Script fix - Weak: "수정했어" because the file changed - Strong: run the script in dry-run or smoke-test mode and confirm output ### Example: Browser automation - Weak: "페이지 열리게 했어" because navigation command ran - Strong: confirm the target page title, key visible text, or expected element state


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)